Innovations and Technological Advances in Healthcare Remote Monitoring Systems for the Elderly and Vulnerable People: A Scoping Review

Abstract

The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such as older adults. This review explores methods for the analysis of physiological signals using remote and intelligent systems and mobile and web-based applications, mostly linked to wearable devices, focusing primarily on the elderly population. The main objective is to identify crucial advancements in the development or integration of technology applied to addressing challenges of this magnitude. The research is structured following the PRISMA-ScR guidelines. The search strategy was implemented in databases such as the ACM Digital Library, IEEE Xplore, PubMed, Science Direct, Scopus, and Springer Link. A total of 411 articles were collected, and inclusion and exclusion criteria were applied to focus on studies published between 2020 and 2024. Ultimately, 100 articles from 35 countries were selected for data extraction. The findings reveal significant progress in remote monitoring technologies but emphasize the need for rigorous validation to ensure accuracy and reliability across diverse populations. To develop robust systems that provide equitable and high-quality healthcare, it is essential to address critical challenges such as data privacy, security, accessibility, and ethical considerations.

1. Introduction

The fast-paced evolution of digital health technologies has led to significant advancements in remote healthcare monitoring, particularly for elderly and vulnerable populations. As the global demographics shift toward an aging society, there is a growing need for innovative solutions that enhance healthcare accessibility, efficiency, and quality while minimizing the burden on healthcare systems. Remote monitoring systems, integrating emerging technologies such as wearable sensors, artificial intelligence (AI), and the Internet of Things (IoT), offer continuous health assessment, early anomaly detection, and timely medical interventions.

These systems enable the real-time collection and analysis of physiological and behavioral data, facilitating proactive healthcare management and improving patient outcomes. Research has demonstrated that remote monitoring can reduce hospitalizations, enhance chronic disease management, and support independent living among elderly individuals. However, despite these promising benefits, challenges remain in terms of technological standardization, data security, ethical considerations, and user adherence, particularly among older adults with limited digital literacy.

According to the World Health Organization (WHO) [1], an older adult is defined as an individual who has surpassed the average life expectancy at birth. This is typically applied to individuals aged 60 and older, based on age-specific mortality rates at a given time. The United Nations [2] similarly defines old age as 60 years or older, acknowledging the diverse needs, capabilities, and experiences of older adults, which are influenced by factors such as gender, health, income, education, and ethnicity. Furthermore, the WHO [3] (2024) reports that most of the global population now has a life expectancy of at least 60 years, and all countries are witnessing an increase in both the number and proportion of older adults.

By 2050, the WHO projects that the global population aged 60 and older will reach 2.1 billion, constituting approximately 22% of the world’s population [3]. The United Nations, in its “World Social Report 2023: Leaving No One Behind in an Ageing World”, highlights that the number of people aged 80 and older is increasing at a faster rate than in those aged 65 and above. Additionally, population aging is occurring more rapidly in developing countries compared to historical trends in more developed nations [4].

Under the impact of the COVID-19 pandemic, the most vulnerable group in the population was the elderly, and biopsychosocial conditions were a determining factor in increasing their vulnerability. During the early stages of the second phase of the pandemic, the population, especially those aged 60 and above, was subjected to home confinement measures [5].

As the global population ages, there are higher levels of chronic diseases, physical disabilities, mental illnesses, and other comorbidities. This demographic shift presents challenges in adapting technological approaches to accommodate bodily limitations. Therefore, it is essential to develop human–computer interaction (HCI) systems that address the evolving needs of older adults and integrate seamlessly into their daily lives [6].

According to Statista, “the market for smartwatches has expanded significantly in recent years, with numerous manufacturers offering a variety of models tailored to different user needs and preferences. The global user base in the ’Smartwatches’ segment of the digital health market is projected to experience a steady increase from 2023 to 2028. This growth is expected to encompass an additional 11.4 million users, reflecting an overall increase of 5.2 percent” [7]. Consequently, it is crucial to develop monitoring systems with a user-centered approach, focusing on simplifying graphical user interfaces for ease of navigation [8]. Additionally, factors such as the sensor size, quality, power consumption, dependability, and stability are critical to the user experience, wearability, and functionality of wearable devices [9].

Furthermore, as highlighted by recent authors [10], there is a need to create health monitoring systems equipped with multiple sensors and data analytics capabilities to provide a comprehensive approach to supporting and caring for older individuals. These systems should consider their activities of daily living (ADLs) and overall health status, considering the wearable devices previously mentioned. The proliferation of connected devices in the healthcare sector can be attributed to recent advancements in communication technology and an increased reliance on the Internet of Things (IoT). The demand for secure, on-time, and extensive data transmission is crucial for modern healthcare applications, including telemedicine, remote patient monitoring, wearable health devices, and emergency response systems [11].

The demand for health monitoring solutions is rising worldwide due to the increasing elderly population and the growing need for continuous healthcare support. Mexico serves as an illustrative example of this global trend. According to the Instituto Nacional de Estadística y Geografía (INEGI), the population aged 60 and older in Mexico was projected to reach approximately 17,958,707 by the end of 2022, constituting 14% of the total population [12]. The Instituto Nacional de las Personas Adultas Mayores (INAPAM) estimates that, by the end of 2024, this proportion will remain similar, with an annual growth rate of 4% [13].

Elderly and vulnerable populations worldwide encounter various barriers to effective health monitoring, including

• Limited access to healthcare services, often requiring frequent in-person visits for medical evaluations;
• Reliance on manual vital sign monitoring devices, which can be difficult to use without assistance;
• Challenges in adopting wearable health technologies, due to technological barriers, limited digital literacy, and the absence of user-friendly devices tailored to their needs.

Previous research [14] demonstrates that older adults experience improvements in health-related quality of life when engaging in physical activity and utilizing preventive healthcare services. However, despite these benefits, elderly and vulnerable populations continue to face substantial challenges in accessing continuous and effective health monitoring.

Given these challenges, this study systematically explores recent innovations in remote healthcare monitoring technologies for elderly and vulnerable individuals. By assessing the current technological advancements and identifying ongoing barriers, this work offers a comprehensive synthesis of the field, aiming to inform future research and the development of inclusive, robust, and accessible healthcare solutions. The main focus of this manuscript is to spotlight key advancements in technology that could enable the integration of user-friendly health monitoring systems, promoting equitable healthcare access for all members of these populations.

Therefore, the research question is as follows: What are the recent advances in developed and integrated technologies that focus on elderly and vulnerable people’s health, and what are the current methods in physiological signal analysis?

Although other reviews exist, such as [15,16,17,18,19,20,21], they mainly concentrate on specific health areas or illnesses. In contrast, our study emphasizes innovations and technological advancements in remote healthcare monitoring systems for the elderly and vulnerable populations.

The structure of this paper is organized as follows. The first section provides an overview of the context surrounding the aging population and the associated challenges that it presents. The subsequent section outlines the methodological framework employed in this research. The third section details the results of the comprehensive literature review conducted. Following this, the Key Considerations section highlights the contributions of the reviewed studies and identifies potential areas for future growth. Finally, the Conclusions summarize the main findings of the study, providing an informed perspective on how technological advancements are addressing the needs of an aging population and their implications for the future of remote healthcare solutions.

2. Materials and Methods

This scoping review was conducted according to the methodological framework developed initially by Arksey and O’Malley [22], with a methodological enhancement suggested by Levac et al. [23]. The report is based on the PRISMA-ScR statement and checklist [24].

2.1. Identifying the Research Question

This work examines the literature on physiological signal analysis techniques, advances in system integration with data acquisition, communication technologies, and wearable devices for the elderly and vulnerable people within the healthcare systems sector. Given the aging population in Mexico, it is imperative to address this issue. Therefore, the research question is the following:

• What are the recent advances in developed and integrated technologies that focus on elderly and vulnerable people’s health, and what are the current methods in physiological signal analysis?

2.2. Identifying Relevant Studies

The search strategy began by delimiting the period of interest for reported articles published between 2020 and 2024. The initial step involved identifying relevant articles through a focused search on prominent research databases, including the ACM Digital Library, Google Scholar, IEEE Xplore, PubMed, ScienceDirect, Scopus, and Springer Link. The search queries were as follows.

• For older adults: “aging population” OR “old people” OR “elderly population” OR “senior” OR “old population” OR “geriatric”.
• For health systems monitoring: “remote monitoring systems” OR “telemetry” OR “e-health” OR “telehealth” OR “healthcare”.
• For wearable devices: “smart bands” OR “smartwatches” OR “wearable sensors” OR “wearable devices” OR “wristbands”.

All aforementioned items are presented in Table 1 in Section 3 below.

2.3. Study Selection

This study’s inclusion and exclusion criteria mainly focused on older adults (aged 60 and above) and remote health monitoring systems.

Inclusion Criteria

Papers considered for inclusion in the review had to meet the following requirements.

• Publication Type: Journal papers or conference proceedings must be indexed in databases recognized for their scientific rigor.
• Topic Relevance: They must monitor vital signs, fall detection, and emergency alerts.
• Technological Scope: They should encompass a range of topics, including physiological signal analysis, artificial intelligence techniques, intelligent computing algorithms, the Internet of Things (IoT), communication technologies, information processing, storage and deployment techniques, expert knowledge-based systems, embedded systems, sensors, telehealth, and e-health.
• Integration Level: They should cover work ranging from physiological signal analysis to integrated home systems featuring a network of intelligent sensors.

Exclusion Criteria

The following types of works were excluded from the review.

• Publication Type: Technical reports, clinical studies, survey articles, framework proposals, usability studies, qualitative studies, studies that do not focus on the elderly, case studies specific to particular countries, and publications unavailable in English or Spanish.

2.4. Charting the Data

Data were extracted from the selected literature using the PRISMA-ScR checklist [24]. The data abstraction process was duplicated, with two reviewers independently extracting data from all included studies. The final analysis and the resolution of differences were performed via discussion.

2.5. Collating, Summarizing, and Reporting the Results

The studies were scoped according to their aims, results, contributions, and limitations. The selected studies were reported in a structured table synthesis that considered the year of publication, country of origin, technology used, physiological signals sensed, type of device, and key findings. Moreover, a set of illustrations is presented, showing the process of collecting studies from around the world.

3. Results and Discussion

The process of selecting suitable studies for data extraction, based on the guidelines and methodological framework of this scoping review, is illustrated in Figure 1.

These studies were published between 2020 and 2024, as shown in Figure 2.

The majority of the papers collected, based on the origin of the database, came from ScienceDirect, accounting for 39%, as shown in

Table 1. Search strategy and queries. Search Queries: (“aging population” OR “old people” OR “elderly population” OR “senior” OR “old population” OR “geriatric”) AND (“remote monitoring systems” OR “telemetry” OR “e-health” OR “telehealth” OR “healthcare”) AND (“smart bands” OR “smartwatches” OR “wearable sensors” OR “wearable devices” OR “wristbands”).

Database	Science Direct	Springer Link	IEEE Xplore	ACM Digital Library	Scopus	Google Scholar	PubMed
Papers	39	6	38	1	14	1	1
English	39	6	38	1	14	1	1
Total 100

. This table also presents the search queries used for each database consulted. In total, 100 studies were included in this scoping review.

Figure 3 shows a geographical representation illustrating the global distribution of works related to technological advancements and innovations in remote monitoring systems and their applications. This visualization helps to illustrate the regions focused on developing solutions for elderly health. The selected studies originated from 35 countries, with China contributing the largest number, followed by India. Other contributing countries included Bangladesh, Brazil, Canada, Croatia, Ecuador, Egypt, France, Germany, Honduras, Indonesia, Iran, Iraq, Italy, Japan, Kazakhstan, Kuwait, Malaysia, Mexico, Norway, the Philippines, Poland, Portugal, Qatar, Romania, Saudi Arabia, Slovakia, Spain, Taiwan, Thailand, Turkey, Ukraine, the United Kingdom, and the United States.

Figure 4 provides a quantitative overview of the works selected for the analysis in this review. It is important to highlight that 51.4% of the countries dedicating efforts to this field represent a minimal proportion of the total global research reported in this study, based on the inclusion and exclusion criteria defined in Section 2.3. Additionally, it is noted that approximately 6% of the countries included in the analysis were responsible for the most impactful developments in countries with higher population indices.

3.1. What Are the Recent Advances in Developed and Integrated Technologies Focused on Elderly and Vulnerable People’s Health?

According to the range of studies collected during the period of interest, several technologies grew exponentially due to the COVID-19 pandemic. This health crisis, which strongly impacted the world, influenced the need to create more health monitoring systems and enabled the measurement of vital signs in a non-invasive and remote way.

This manuscript examines remote healthcare systems designed not only for the elderly but also for other vulnerable populations. The study presented in [25] introduces a system that integrates Kinect sensors with ECG, EEG, EMG, and PPG sensors to enable the comprehensive monitoring of brain activity, body movement, and physiological signals. This approach aims to enhance cognitive rehabilitation and remote patient monitoring, addressing the growing need for advanced technological solutions in managing neurological disorders. Additionally, the research in [26] proposes a vital sign monitoring and alert system initially designed to assist students in school environments but also adaptable for individuals confined to their homes. Furthermore, the study in [27] introduces a digital solution, D-SORM, for e-health remote monitoring, focusing on arm rehabilitation and human activity recognition through wearable sensors and deep learning techniques. This system has significant applications in telerehabilitation and elderly care, with the potential to improve healthcare outcomes for individuals with mobility impairments.

An exhaustive literature search yielded many studies that provide a strong background for integrated technology proposals. The most outstanding advances are described as follows. SeisMote is a wireless system designed for the monitoring of cardiovascular function through sensorized nodes that enable ECG, acceleration, rotational velocity, and PPG measurement. It operates in three modes: real-time mode, offline mode, and Bluetooth real-time mode [28]. Meanwhile, a smart mattress system can measure patients’ respiration and heart rate by detecting slight body movements, with a sensor that can distinguish between different sleep states and measure the respiration and heart rate in various positions and postures [29]. An affordable and precise fall detection system has been proposed, using a 24 GHz continuous-wave Doppler radar, chosen for its ability to track human movements effectively, penetrate obstacles, and function regardless of the lighting conditions to enable the monitoring of indoor human activities and identify falls [30]. Moreover, DeFall is an environment-independent indoor fall detection system that uses WiFi signals to detect falls based on distinctive speed and acceleration patterns, achieving high accuracy without prior training in new environments [31]. A pioneering telehealthcare system has also been designed to monitor the psychoneurological conditions of elderly and disabled individuals. The system combines Kinect sensors with ECG, EEG, EMG, and PPG sensors for the comprehensive monitoring of brain activity, body movement, and physiological signals [25]. A novel health detection system utilizing millimeter-wave radar technology integrates non-contact and contact mechanisms. Alongside monitoring vital signs like breathing and heartbeats, the system gathers essential data, including human movement, blood oxygen levels, and body temperature, through diverse IoT node devices [32]. Another group has introduced a cyber–physical system-based home robotic system combined with artificial intelligence (AI), intelligent wearable sensors, and remote sensing. It was implemented using a microcontroller board and sensing modules to measure patients’ SPO2, heart rate, and body temperature, with features like fall detection and GPS location detection [33].

3.1.1. Intelligent Homes

Considering the Internet of Things perspective and the growing popularity of smart cities, significant technological advancements have emerged for the monitoring of vital signs and recognition of human activity using sensor networks interconnected through wireless technology using microcontrollers such as ESP8266, ESP32, and ATMega models. Their high level of integration and ease of use have been fundamental in developing smart home health monitoring systems.

One study [34] provides an essential framework for the development of healthcare IoT systems since it is important to have the necessary hardware resources to provide time-critical responses to emergencies that may arise when working in the healthcare area.

Regarding home care-based systems, a prototype device was developed that could acquire and remotely monitor patients’ heart rates and blood oxygen saturation [35]. A dependable patient management system using IoT technology allows healthcare professionals to monitor hospital and at-home patients by continuously tracking vital signs using sensors. Additionally, it incorporates a camera and AI technology for image analysis, enabling the detection and classification of actions such as standing, bending, and falling [36]. Meanwhile, LI-Care, an e-health monitoring system based on LabVIEW and IoT technology, is aimed at cost-effective health condition diagnosis, tailored to hospitalized patients or those confined to bed at home [37]. Han et al. have developed an innovative smart mattress system capable of measuring patients’ respiration and heart rate by detecting subtle body movements. This is achieved using commercially available, low-cost, plastic optical fibers embedded within the mattress. The system’s design facilitates its large-scale production and deployment for overnight sleep monitoring [29]. Another group has developed a cyber–physical system-based home robotic system combined with artificial intelligence (AI), intelligent wearable sensors, and remote sensing. The system was implemented using a Raspberry Pi Pico (Raspberry Pi Ltd., Cambridge, UK) and sensing modules to measure patients’ SPO2, heart rate, and body temperature, with features like fall detection and GPS location detection [33]. A novel non-contact respiration detection method has been proposed, incorporating remote interaction capabilities. The authors have established a three-tier architecture, encompassing IoT, intelligent terminal (IT), and cloud components, which facilitate data acquisition, signal transmission, remote interaction, and diagnosis [38]. A household monitoring system has also been developed for the tracking of multiple physiological signals, aiming to facilitate health management for elderly, weak, and chronically ill individuals in the comfort of their homes [39]. Another group has explored the use of assistive health systems to improve the quality of life of elderly individuals living at home [40]. The authors introduce a comprehensive, unobtrusive system for the in-home health monitoring of elderly individuals, using a range of sensors to collect data non-intrusively while preserving privacy. This ensures continuous health monitoring with full living space coverage [41]. Other authors have proposed a novel home-based monitoring (HM) concept using spatiotemporal visual learning (STVL-HM). The system collects data from wearable sensors and smart devices in the home [42]. Moreover, an innovative RFID-based approach for accurate and continuous respiration monitoring in mobile users has been presented; this methodology effectively addresses challenges such as body movement and phase ambiguity [43]. The SMARTCARE system is an IoT solution that combines home automation systems with medical monitoring devices [44]. Other proposals include a monitoring system using LiFi technology to transmit a patient’s vital information [45]; an autonomous alerting system to address the impracticality of manual operation during emergencies [46]; a system to enhance the utilization of sensors to monitor the real-time movement and health status of the elderly, both within their homes and outside [47]; a health monitoring system using a single millimeter-wave radar on the ceiling to accurately estimate posture and detect the heart rate with high accuracy [48]; a multi-tier remote health monitoring system designed for smart cities [49]; the RO-SmartAgeing system, which utilizes Internet of Things (IoT) devices and cloud computing to monitor the health of elderly individuals in their homes remotely [50]; a sensor system that was used to monitor sleep metrics in 30 older adults [51]; a comprehensive solution for the monitoring of falls and the post-fall health status [52]; and a novel approach utilizing ultrawideband (UWB) radar within an Internet of Medical Things (IoMT) framework for the remote monitoring of vital signs and fall detection among elderly individuals. This system aims to enhance telehealth services tailored to elderly care [53]. Others include a wearable health monitoring device using ZigBee wireless networks to measure the ECG, respiration, pulse wave, and bioimpedance. It provides stable transmission and continuous operation for over a day [54]. Another e-health system is aimed at tracking the health and activities of elderly and disabled individuals by utilizing real-time data from inertial, ECG, and video sensors [55]. A fall detection system with artificial intelligence and IoT health monitoring has also been proposed [56], as well as a smart medicine reminder kit with various features, like phone call reminders, health monitoring, and user-friendly interfaces, which is proposed to improve medication adherence and patient safety [57]. Others include Helena, a health and sleep nursing assistant [58], and a physical health monitoring system (PHMS) tailored to the elderly’s needs [59]. Another study [60] presents a mining approach to monitor geriatric health through sensors and devices; finally, a system for non-invasive heart rate (HR) and blood pressure (BP) monitoring has been developed [61].

Regarding IoT-based systems, Matsuo and colleagues created a system incorporating vital sign sensors into a glove, paired with a mobile application [62]. Other proposals include an initial wearable prototype for the health monitoring of elderly individuals [63] and a technique called the “Internet of Things-Enabled Data Transmission Privacy Recognition Algorithm (IoT-DTPRA)” that addresses privacy and ethical concerns associated with constant surveillance. It uses advanced sensors in wearable devices for real-time activity tracking and has been validated through simulations in realistic and simulated environments [64]. Another significant development is the creation of a system to bridge the gap caused by distance and the shortage of medical professionals in rural areas, enabling the remote monitoring of patients’ health conditions [65]. Others have proposed a concept for the utilization of wearable devices as health monitoring observers with a three-layer architecture: sensors, integration and association, and application and diagnostics [66]. A remote monitoring system for falls using the Internet of Things and six-axis acceleration sensors has been proposed [67], as well as the design of an accurate fall detection network with deep learning methods [68]. Famá et al. presented an IoT-based healthcare system for domiciliary hospitalization that can wirelessly monitor and classify patients’ statuses [69]. Others have integrated artificial intelligence technology into a secure healthcare monitoring system to prioritize patient health parameters collected from sensor nodes, ensuring reliable, accurate, and real-time patient monitoring [70]. Aldousari et al. developed a wearable healthcare monitoring system for elderly individuals, using a wearable device designed as a T-shirt that connects to the ThingSpeak IoT platform [71]. Others have described the creation of a patient monitoring system with vital sign sensors, including a microcontroller, a Message Queuing Telemetry Transport (MQTT) broker for communication, and a mobile application or website as a receptor [72]. The study of Rajesh et al. presented a robust and efficient system for remote health monitoring by utilizing IoT devices and sensors. This system was designed to provide the real-time tracking of medical equipment locations and the continuous monitoring of essential health metrics [73]. Lakshmi et al. proposed prototypes for geriatric health monitoring, using various sensors to send alert messages to doctors and family members when vital signs are abnormal [74]. In addition, one group has developed an advanced system for the continuous real-time monitoring of physiological parameters, including the temperature, heart rate, and blood oxygen saturation (SPO2). The system is designed to transmit the collected data directly to healthcare providers, facilitating the enhanced management and treatment of chronic conditions [75]. Ahila et al. introduced a telemedicine infrastructure for e-healthcare driven by artificial intelligence and the Internet of Medical Things (IoMT) [76]. A biomedical health monitoring and diagnosis system was developed in [77]. A fuzzy-assisted machine learning framework for remote healthcare monitoring using IoT devices and fog computing was described in [78]; finally, the work in [79] proposes a wearable device integrated with various IoT sensors for the elderly’s smart homes to promote sustainable living. The studies discussed in this subsection are summarized in

Table 2. Summary of research for the intelligent home category.

Research Domain	Technology and Sensors Used	Applications	Novelty	References
Smart home health systems	IoT, ESP8266, ESP32, ATMega microcontrollers, sensors for vital signs	Smart home health monitoring, activity recognition	Integration of IoT into home health systems, AI for activity detection, low-cost solutions	[34,35,36,37,41,42]
Home care and elderly assistants	IoT, AI, cameras, sensors (heart rate, SPO2, body temperature), Raspberry Pi Pico, wearable sensors, LiFi, radar, RFID	Patient health monitoring, fall detection, action classification, smart mattress for sleep monitoring, real-time movement monitoring	Non-intrusive, comprehensive health monitoring, low-cost smart mattress for heart rate and respiration, AI integration for fall detection	[29,33,38,39,40,43,44,52,53,75]
IoT health monitoring systems	Wearable sensors (ECG, respiration, pulse wave, bioimpedance), ZigBee, millimeter-wave radar, cloud computing, AI	Remote health monitoring, fall detection, vital sign tracking, geriatric health monitoring	Integration of IoT, AI, and cloud computing for real-time health monitoring	[62,64,65,66,67,68,69,79]
Remote monitoring and smart medicine	Wearable devices, AI, IoT, mobile apps, sensors (heart rate, BP, ECG), deep learning, ThingSpeak platform, cloud computing	Medication adherence, real-time health monitoring, chronic condition management, fall detection, smart reminders	Integration of medication reminders with health monitoring, real-time patient status monitoring, AI, and cloud integration	[57,58,59,71,72,73,76,78]

.

3.1.2. Wearable Devices

Wearable devices have become a crucial component of the Internet of Things (IoT), especially within smart healthcare systems. This category includes technologies like smart clothing, wristwear, and medical wearables that excel in data processing and offer IoT services tailored to users. The current wearable market is driven by applications centered on health, safety, interaction, tracking, identity, and fitness. Wearables effectively integrate users into the IoT ecosystem by seamlessly merging the physical and digital worlds [80].

Regarding wristbands, in one study [63], a set of wristbands was designed with a watch-like style, focusing solely on data collection and presentation, without performing any analysis. The design of wearable devices based on ARM was described in [81]; another wristband was specifically designed for elderly individuals with a system that could alert nearby caregivers in case of emergencies or when health metrics become unstable [82]. The authors of [8] combined humanized design principles to create a wearable smart bracelet tailored to the elderly; moreover, a wearable device with a physiological parameter measurement system was integrated into a health bracelet terminal [83].

Regarding the design of wearables, a multimodal real-time health monitoring device was described in [84]; additionally, in [62], the proposed system consisted of vital sign sensors integrated into a glove and a mobile application known as the Granny Watch, which gathers heart rate measurements, blood oxygenation, the environmental temperature, the atmospheric pressure, and fall detection. Others include Vitality TM, which is designed to be worn on the upper chest [85]; a wearable LTE-connected device for the monitoring of personal health and safety [86]; a novel space-omnidirectional wearable inertial switch for the monitoring of the elderly in daily life and principally for fall detection [87]; and an ionic conductive material called “i-gluten”, described in [88], which is a promising material for the monitoring of elderly physical activity. Its properties, such as electrical conductivity, mechanical strength, flexibility, healing abilities, and strain-sensing capabilities, make it suitable for exercise posture assessment, personalized guidance, and injury prevention. It also offers solutions for sustainable e-waste disposal in wearable electronics.

Regarding clothing, in [89], the system integrated a single piece of clothing consisting of two separate T-shirts: the first T-shirt serves as a mounting surface for the sensors, while the second shirt provides weather protection and an aesthetic cover. In [71], the authors designed a T-shirt connected to the server ThingSpeak; in addition to this, biomedical sensors were deployed on the patient’s clothing or body with a body area network (BAN) to capture vital signs such as the blood pressure, heart rate, oxygen saturation, blood sugar, and other physiological data [90]. Lastly, another group developed smart clothing capable of collecting physiological signals via a biosensor, with precise measurements; it was elastic and designed to be close-fitting [91].

Some systems have been developed taking advantage of commercial devices such as smartwatches and providing infrastructure for the comprehensive monitoring of vital signs. These include the BITalino (PLUX Biosignals, Lisboa, Portugal) wearable device used along with the ATHENA I system in [92]. The study in [93] used the TicWatchPro (Mobvoi, Beijing, China) to create a Web3-type monitoring system, which reduces the burden on healthcare providers and maintains a sustainable health status by establishing mutual oversight among the elderly. Friedrich et al. used a smartwatch to connect to a data platform named COMES. This integration platform provides a holistic view of data parameters, enabling the recognition of correlations without manual data analysis and offering immediate feedback to users [40]. The work in [94] employed the TicWatch, based on the Snapdragon 410c platform, for data collection and analysis in healthcare monitoring. The authors in [95] compared the accuracy of two all-in-one monitoring devices: the BodiMetrics (Shenzhen Viatom Technology Co., Ltd., Shenzhen, China) performance monitor and the Everlast TR10 (Everlast, Hong Kong, China)smartwatch; additionally, the system developed in [49] requires the user to wear a unique smartwatch or smart band to connect to a multi-tier remote health monitoring system designed for smart cities. Correia et al. simultaneously used the Polar H10 (Polar Electro, Kempele, Finland) and James One (MindProber Labs, Porto, Portugal) heart rate sensors to measure the heart rate variability (HRV) during various tasks, and they transmitted the data from both devices to a computer for analysis [96]. The home-based system in [42] collects data from commercial smartwatches to perform a robust analysis for the understanding of activities towards improved care and safety at home; furthermore, the research outlined in [97] compared the accuracy of blood oxygen in consumer smartwatches. Moreover, the Empatica E4 (Empatica, Milan, Italy) was used in the mHealth system to gather stress scale (SC) data during daily activities in [98]. The research in [99] presents a comparison between three prototypes of PPG sensors and two wearables; the study suggests that PPG signals can be successfully recorded and analyzed in real time, even in a weak-magnetic-field environment, with negligible pressure effects on the signal.

Table 3. Summary of studies for the wearable devices category.

Study Focus	Technology and Sensors Used	Applications	Novelty	References
Designed wristbands	Custom-designed wristbands, ARM-based devices, wearable smart bracelets	Health monitoring for elderly individuals, emergency alerts, physiological parameter measurement	Focused on data collection and presentation without analysis, humanized design, integration of multiple physiological parameters	[8,63,81,82,83]
Design of wearables	Multimodal sensors, LTE-connected wearables, smart gloves, accelerometers, omnidirectional inertial switch, ionic conductive material	Real-time health monitoring, fall detection, personal safety, posture assessment	Integration of multiple vital sign sensors and novel materials (i-gluten) for sustainability and enhanced sensing	[62,84,85,86,87,88]
Clothing	Smart textiles, biomedical sensors, body area network (BAN), biosensors	Physiological signal collection, environmental protection, health monitoring	Integration of sensors into esthetic and functional garments, ThingSpeak server connectivity, elastic biosensors for accurate measurements	[71,89,90,91]
Commercial smartwatches	BITalino, TicWatchPro, Polar H10, James One, BodiMetrics, Everlast TR10, Empatica E4	Vital sign monitoring, stress level tracking, remote healthcare monitoring	Infrastructure for real-time health tracking, Web3-based monitoring, comparison of consumer smartwatches, PPG sensor evaluation in weak magnetic fields	[40,42,49,92,93,94,95,96,97,98,99]

summarizes the studies discussed in this subsection. A detailed description of each listed work can be found in

Table A1. Recent advances in healthcare remote monitoring systems, with studies organized by year of publication.

No.	Ref.	Country, Year	Technology Used	Physiological Signals Sensed	Type of Device	Key Findings
1.	[34]	Qatar, 2020	Multicore processors, compressive sensing theory	ECG	Wearable medical sensors	Optimizing power consumption of wearable medical sensors and practical applications in remote health monitoring systems.
2.	[100]	Italy, 2020	Service-oriented architecture, Bluetooth	Various physiological signals	Bluetooth medical devices	Facilitates exchange and utilization of services, information, and documents in home care, cloud-based infrastructure, and web portals.
3.	[28]	Italy, 2020	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems offer continuous and real-time health data for elderly care.
4.	[111]	China, 2020	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care using wearable technology provide scalable and efficient health monitoring solutions.
5.	[77]	India, 2020	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled wearable health monitoring systems enhance elderly care through real-time data.
6.	[96]	Portugal, 2020	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based wearable health monitoring systems enhance elderly care through real-time data collection and analysis.
7.	[65]	Malaysia, 2020	Wearable sensors, IoT, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems offer scalable solutions for elderly care using wearable technology.
8.	[83]	China, 2020	Wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems for health monitoring offer scalable and efficient solutions for elderly care.
9.	[46]	India, 2020	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care offer scalable and efficient solutions.
10.	[95]	USA, 2020	Wearable sensors, IoT, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems offer scalable solutions for elderly care using wearable technology.
11.	[54]	China, 2020	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT provide continuous and real-time health data for elderly care.
12.	[47]	Portugal, 2020	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Advancements in wearable technology and IoT improve elderly health monitoring.
13.	[90]	Iran, 2020	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled wearable health monitoring systems enhance elderly care through real-time data.
14.	[103]	Canada, 2021	CNN, phase-based video motion processing algorithm	Heart rate, breathing rate	Android application	Higher accuracy and efficiency in estimating HR and BR compared to traditional methods, practical applications in healthcare, and disease prevention for elderly individuals.
15.	[102]	Indonesia, 2021	Signal conditioning circuit, ATMega328P microcontroller, Android application	Heart rate	Heart rate monitoring device, Android application	Efficiently monitors and retrieves patient data, providing a customizable alert mechanism through the mobile application.
16.	[58]	USA, 2021	IoT, Wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems enhance elderly care through real-time data collection and analysis.
17.	[29]	China, 2021	IoT, cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Integrating IoT and cloud computing enhances the efficiency and accessibility of health monitoring systems.
18.	[27]	France, 2021	IoT, cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Wearable devices and cloud computing significantly improve healthcare monitoring and patient management.
19.	[114]	China, 2021	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Current technologies in wearable health monitoring systems offer improved care for elderly individuals.
20.	[104]	Romania, 2021	Google Fit application, wearable sensors	Various physiological signals	Mobile application, web dashboard	Enhances communication, continuous patient health monitoring, real-time analysis, and alerts for detected anomalies.
21.	[105]	Italy, 2021	e-health, cloud-based platform	Various physiological signals	Vital sign sensors, gateway (tablet), panic button	Enables remote management of cerebrovascular risk factors, is highly compliant with measurement tasks, and improves health outcomes for TIA/stroke patients.
22.	[106]	Spain, 2021	Sensors, cloud server, mobile application	Heart issues, falls, temperature changes	Smart garment	Provides real-time monitoring and alerts and ensures user-friendliness and effectiveness in remote monitoring of elderly individuals.
23.	[37]	India, 2021	MyRIO 1900, IoT sensors, web publishing tool	Heart rate, body temperature, blood pressure, oxygen level, breathing rate	Wearable sensors, web-based application	Cost-effective health condition diagnosis, remote access for monitoring, and live video feeds of patients.
24.	[43]	China, 2021	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care using wearable technology provide scalable and efficient health monitoring solutions.
25.	[50]	Romania, 2021	Cloud computing, wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care offer scalable and efficient wearable technology solutions.
26.	[30]	Turkey, 2021	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud computing integration in wearable health monitoring systems enhances data accessibility and efficiency.
27.	[70]	Egypt, 2021	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled wearable health monitoring systems enhance elderly care through real-time data.
28.	[36]	India, 2021	IoT, sensors, AI technology	Temperature, heart rate, ECG, blood pressure, SPO2	Sensors, microcontroller, mobile application	Allows continuous monitoring of hospital and home patients and provides healthcare professionals with real-time data access.
29.	[31]	USA, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT and wearable technology provide effective health monitoring solutions for elderly care.
30.	[61]	Thailand, 2022	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems offer scalable and efficient health monitoring solutions for elderly care.
31.	[123]	China, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems offer continuous and real-time health data for elderly care.
32.	[44]	Romania, 2022	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems offer scalable and efficient health monitoring solutions for elderly care.
33.	[101]	Italy, 2022	Bluetooth, Android app, MariaDB	Blood pressure, heart rate	Wearable devices	Enhances healthcare delivery, cost-efficiency, and patient monitoring by leveraging remote monitoring and real-time data access.
34.	[62]	Brazil, 2022	Internet of Things, Firebase platform	Heart rate, blood oxygenation, temperature	Wearable glove	Offers real-time data monitoring and presents a cost-effective alternative to commercial devices.
35.	[35]	Malaysia, 2022	MAX30102, GPS, NodeMCU, Blynk application	Heart rate, oxygen saturation	Wearable prototype	Provides real-time monitoring, patient location information, and a user-friendly mobile application.
36.	[109]	Italy, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Advancements in wearable technology and IoT integration improve health monitoring for elderly individuals.
37.	[89]	Poland, 2022	IoT, wearable sensors	Heart rate, blood pressure, oxygen saturation	Wearable devices, mobile applications	IoT-based healthcare systems offer real-time monitoring and improved patient outcomes.
38.	[38]	China, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems improve elderly care through real-time data collection and analysis.
39.	[40]	Germany, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT for health monitoring elderly individuals offer effective health monitoring solutions.
40.	[74]	India, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Prototype for geriatric health monitoring using an IoT cloud platform for data transmission.
41.	[119]	Taiwan, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems offer continuous and real-time health data for elderly care.
42.	[87]	China, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT offer effective health monitoring solutions for elderly care.
43.	[60]	Malaysia, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT for health monitoring elderly individuals offer effective health monitoring solutions.
44.	[86]	Ukraine, 2022	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems offer scalable and efficient health monitoring solutions for elderly care.
45.	[45]	Saudi Arabia, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems for elderly individuals provide comprehensive and real-time health data.
46.	[69]	Portugal, 2022	Wearable sensors, IoT, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care using wearable technology provide scalable and efficient solutions.
47.	[78]	Saudi Arabia, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide comprehensive health data for elderly care.
48.	[99]	Slovakia, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled health monitoring systems for elderly individuals enhance real-time data collection and care.
49.	[73]	India, 2022	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems for elderly individuals provide comprehensive and real-time health data.
50.	[112]	India, 2022	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, respiration rate	Wearable belt-based sensor	The system integrates various biomedical sensors and offers timely alerts to healthcare providers.
51.	[48]	United Kingdom, 2022	Wearable sensors, IoT, AI	Heart rate, blood pressure, temperature	Wearable devices	AI and IoT integration in wearable health monitoring systems enhances elderly care.
52.	[94]	India, 2022	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Evaluation on wearable devices	An in-depth evaluation of the performance of machine learning algorithms in system-on-chips embedded in a wearable healthcare monitoring device.
53.	[32]	China, 2023	Wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems for health monitoring offer scalable and efficient solutions for elderly care.
54.	[51]	Mexico, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide continuous and real-time health data for elderly care.
55.	[97]	USA, 2023	Wearable sensors, IoT, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care using wearable technology provide scalable and efficient solutions.
56.	[72]	Saudi Arabia, 2023	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud computing integration in wearable health monitoring systems enhances data accessibility and efficiency.
57.	[25]	India, 2023	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems provide scalable and efficient health monitoring solutions for elderly care.
58.	[107]	Taiwan, 2023	IoT devices, smart necklace, mobile application	Various physiological signals	Wearable devices, mobile application	Provides real-time positioning and environmental monitoring and enhances caregiver monitoring capabilities.
59.	[93]	Japan, 2023	Blockchain, Wear OS, Bluetooth	Various physiological signals	Wearable devices	Confirmed effectiveness in the pilot test; further validation is needed.
60.	[110]	Thailand, 2023	IoT, MAX30102, LM35, NodeMCU ESP32 DevKit, myHealth application	Pulse, oxygen saturation, temperature	Wearable device, mobile application	Monitors vital health parameters and provides accurate measurements and valuable functionalities.
61.	[26]	Philippines, 2023	Various technologies	Various physiological signals	Wearable devices	Provides real-time health monitoring and improves elderly care.
62.	[63]	Honduras, 2023	Smart home technology, IoT, AI	Heart rate, movement, fall detection	Home-based sensors, wearable devices	Smart home systems provide efficient and real-time health monitoring, enhancing elderly care.
63.	[76]	India, 2023	Cloud computing, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based health monitoring systems for elderly care using wearable technology provide scalable and efficient health monitoring solutions.
64.	[117]	China, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Technological advancements in wearables and IoT enhance elderly health monitoring.
65.	[121]	India, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide comprehensive health data for elderly care.
66.	[39]	China, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled wearable health monitoring systems enhance elderly care through real-time data.
67.	[92]	Brazil, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Developed a prototype system for healthcare monitoring that uses wearable devices to collect physiological data from patients.
68.	[64]	Iraq, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based health monitoring systems offer continuous and real-time health data for elderly care.
69.	[81]	China, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide continuous and real-time health data for elderly care.
70.	[118]	China, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Sensor and wearable device	Evaluation of a frequency-tracking algorithm for vital sign monitoring using radar data.
71.	[52]	India, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT offer effective health monitoring solutions for elderly care.
72.	[66]	Croatia, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Examines the potential of IoT technology and wearable devices and introduces a conceptual framework for the use of wearable devices as health monitoring observers.
73.	[49]	Brazil, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Introduces the VitalSense model, a multi-tier remote health monitoring system for elderly care in smart cities.
74.	[82]	India 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled health monitoring systems for elderly individuals provide real-time data and enhance care.
75.	[56]	Ecuador, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT provide continuous and real-time health data for elderly care.
76.	[71]	Kuwait, 2023	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud computing integration in wearable health monitoring systems enhances data accessibility and efficiency.
77.	[55]	Kazakhstan, 2023	Wearable sensors, IoT, AI	Heart rate, blood pressure, temperature	Wearable devices	AI and IoT integration in wearable health monitoring systems enhances elderly care.
78.	[115]	India, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based wearable health monitoring systems for elderly care provide real-time data and enhance elderly care.
79.	[116]	China, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide comprehensive health data for elderly care.
80.	[59]	Philippines, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	IoT-based wearable health monitoring systems enhance elderly care through real-time data collection and analysis.
81.	[120]	United Kingdom, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT for health monitoring in elderly individuals offer effective solutions.
82.	[124]	China, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	The system improves the activity recognition efficiency and stability in real time by using multi-sensor wearables and a two-channel neural network.
83.	[75]	Iraq, 2023	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide comprehensive health data for elderly care.
84.	[68]	Iraq, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled health monitoring systems for elderly individuals provide real-time data and enhance care.
85.	[42]	Norway, 2023	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide continuous and real-time health data for elderly care.
86.	[91]	China, 2023	IoT, Wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	Development of an IoT-based health monitoring system for elderly care through real-time data collection and analysis.
87.	[41]	China, 2024	IoT, wearable sensors	Heart rate, blood pressure, temperature	Wearable devices	A fully comprehensive and unobtrusive in-home health monitoring system tailored to elderly individuals living alone.
88.	[33]	India, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide continuous and real-time health data for elderly care.
89.	[53]	China, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	IoT-enabled health monitoring systems for elderly individuals provide real-time data and enhance care.
90.	[67]	China, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Advancements in wearable technology and IoT improve elderly health monitoring.
91.	[79]	India, 2024	Wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud-based systems for health monitoring offer scalable and efficient solutions for elderly care.
92.	[98]	Spain, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	A semi-automatic mobile health system to analyze the relationship between preclinical chronic pain in elderly individuals and various physiological and stress indicators.
93.	[85]	Canada, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable technology and IoT offer effective health monitoring solutions for elderly care.
94.	[122]	Italy, 2024	Wearable sensors, IoT	Heart rate, blood pressure, temperature	Wearable devices	Wearable health monitoring systems provide comprehensive health data for elderly care.
95.	[88]	China, 2024	Wearable sensor	Breath rate, heart rate	Wearable sensor	An innovative ionic conductive wearable material designed for real-time posture assessment and injury prevention in elderly individuals.
96.	[8]	China, 2024	Sensing technology and intelligent algorithms	Heart rate monitoring, sleep monitoring, oxygen monitoring	Wearable smart bracelet	An intelligent bracelet designed under humanized design principles, with advanced sensing technology and user-friendly features.
97.	[108]	USA, 2024	Smartphone camera, machine learning	Blood pressure	Mobile and web application	Face videos captured by a webcam or smartphone camera can help to detect hypertension-related complications early.
98.	[57]	Bangladesh, 2024	ESP32 module, GSM module, mobile application	Heart rate, oxygen saturation, temperature	Medicine reminder unit, mobile application	Improves medication adherence and patient safety through timely reminders and health monitoring features.
99.	[84]	China 2024	IoT, wearable sensors, cloud computing	Heart rate, blood pressure, temperature	Wearable devices	Cloud computing integration in wearable health monitoring systems enhances data accessibility and efficiency.
100.	[113]	Mexico, 2024	Bluetooth, IMUs	Biomechanical signals	Sensors on the body	A computer model for gait assessment in Parkinson’s disease through advanced signal processing for biomechanical indicators to enhance treatment personalization.

, which is organized by year of publication.

3.1.3. Web-Based and Mobile Applications for Healthcare Monitoring

Regarding mobile applications, the HEREiAM platform is an Android-based system that aims for accessibility through television and portable devices, along with additional features tailored to assist elderly individuals in living independently [100]. Others have developed a telemedicine system that integrates Bluetooth technology for continuous vital sign monitoring, including an Android app, a web dashboard, and a server application for data management. The Android app connects to the medical device Checkme Pro via Bluetooth for data retrieval [101]. An Android-based application has been engineered to receive and store data and to transmit SMS alerts to a predefined contact number as part of an alert system [102]. Another Android-based application provides a low-cost and portable solution for the monitoring of vital signs, with higher accuracy and efficiency in estimating the heart rate and breath rate compared to traditional methods [103]. The application consistently monitors the patient’s health, evaluates their condition, and transmits pertinent information to medical experts [104]. Another work presented a telemonitoring system for transient ischemic attack (TIA)/stroke for patients during the COVID-19 pandemic [105]. It presents a promising application solution for the remote monitoring of the elderly, offering flexibility, convenience, and peace of mind for caregivers [106]. Meanwhile, an Android-based application integrated with wearable technology has been developed to enable the real-time tracking of elderly individuals’ locations, incorporating anti-loss and alarm features for enhanced safety [107]. A mobile app was also created to input medication schedules, connecting to a medication reminder device via WLAN [57]. Finally, UbiHeart is an application with a system that utilizes facial videos to extract pulse signals and estimate blood pressure [108].

Regarding web-based apps, relevant proposals include a monitoring system with software that controls data publication and updates on a webpage accessible to authorized users [89], as well as the creation of a web-based application to improve communication between patients and healthcare professionals [104]. The work of Ciocca et al. enabled users to gather health data from various sources via a single web-based application; the front end is used by patients, caregivers, and physicians to monitor collected data [109]. Another system aims to improve healthcare delivery, cost-effectiveness, and patient monitoring through remote monitoring and real-time data access for healthcare professionals with a web dashboard and a server application for data management [101]. A real-time web application, developed with CSS and HTML, displays IoT device data via MQTT messages and alerts medical personnel to critical changes, publishing data to a private cloud server [110]. Finally, a Web3-type monitoring system was proposed that reduces the burden on healthcare providers and maintains a sustainable health status by establishing mutual oversight among the elderly [93].

Table 4. Summary of studies in the web-based and mobile applications category.

Platform Type	Technology Used	Primary Use Cases	Innovative Features	References
Mobile Apps	Android-based systems, Bluetooth, wearable technology, facial video processing	Assists elderly, continuous vital sign monitoring, TIA/stroke telemonitoring, medication reminders	Accuracy in monitoring, real-time tracking, integration with wearable devices, SMS alerts	[57,100,101,102,103,104,105,106,107,108]
Web Apps	Web dashboards, server applications, Web3 technology, CSS/HTML, MQTT messaging	Health data aggregation, remote monitoring, patient–healthcare provider communication	Real-time alerts, seamless integration of data sources, cost-effectiveness, sustainability in healthcare	[89,93,101,104,109,110]

summarizes the studies related to web-based and mobile applications for healthcare monitoring. A detailed description of each listed work can be found in Table A1, which is organized by year of publication.

3.1.4. Integrated Healthcare Systems

A multi-physiological-parameter medical monitoring system based on the ARM-embedded platform [111] has been developed; this system can detect out-of-standard data and issue alarms when necessary, such as when the blood oxygen saturation is critically low.

The investigation outlined in [112] developed a reliable and smart e-healthcare system for the monitoring of the intravenous fluid level, pulse, and respiration rate, particularly focusing on elderly patients in healthcare settings like hospitals. The system enables constant monitoring and provides timely alerts to healthcare providers, enhancing patient care and safety.

The MiVitals system [85] is a mixed-reality tool enabling healthcare professionals to monitor patients using holographic visualizations of real-time vital signs. It was found to be highly usable in a study by medical doctors and healthcare practitioners.

The research team in [113] developed a novel method for gait assessment in patients with Parkinson’s disease by employing inertial measurement units (IMUs) to quantitatively analyze gait patterns and incorporate biomechanical measurements. A fuzzy inference system was integrated into the model and validated through qualitative evaluations from multiple experts. It assists in the monitoring of disease progression and the personalization of treatment plans.

3.1.5. Discussion of Key Studies Regarding the Research Question

In organizing the contributions of the previously mentioned works, we have categorized them based on their specific applications. For example, numerous systems have been created mainly for fall detection, which is crucial in safeguarding vulnerable groups such as the elderly. These systems employ various sensor technologies and algorithms to detect falls in real time and alert caregivers or emergency services when needed. By categorizing these contributions based on their intended applications, we can better comprehend the strengths and functionalities of each system within the larger context of health and safety technology. For instance, [30] monitors indoor human activities and detects falls using radar sensors, while [31] attains high accuracy in fall detection by utilizing WiFi signals. Both systems can operate under various environmental conditions—Doppler radar functions regardless of lighting, and DeFall does not require prior training in new environments. However, there are potential privacy concerns associated with constant monitoring. Other systems concentrate on domains including cardiovascular health and sleep monitoring, as highlighted in [28,29], which discusses an innovative mattress created from low-cost plastic optical fibers. While these mattresses are affordable and scalable, they may not be effective for individuals with minimal body movement during sleep. Consequently, both systems have limitations tied to specific monitoring scenarios. Additionally, for comprehensive health monitoring, we have the pioneering telehealthcare system in [25], the millimeter-wave radar health detection system in [32], and the home robotics system based on cyber–physical systems in [33]. These systems share common strengths, such as real-time data analysis and visualization through cloud-based platforms. However, they may feature complex setups that could be challenging for users. Some studies focus on integrating AI to enhance real-time decision-making and promote the use of mobile applications for improved accessibility. These studies, such as [35,36,37,44,50], highlight how these advancements can reduce healthcare costs and improve access, particularly in remote areas. Nevertheless, these innovations often rely heavily on internet connectivity, which may limit their effectiveness in rural locations. Monitoring systems, such as those described in [26,39,53,75], leverage specialized microcontrollers like NI’s MyRIO, NodeMCU, and ESP32 for signal acquisition and processing. These systems prioritize local data processing, reducing the dependence on cloud storage and making them well-suited for home environments. However, their scalability is limited compared to cloud-based solutions, requiring specialized hardware and software expertise. Moreover, IoT-based wearable health monitoring systems, as highlighted in [71,79], have been developed for real-time health monitoring with wearable devices and GSM modules for emergency alerts and remote communication. However, these systems depend on network availability and battery life and face limitations in terms of power consumption. Additionally, studies such as [43,45,54,69,72] provide insights into communications and data transmission protocols. Some of them utilize BLE, RFID, LiFi, ZigBee, and MQTT, primarily focusing on interoperability and integration with hospital systems and home environments. Nevertheless, some compatibility issues exist with current medical systems (for hospital settings), alongside cybersecurity risks associated with wireless communication.

Table 5. Summary of key studies for each presented category.

Category	Technologies and Sensors Used	Applications	References
Intelligent homes, Section 3.1.1	Microcontrollers (ESP8266, ESP32, ATMega), IoT sensors, AI, LiFi, RFID, millimeter-wave radar, UWB radar, wearable sensors, cloud computing, MQTT, smart cameras	Home care monitoring, patient management, fall detection, health tracking, e-health monitoring, IoT-based monitoring, remote sensing, assistive health systems	[29,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61]
Wearable devices, Section 3.1.2	Smart clothing, wristbands, smartwatches, IoT sensors, ECG, PPG, LTE, inertial sensors, AI, i-gluten conductive material, body area network (BAN)	Health tracking, elderly monitoring, fall detection, emergency alerts, fitness tracking	[8,40,42,49,62,63,71,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99]
Web-based and mobile applications, Section 3.1.3	Android apps, web platforms, Bluetooth, MQTT, AI, facial recognition, cloud-based dashboards, telemedicine	Remote health tracking, medication reminders, telemonitoring, real-time alerts, doctor–patient communication	[57,89,93,100,101,102,103,104,105,106,107,108,109,110]
Integrated healthcare systems, Section 3.1.4	ARM-embedded platforms, AI, IoMT, fuzzy inference systems, BLE, IMUs, cloud computing, holographic visualization	Multi-parameter monitoring, real-time health alerts, gait assessment, telemedicine	[85,111,112,113]

summarizes the studies related to each category presented in response to the question in Section 3.1. A detailed description of each listed work can be found in Table A1, which is organized by year of publication.

3.2. What Are the Current Physiological Signal Analysis Methods?

The analysis of physiological signals is central to the development of effective remote healthcare monitoring systems. Physiological signals such as the heart rate, respiratory rate, blood pressure, and movement patterns offer valuable insights into an individual’s health status and can enable the early detection of potential health issues. Various methods have been developed to capture, analyze, and interpret these signals, employing a range of technologies, including wearable sensors, radar systems, and advanced signal processing techniques. The studies cover a variety of artificial intelligence techniques. The papers that include the most outstanding pattern analysis and classification techniques include the following. S. Zhang et al. introduced RF-RMM, a novel RFID-based approach for continuous and accurate respiration monitoring to mitigate body movement effects and a distance tracking algorithm to address phase ambiguity [43]. A modified independent component analysis method to estimate respiratory and heart rates from multiple radar channels was described in [114]. The CSA-TkELM classifier is a crossover Aquila-optimized tri-kernel extreme learning machine approach for accurate activity recognition. This classifier utilizes a combination of three kernels, namely RBF, linear, and polynomial, and the crossover Aquila (CSA) algorithm [115]. A long short-term memory (LSTM)-based remote health risk assessment system for the elderly has been proposed that includes a wireless physiological parameter sensing unit, a vital sign prediction unit using LSTM neural networks, and a risk scoring criteria unit for the forecasting of vital signs [116]. The LANDMARC algorithm was implemented for blood pressure estimation [117]. The study by Cao et al. presented a frequency-tracking algorithm for vital sign monitoring using radar data. The M-Rife algorithm enhances the accuracy of frequency measurements, and the discrete antenna cross-correlation method extracts the phase to identify respiration and heartbeat signals [118]. Fall detection algorithms have been presented that incorporate You Only Look Once (YOLO)-based AI models and SVM classifiers [119]. The study of Khan et al. proposes a statistical method to augment time series data to create a diverse digital twin (DT) model for multi-patient respiration. It focuses on generating synthetic respiration data and evaluating augmentation techniques for machine learning, seeking to enhance diagnosis, patient monitoring, healthcare training, early detection, and treatment development [120]. A fall detection technique using the K-nearest neighbors (kNN) algorithm was proposed to determine whether an event is a fall by analyzing features from a convolutional neural network independently [121]. Caroppo et al. presented a pipeline on the Raspberry Pi 4 to estimate the heart rate, breath rate, and SPO2 values. It uses algorithmic blocks to enhance the accuracy and address challenges associated with skin tone and facial features, and it was validated on a dataset of subjects aged 65+ [122]. MhNet has been proposed for real-time fall risk assessment in the elderly using wearable in-shoe pressure sensors [123]. The estimation of blood pressure has been achieved by analyzing the correlation between the image-based pulse transit time (iPTT) and blood pressure, with the heart rate variability (HRV) employed as the curve-fitting coefficient to enhance the accuracy [108]. A system utilizing multi-sensor wearable networks and a two-channel convolutional neural network for improved recognition efficiency was developed for human activity recognition and classification. Principal component analysis (PCA) was used for the extraction of characteristics and to enable a reduction in data dimensions [124].

The corresponding description of each listed work can be found in Table A1, which is organized by year of publication.

Discussion of Key Studies in This Subsection

Recent studies on remote health monitoring for the elderly highlight significant advancements in non-invasive, real-time solutions using technologies such as RFID, wearable sensors [124], radar [114], and edge computing. These systems leverage AI and machine learning for accurate activity recognition, vital sign monitoring, and fall detection, with some methods achieving over 90% accuracy [118,119,121]. Real-time predictions using LSTM and radar techniques contribute to improved elderly care and early intervention [114,116,118]. Notably, approaches such as RFID-based respiration monitoring [43], LSTM-based health risk assessment [116], and radar networks for vital sign detection [114] have shown promising results. However, challenges remain, including limited operational ranges, signal interference, and usability concerns for elderly users, particularly those with physical or cognitive limitations [115]. Additionally, several methods require further validation in diverse real-world environments. The challenge of achieving consistent performance across different settings and user conditions persists [123], along with issues related to power and resource constraints that affect the systems’ scalability and practical application [118,119,120].

3.3. Key Considerations

The COVID-19 pandemic has greatly accelerated the development and implementation of health monitoring technologies. However, this swift progress has brought to light several critical issues that must be addressed to ensure effectiveness and reliability, as well as ethical implications. This study explores the main research question regarding the latest advances in elderly healthcare: what are the recent developments in technologies focused on elderly and vulnerable people’s health that have been developed and integrated? Numerous studies across various domains have been examined to answer this question. While systems like SeisMote and smart mattresses demonstrate impressive capabilities in monitoring cardiovascular function and sleep metrics, questions remain about their accuracy and reliability across diverse populations and conditions. For instance, SeisMote’s effectiveness in varying physiological conditions and the smart mattress’s sensitivity to different body types and sleep disorders need further validation. The robustness of fall detection systems using Doppler radar and WiFi signals is also contingent on environmental factors, which may not have been thoroughly tested across different living environments. Some research focuses on the Internet of Things (IoT) and smart cities, proposing the use of networks of sensors to monitor vital signs either within the home environment or attached to the bodies of elderly individuals. Integrating multiple sensors and IoT devices in telehealthcare systems, including Kinect, with ECG, EEG, EMG, and PPG sensors, presents considerable data privacy and security challenges. Collecting and transmitting sensitive health information through various IoT nodes introduces vulnerabilities that could be exploited if not adequately protected. Implementing robust encryption and secure data handling protocols is crucial to prevent unauthorized access and the misuse of personal health information. Additional studies have focused on the design of wearable devices for the measurement of vital signs and detection of events such as falls, as well as on mobile and web applications that issue alerts and display data through intuitive, user-friendly interfaces.

In addition, numerous studies have explored the integration of commercial wearable devices, such as smart watches or wristbands, with custom-developed mobile or web applications that employ artificial intelligence algorithms for data analysis and pattern classification. Other research focuses on developing functional health monitoring systems specifically designed for hospital environments.

The significance of integrated technologies, such as Arduino, is also highlighted. These platforms enable the incorporation of various low-cost hardware components, thereby facilitating the creation of intelligent systems for continuous health monitoring in home settings.

3.4. Limitations

This scoping review targets the elderly population and vulnerable people, focusing on peer-reviewed journal articles and conference papers written in English and indexed in acknowledged databases. The review excludes case studies, clinical reports, and articles that lack sufficient references to support their findings. It centers on technological advances, particularly in remote technology, within a defined time frame. It aims to provide a comprehensive overview of recent developments in remote healthcare systems and the analysis of physiological signals. Instead of addressing specific illnesses, this review seeks to identify key growth opportunities in the field, mapping technological innovations to support the design and implementation of cost-effective solutions intended to enhance the healthcare and well-being of the elderly and vulnerable populations.

4. Conclusions

This scoping review highlights the critical importance of developing health monitoring technologies tailored to the needs of the growing elderly population. The synthesis of the research findings demonstrates the rapid evolution of systems incorporating IoT, wearable devices, and advanced sensor technologies to improve remote health monitoring.

Despite the promising advances, this review emphasizes the need for the rigorous validation of these technologies to ensure their accuracy and reliability across diverse populations and conditions. Critical issues such as data privacy, security, accessibility, and ethical considerations remain pressing challenges that must be addressed to fully realize the potential of these innovations. Tackling these challenges is essential in developing robust and reliable health monitoring systems capable of delivering high-quality care to all individuals, with a focus on elderly and vulnerable populations, regardless of socioeconomic status or geographical location.

Future research should focus on bridging the technological gaps in this review and developing inclusive, practical, and beneficial solutions for the elderly. It should also overcome these challenges by developing standardized protocols, improving the data security measures, and ensuring user-friendly designs.

Finally, this work provides a comprehensive overview of the current landscape of health monitoring technologies worldwide, collected in the period of interest. It provides valuable information on areas that require further investigation and development in Mexico and worldwide. By addressing the identified gaps and challenges, the healthcare industry can move closer to achieving a future where advanced health monitoring technologies are accessible, reliable, scalable, and ethically sound, ultimately improving health outcomes on a global scale.