Search Results (369)

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management. Full article

Surface roughness is a key factor influencing the safety, comfort, and overall quality of bicycle lanes, which are increasingly integrated into urban transportation systems worldwide. This study aims to assess and quantify the roughness of bicycle lanes in Sejong City, Republic of Korea, by utilizing accelerometer-based sensor technologies. Five study sections (A–E) were selected to represent a range of road surface conditions, from newly constructed roads to severely deteriorated surfaces. These sections were chosen based on bicycle traffic volume and prior reports of pavement degradation. The evaluation of road surface roughness was conducted using a smartphone-mounted accelerometer to measure the vertical, lateral, and longitudinal accelerations. The data collected were used to calculate the Bicycle Road Roughness Index (BRI) and Faulting Impact Index (FII), which provide a quantitative measure of road conditions and the impact of surface defects on cyclists. Field surveys, conducted in 2022, identified significant variation in roughness across the study sections, with values of BRI ranging from 0.2 to 0.8. Sections with a BRI greater than 0.5 were considered unsafe for cyclists. The FII showed a clear relationship between bump size and cycling speed, with higher bump sizes and faster cycling speeds leading to significantly increased impact forces on cyclists. These findings highlight the importance of using quantitative metrics to assess bicycle lane conditions and provide actionable data for maintenance planning. The results suggest that the proposed methodology could serve as a reliable tool for the evaluation and management of bicycle lane infrastructure, contributing to the improvement of cycling safety and comfort. Full article

Betalains are hydrophilic natural pigments commonly found in plants of the Caryophyllales order, as well as in specific species and genera of fungi, such as Hygrocybe, Hygrophorus, and Amanita muscaria. Betalains are sorted into two groups: betacyanins, which form red-violet pigments, and betaxanthins, which form yellow-orange pigments. These compounds can be employed as colorimetric sensors and biosensors. This paper provides a review of the isolation methods of betalains and the various applications of betalains as colorimetric sensors and biosensors. The review was conducted by collecting publications over the last decade. The results show that betalains can be used as a colorimetric sensor to identify metal compounds in water and nonmetal compounds that indicate the quality of food. In addition, betaxanthin has been used for developing cell-based biosensors from yeast and bacteria. Furthermore, betalain as a colorimetric sensor and biosensor is developed by using an innovative digital detector, such as a smartphone. Nevertheless, the fragile stability of betalains presents a significant barrier during the extraction. As a result, future studies could focus on adding innovative technologies for optimizing extraction and also developing betalain as novel bio-indicators for specific analytes. Full article

Remote monitoring technologies have transformed healthcare delivery by enabling the in-home management of chronic conditions, improving patient autonomy, and supporting clinical oversight. Passive sensing, a subset of remote monitoring, facilitates unobtrusive, real-time data collection without active user engagement. Leveraging devices such as smartphones, wearables, and smart home sensors, these technologies offer advantages over traditional self-reports and intermittent evaluations by capturing behavioural, physiological, and environmental metrics. This systematic review evaluates the clinical utility of passive sensing technologies used in remote monitoring, with a specific emphasis on their impact on clinical outcomes and feasibility in real-world healthcare settings. A PRISMA-guided search identified 26 studies addressing conditions such as Parkinson’s disease, dementia, cancer, cardiopulmonary disorders, and musculoskeletal issues. Findings demonstrated significant correlations between sensor-derived metrics and clinical assessments, validating their potential as digital biomarkers. These technologies demonstrated feasibility and ecological validity in capturing continuous, real-world health data and offer a unified framework for enhancing patient care through three main applications: monitoring chronic disease progression, detecting acute health deterioration, and supporting therapeutic interventions. For example, these technologies successfully identified gait speed changes in Parkinson’s disease, tracked symptom fluctuations in cancer patients, and provided real-time alerts for acute events such as heart failure decompensation. Challenges included long-term adherence, scalability, data integration, security, and ownership. Future research should prioritise validation across diverse settings, long-term impact assessment, and integration into clinical workflows to maximise their utility. Full article

In the past decades, both low-cost gas sensors for air quality monitoring and smartphone devices have experienced a remarkable spread in the worldwide market. Smartphone devices have become a unique tool in everyday life, whilst the use of low-cost gas sensors in air quality monitors has allowed for a better understanding of the personal exposure to air pollutants. The traditional technologies for measuring air pollutant concentrations, even though they provide accurate data, cannot assure the necessary spatio-temporal resolution for assessing personal exposure to the various air pollutants. In this respect, one of the most promising solutions appears to be the use of smartphones together with the low-cost miniaturized gas sensors, because it allows for the monitoring of the air quality characterizing the different environments frequented in everyday life by leveraging the capability to perform mobile measurements. In this research, a handheld air quality monitor based on low-cost gas sensors capable of connecting to smartphone devices via Bluetooth link has been designed and implemented to explore the different ways of its use for assessing the personal exposure to air pollutants. For this purpose, two experiments were carried out: the first one was indoor monitoring of CO and NO₂ concentrations performed in an apartment occupied by four individuals and the second one was mobile monitoring of CO and NO₂ performed in a car cabin. During the indoor measurements, the maximum value for the CO concentrations was equal to 12.3 ppm, whilst the maximum value for NO₂ concentrations was equal to 64 ppb. As concerns the mobile measurements, the maximum concentration of CO was equal to 8.3 ppm, whilst the maximum concentration of NO₂ was equal to 38 ppb. This preliminary study has shown that this system can be potentially used in all those situations where the use of traditional chemical analyzers for measuring gas concentrations in everyday life environments is hardly feasible, but also has highlighted some limits concerning the performance of such systems. Full article

(1) Background: Digital health technologies (DHTs) are increasingly being utilized to facilitate receiving rehabilitation services remotely, offering innovative solutions to enhance recovery outcomes. This scoping review examines the role of DHT in home-based stroke rehabilitation, focusing on its applications, effectiveness, and limitations. It identifies key advancements and future directions for improving stroke recovery through technological innovations. (2) Methods: Using Arksey and O’Malley’s framework, a systematic search was conducted across multiple databases to identify studies involving DHT for home-based stroke rehabilitation. Eligible studies incorporated technologies for monitoring and evaluation. Data extraction followed PRISMA-ScR guidelines, synthesizing findings across various research designs. (3) Results: Ten studies were reviewed, categorizing technologies into wearable devices, smartphones, and sensor-based solutions. These tools primarily assessed mobility, upper extremity function, cognitive function, daily living activities, and continuous physiological monitoring. High feasibility and usability were reported, though challenges included small sample sizes and user-centered design limitations. (4) Conclusions: Most DHTs used for evaluating and monitoring home-based stroke rehabilitation are wearable and sensor-based, mainly focusing on mobility and upper extremity function. Their application is effective, but limitations remain. Future research should address these gaps to enhance usability and coverage. Full article

This review offers a comprehensive examination of the development and current state of the art in the field of molecularly imprinted polymer (MIP)-based colorimetric sensors, focusing on their potential for the rapid detection of organic compounds. These MIP-sensors are gaining considerable attention due to their distinctive capacity to modify sensor surfaces by creating recognition cavities within the polymer matrix, providing a versatile and highly selective platform for detecting a broad spectrum of analytes. This review systematically examines different types of MIP-based colorimetric sensors, attending to the target analyte, highlighting their applications in on-site sample detection, drug monitoring, environmental analysis, and food safety detection. The integration of novel technologies, such as nanozymes and smartphone-based detection, which enhance the capabilities of colorimetric MIP sensors, is also addressed. The sensors are particularly valuable due to their low cost, rapid response times, portability, and ease of use. Finally, the review outlines the future challenges for the development of MIP-based colorimetric sensors, focusing on overcoming existing limitations, improving sensor performance, and expanding their applications across various fields. Full article

In the current era, which is characterized by increasing demand for high-precision location and navigation capabilities, various industries, including those involved in intelligent vehicle systems, logistics, augmented reality, and more, heavily rely on accurate location information to optimize processes and deliver personalized experiences. In this context, the integration of Global Navigation Satellite System (GNSS) and inertial sensor technologies in smartphones has emerged as a critical solution to meet these demands. This research paper presents an algorithm that combines a GNSS with a modified downdate algorithm (MDDA) for satellite selection and integrates inertial navigation systems (INS) in both loosely and tightly coupled configurations. The primary objective was to harness the inherent strengths of these onboard sensors for navigation in challenging environments. These algorithms were meticulously designed to enhance performance and address the limitations encountered in harsh terrain. To evaluate the effectiveness of these proposed systems, vehicular experiments were conducted under diverse GNSS observation conditions. The experimental results clearly illustrate the considerable improvements achieved by the recommended tightly coupled (TC) algorithm when integrated with MDDA, in contrast to the loosely coupled (LC) algorithm. Specifically, the TC algorithm demonstrated a remarkable reduction of over 90% in 2D position root mean square error (RMSE) and a 75% reduction in 3D position RMSE when compared to solutions utilizing the weighting matrix provided by Google with all visible satellites. These findings underscore the substantial advancements in precision resulting from the integration of GNSS and INS technologies, thereby unlocking the full potential of transformative applications in the realm of intelligent vehicle navigation. Full article

Visually impaired people face significant obstacles when navigating complex environments. However, recent technological advances have greatly improved the functionality of navigation systems tailored to their needs. The objective of this research is to evaluate the effectiveness and functionality these navigation systems through a comparative analysis of recent technologies. For this purpose, the PRISMA 2020 methodology was used to perform a systematic literature review. After identification and screening, 58 articles published between 2019 and 2024 were selected from three academic databases: Dimensions (26 articles), Web of Science (18 articles), and Scopus (14 articles). Bibliometric analysis demonstrated a growing interest of the research community in the topic, with an average of 4.552 citations per published article. Even with the technological advances that have occurred in recent times, there is still a significant gap in the support systems for people with blindness due to the lack of digital accessibility and the scarcity of adapted support systems. This situation limits the autonomy and inclusion of people with blindness, so the need to continue developing technological and social solutions to ensure equal opportunities and full participation in society is evident. This study emphasizes the great advances with the integration of sensors such as high-precision GPS, ultrasonic sensors, Bluetooth, and various assistance apps for object recognition, obstacle detection, and trajectory generation, as well as haptic systems, which provide tactile information through wearables or actuators and improve spatial awareness. Current navigation algorithms were also identified in the review with methods including obstacle detection, path planning, and trajectory prediction, applied to technologies such as ultrasonic sensors, RGB-D cameras, and LiDAR for indoor navigation, as well as stereo cameras and GPS for outdoor navigation. It was also found that AI systems employ deep learning and neural networks to optimize both navigation accuracy and energy efficiency. Finally, analysis revealed that 79% of the 58 reviewed articles included experimental validation, 87% of which were on haptic systems and 40% on smartphones. These results underscore the importance of experimentation in the development of technologies for the mobility of people with visual impairment. Full article

The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently. Full article

Heavy metal contamination of soil and water is a growing environmental concern, especially mercury, lead, and cadmium. Therefore, fast and reliable methodologies to assess contamination in the field are in demand. However, many methodologies require laborious, expensive, and cumbersome equipment that is not convenient for rapid field analysis. Mobile phone technology coupled with bioluminescent assays provides accessible hands-on alternatives that has already been shown to be feasible. Previously, we demonstrated that firefly luciferases can be harnessed as luminescence color-tuning sensors for toxic metals. An assay based on such a principle was already successfully applied for teaching biochemistry laboratory lessons, which demonstrates the effect of cadmium on enzyme function based on bioluminescence color change. For analytical detection of cadmium in water, here, we developed a novel bioluminescence assay using the cadmium-sensitive Amydetes vivianii firefly luciferase coupled with a cell phone provided with a program to quantify cadmium concentration based on luminescence color discrimination. The application has proven to be efficient with high precision between 0.10 and 2 mM of cadmium, being appliable to diluted water samples (0.1–2 µM) upon concentration and relying on reference cadmium standards values. The light emitted by the reference standards and samples in a dark box is captured by the smartphone’s camera, which, using computer vision, automatically quantifies cadmium according to the RGB color. CadmiLume is a simple and easy luminescent enzymatic biosensor for cadmium contamination in water samples, which instantaneously can provide results with the convenience of a smartphone in the palm of one’s hands. Full article

Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field. Full article

The home is an ideal setting for long-term sleep monitoring. This review explores a range of home-based sleep monitoring technologies, including smartphone apps, smartwatches, and smart mattresses, to assess their accuracy, usability, limitations, and how well they integrate with existing healthcare systems. This review evaluates 21 smartphone apps, 16 smartwatches, and nine smart mattresses through systematic data collection from academic literature, manufacturer specifications, and independent studies. Devices were assessed based on sleep-tracking capabilities, physiological data collection, movement detection, environmental sensing, AI-driven analytics, and healthcare integration potential. Wearables provide the best balance of accuracy, affordability, and usability, making them the most suitable for general users and athletes. Smartphone apps are cost-effective but offer lower accuracy, making them more appropriate for casual sleep tracking rather than clinical applications. Smart mattresses, while providing passive and comfortable sleep tracking, are costlier and have limited clinical validation. This review offers essential insights for selecting the most appropriate home sleep monitoring technology. Future developments should focus on multi-sensor fusion, AI transparency, energy efficiency, and improved clinical validation to enhance reliability and healthcare applicability. As these technologies evolve, home sleep monitoring has the potential to bridge the gap between consumer-grade tracking and clinical diagnostics, making personalized sleep health insights more accessible and actionable. Full article

Artificial intelligence has increasingly taken over various aspects of daily life, resulting in the proliferation of smart devices and the development of smart living and working environments. One significant domain within this technological advancement is human activity recognition, which includes a broad spectrum of applications such as patient monitoring and supervision of children’s activities. In this research, we endeavor to design a human activity recognition system that effectively analyzes multi-user data through a machine learning framework centered on graphical plot images. The proposed methodology uses a PIR sensor-based system. This system uses a two-stage process; the first one involves generating new image datasets as density map images and graphical representations based on the Kyoto CASAS multi-user dataset. In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. To generate the new datasets, we only used data from ambient sensors, which were organized in windows. We tested many types of window dimensions and extra features such as temporal aspect and the limitation of two activities in one window. The neural network was optimized by increasing the deconvolutional layers and adding the AdamW optimizer. The results demonstrate the viability of this method, evidencing an accuracy rate of 83% for multi-user activity and an accuracy rate of 99% for single-user activity. This study successfully achieved its objective of identifying an efficient activity recognition methodology and a data image representation. Furthermore, future enhancements are anticipated by integrating data sourced from PIR sensors, with information gathered from user-personal devices such as smartphones. This approach is also applicable to real-time recognition systems. Full article

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. Full article