1. Introduction
Chronic diseases are the most prevalent and non-communicable health issue, causing the highest global mortality rate [
1]. Numerous individuals live with complicated health conditions that affect their overall quality of life. Furthermore, the prevalence of these chronic diseases tends to escalate as patients age [
2,
3]. The delay in diagnosing and treating these chronic illnesses contributes to the deterioration of patients’ overall well-being. Therefore, the early identification of symptoms of chronic illness is crucial. Continuous physiological monitoring allows for the early detection, management, and prevention of chronic diseases through the use of wearable technologies [
4].
A wearable device enables the continuous acquisition of physiological signals, allowing healthcare providers to closely monitor patients’ overall health status [
5,
6,
7]. Moreover, these devices are not limited to just measuring physiological parameters but can also process the data collected. These wearable devices possess immense potential for applications in health monitoring and diagnosis [
8]. Individuals with disabilities and elders engaged with multiple health concerns can now access healthcare without the need to physically visit a hospital [
9,
10]. However, the primary obstacle faced by wearable devices in healthcare is battery life shortages, particularly when monitoring patients in critical situations. To address this, power management has become paramount in wearable devices to deliver dedicated healthcare monitoring without power failure and maximize the battery life of these devices [
11,
12]. Nowadays, individuals seek durable and energy-efficient wearable devices that fulfill their requirements. Consequently, uninterrupted monitoring can only be achieved if the wearable device has sufficient battery power to gather physiological data [
6,
13].
Low-power techniques (LPTs) play a crucial role in minimizing power consumption in wearable devices, particularly for the purpose of continuous physiological monitoring [
14,
15]. The significance of reducing power consumption in wearable devices is a top priority for resource-constrained devices [
10,
11,
16]. Hence, this review aims to investigate LPTs utilized for power consumption reduction and identify their barriers in wearable medical devices for healthcare settings. LPTs play an important role in prolonging the lifespan of these devices, enabling continuous monitoring and measurement of physiological parameters. These parameters offer valuable insights into the physiological condition of patients. Numerous techniques for reducing power consumption have been suggested, particularly for wearable technologies [
17,
18,
19,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46]. Despite the rapid advancements in wearable technologies, the power consumption challenge continues as these devices are still power-aggressive. The main achievements of this review include the following:
- i.
Analyzing and identifying LPTs used in wearable medical devices for acquiring, processing, and transmitting physiological parameters.
- ii.
Classifying and establishing a taxonomy of LPTs depending on their common features and use in medical applications for healthcare.
- iii.
Exploring the barriers and possible enhancements in the utilization of LPTs within multimodal medical wearable devices.
This review highlights the significance of LPTs in wearable devices by comparing the various methods and proposing a combined approach. It provides an overview of the power consumption in wearables, emphasizing the importance of reducing power consumption and introducing common techniques used in wearables for healthcare.
Section 2 outlines the motivation behind this review, referencing state-of-the-art research in wearable healthcare devices. It offers a comprehensive comparative analysis of existing reviews, highlighting their respective strengths and weaknesses in comparison to our work.
Section 3 details the methodologies employed in the screening of different databases, the analysis of relevant research papers, and the inclusion and exclusion criteria. LPTs used in the selected works are analyzed in
Section 4. A novel taxonomy is proposed based on the identified LPTs based on classification criteria, which is discussed in detail in
Section 5. In order to support our proposed taxonomy, a bibliometric analysis is presented in
Section 6, which also highlights the importance of LPTs in wearable medical devices. This section also provides a critical analysis of the practical implications of these LPTs along with the effects of a proposed hybrid approach. Finally, we conclude our work by summarizing the key findings and suggestions for future research in
Section 7.
2. Related Work and Motivation
Over the last few years, several studies have addressed the differences in low-power technologies proposed to alleviate the limited power budget of wearable electronics. According to recent works [
47], wearable medical devices are still facing power issues that require additional research to create ultra-low-power devices. The need for energy-efficient solutions is growing, with a focus on minimizing power consumption for optimal operation and reduced maintenance. As stated in [
48], circuit design is a major concern, particularly when developing circuits for low-power applications. The incorporation of more transistors into circuits leads to simultaneous increases in power dissipation and circuit size. The power consumption in circuits is a crucial factor, particularly in the development of circuits for low-power applications. A study conducted by [
49] also indicated that different power management techniques for wearable devices depend on the device resources and application requirements. Dynamic power management techniques, scheduling algorithms, and duty cycling are considered to be the most suitable approaches for optimizing system resources and meeting application requirements. Various power management techniques have been utilized in studies to prolong the lifespan of wearable devices, such as circuit design, clock gating, power gating, duty cycle, compressive sensing, clock frequency, voltage reduction, and sampling schemes, as proposed in recent research [
6].
The trends and challenges of modern wearables are discussed in [
50]. As the future of wearable devices will become more compact and integrated, it is essential to focus on energy-efficient techniques that are practical and durable. The combination of energy-efficient systems and a continuous power supply for wearables is crucial for maximizing power savings. The authors in [
51] introduced a battery-free mechanism that relies on multiple power sources. The proposed power management system enables reliable, battery-free, and continuous monitoring of self-contained wearable devices. Efficient energy management is a critical factor in the system-on-chip design. As per [
52], low-power very-large-scale integration (VLSI) technology is introduced to manage energy at various levels, including systems, algorithms, architectures, and circuits. These techniques are considered at the design level to minimize the power consumption of modern wearable devices. The work carried out by [
53] demonstrated that cloud computing offers a dependable and optimal environment for robust life-saving systems that are designed to function without any communication interruptions. Real-time medical applications depend heavily on data and communication systems for remote health monitoring, leading to the rapid generation of large volumes of data.
The challenges of processing and managing a large amount of clinical data on resource-limited wearable devices is discussed in [
54]. The authors suggest lossless data compression to ensure that no relevant clinical information is lost during the compression process. In addition, a study conducted in [
55] demonstrated that advanced power sources for wearable devices can be achieved through the continuous integration of the required energy capacity. These power sources include batteries, energy harvesters, ambient environments, and hybridization technologies. In [
56], the authors focused on reducing the power consumption for major power consumers, such as the main CPU and system interconnect. The demand for compact and low-power wearable devices has grown because of their contribution to health monitoring and disease treatment. Therefore, improving energy efficiency is crucial for power-hungry devices [
57]. A recent study [
58] introduces a task-scheduling algorithm for wearable medical devices, aiming to reduce power consumption by optimizing task scheduling.
In [
59], low-power technologies are generally investigated for telecare and telehealth wearable systems that continuously monitor a user’s physiological status. The work roughly categorized low-power technologies into two major categories, namely hardware- and firmware-based approaches. However, the effectiveness and evaluation of using their techniques for power reduction during physiological monitoring were not discussed. The authors in [
17] assess low-power wearable development for continuous physiological monitoring. This review intends to analyze physiological signals, wearable design considerations, and vital parameters for preventive healthcare. Unfortunately, the work did not specifically address LPTs for wearable devices for continuous monitoring of health conditions.
In [
60], a solution for the limited life of the battery of miniature devices for the future is presented. The authors proposed battery-less or implantable wearable devices that harvest energy from the physical body of humans or the environment using thermoelectric generators, piezoelectric generators, solar energy, and radio frequency. However, the challenges associated with these energy-harvesting techniques and their effectiveness were not investigated in wearables.
In healthcare applications, human context recognition is becoming a challenging task for offering continuous physiological monitoring to users. According to [
61], human context recognition faces a power issue for continuous monitoring of personal and environmental parameters for many medical applications. As the authors indicate, energy-efficient mechanisms are classified based on human context-recognition (HCR) applications. The work boldly provides a combination of techniques used for further power reduction. However, the features of LPTs and the bottlenecks associated with them in wearable medical applications are not addressed in their work. Even though the authors intend to provide relevant information regarding LPTs for designers, the limitations of implementing the identified LPTs in HCR wearable systems are not explored. In [
7], wearable devices and their challenges are presented. The survey presents the categorization of commercial wearable products and research prototypes with little focus on power reduction. Energy efficiency is considered in terms of battery technologies, such as Li-poly and Li-ion batteries, as well as energy harvesting, such as kinetic, thermoelectric, and solar energy. According to [
62], the integration of biomedical processor SoCs in wearable devices is used to meet the requirements of healthcare applications. The review categorizes power-reduction techniques as communication, computation, and sensing for improving energy efficiency.
The authors in [
14] intensively reviewed energy efficiency for a wide application area in the Internet of wearable things. A developed taxonomy depends on an application area such as healthcare, activity recognition, and environments, but it does not consider features of LPTs for wearable devices. According to [
63], energy sources are the solution for powering wearable medical devices, such as batteries, biofuel cells, solar cells, supercapacitors, thermoelectric, piezoelectric, and triboelectric generators, and radio frequency. Their review targets energy-harvesting techniques instead of power-saving techniques.
Therefore, many reviews explicitly focused on the energy-harvesting aspects instead of briefly mentioning the features of the techniques, challenges in applying techniques, and power-saving techniques in wearable devices during continuous physiological monitoring, as shown in
Table 1. However, energy harvesting from environmental sources is often intermittent and unpredictable, leading to an inconsistent power supply. Some studies have evaluated algorithms without considering the limited resources of the wearables, while other studies have focused solely on reducing power consumption without considering vital signals Furthermore, most reviews are not aimed at LPTs as their primary goal; instead, they focus on a single energy efficiency module [
14,
59,
61,
62]. In fact, the power is consumed in different modules of wearable devices such as communication, ADC, MCU, and LCD.
To the best of our knowledge, few accessible studies have been conducted on cutting-edge research on LPTs in wearable medical devices with simultaneous vital sign monitoring. Although numerous reviews have been conducted in the area of energy harvesting for wearable medical devices, only a limited number of reviews have specifically categorized LPTs and their common features for medical applications in wearable devices. Instead, our novel taxonomy considers the power consumption of wearables during the acquisition, processing, and transmission of signals while detecting vital signs in wearable devices. Our work explores LPTs based on hardware, software, application, and self-awareness perspectives for managing static and dynamic power consumption, as well as the most power-consuming modules.
4. The Common LPTs for Wearable Devices in Medical Applications
In this section, the identified works were thoroughly investigated to uncover the various LPTs used, along with a detailed discussion to highlight their corresponding implications. Many strategies target the optimization of the time the device is active [
21,
74,
75]. In that regard, the optimization of the duty cycle, the ratio of time that devices or systems are active to the total time, leads to a significant power reduction by minimizing the active time of devices and maximizing their sleeping time. The sleep modes are used to turn off peripheral and idle components to reduce power consumption while keeping essential components and circuits active [
20]. This strategy is feasible when the devices and sensors need to be operated for a proportion of time and then go to sleep [
13,
76,
77]. Nevertheless, it becomes problematic when certain components or sensors must function continuously [
78]. A balanced computational workload is used for an efficient distribution of tasks across a processor to optimize performance, which leads to an increase in power savings [
22,
28]. It contributes to minimizing power consumption by enabling efficient parallelization of tasks across multi-cores, efficient resource utilization, and load balancing [
23]. Instead of processing sequentially, task pipelining provides parallel and concurrent data processing to reduce the execution time [
24,
26].
Similarly, a clock frequency determines a processor’s performance and power consumption [
79]. Higher clock speeds offer better performance, but they also increase power consumption. As the clock frequency of a processor increases, its power consumption increases linearly due to the higher dynamic power consumption. Therefore, the association between clock frequency and power consumption is crucial for optimizing the design and operation of devices [
11]. An effective clock frequency is essential to balance performance and power efficiency, especially in battery-powered and high-performance computing environments. Furthermore, the other technique is clock gating, which emphasizes minimizing dynamic power consumption, specifically by turning on or off some of the digital circuits. It is selectively applied to disable the clock signal when it is not needed and inactive [
28,
29]. However, glitch management is the challenge associated with clock gating, when an unintended pulse causes incorrect digital circuit operation.
Moreover, the other strategy for reducing power consumption in low-power devices is to minimize the number of samples needed for accurate signal reconstruction. Among these strategies, compressive sensing (CS) is a powerful approach that operates effectively with sparse signals in wireless sensor networks [
34,
35,
36,
37]. CS can be applied for signal processing, particularly for signal acquisition and transmission. So, it leads to reducing power consumption in low-power devices by acquiring fewer samples, lowering the computational load, and reducing data transmission. According to the conventional method based on Nyquist’s theorem, information must be preserved using a sampling frequency of at least twice the original signal’s bandwidth [
37]. Joint compressed sensing (JCS) extends the concept of CS when multiple signals that are correlated or share some common structure are acquired and reconstruction processes are performed. It contributes to reducing the power consumption of devices or sensors during data collection and processing from multiple sources. Therefore, it exploits the correlations between different data sources to perform data acquisition and compression simultaneously, thereby reducing the amount of data that need to be processed and transmitted, which leads to significant power savings [
38]. Correlated double sampling (CDS) enhances signal quality by reducing offset, low-frequency noise, and unwanted signal variation that downgrade the quality of the output. The key idea is that the process of reducing offset improves the signal quality without needing to increase power consumption [
39]. Knowledge-based adaptive sampling is a valuable strategy in data acquisition and sensor networks to minimize power consumption while maintaining or improving data quality. Therefore, it can reduce power by dynamically adjusting the sampling rate of signal acquisition [
40].
Finally, self-awareness is also a key aspect for low-power wearables, allowing the system to analyze and adjust its behavior and state to minimize power consumption, as reported in [
41,
80,
81]. Hence, it has the capability to adapt and evaluate processes in order to efficiently enhance energy usage. However, additional resources are required to reduce the device’s power consumption. A self-power manager can set parameters to define policies and perform a set of experiments to find the most efficient setting to minimize power. These parameters are observation, activity, energy level, policy, and priority. Depending on these parameters, it dynamically optimizes the power of the devices. This technique is smart power management, which optimizes power consumption [
18]. Furthermore, power gating is another strategy to effectively minimize static power consumption in digital circuits [
82]. It saves power by completely turning off the power supply to the inactive circuit by entering sleep mode. However, it takes time to power up a previously gated domain due to the need to stabilize the power supply and reinitialize the domain.
Hence, numerous techniques have been applied to wearable devices, ranging from complex computational algorithms to lightweight algorithms. The significant demand is to minimize power consumption, which is the target of many studies. Building upon the underlying ideas of different LPTs discussed in this section, we have classified them and present a taxonomy in the next section,
Section 5. This taxonomy can assist researchers in selecting suitable LPTs based on their underlying principles and applications.
6. Results and Discussion
This comprehensive analysis section assesses LPTs for wearable healthcare devices by synthesizing information from existing and relevant literature, incorporating a bibliometric analysis, critical examination of various LPTs’ aspects and corresponding implications. The distribution of the reviewed work by year of publication is illustrated in
Figure 3. The examined articles span from 2000 to 2023. Our investigation centers on analyzing and pinpointing the most pertinent articles concerning LPTs, biosignals, and vital signs in wearable devices that are published in global conferences and leading scientific publications. The majority of these studies focus on power optimization for wearable devices in the healthcare sector. Nevertheless, it should be noted that our work specifically addresses studies related to low-power technologies (LPTs). This review highlights that LPTs have been examined in healthcare settings with taking into account important vital signs in many studies. It is crucial to assess the impact of LPTs on power consumption in medical applications using wearable devices while considering vital signs.
The physiological data of a patient can be remotely acquired for healthcare purposes. However, to ensure continuous monitoring, various essential elements are needed for collaborative effectiveness. These elements include gathering physiological information, biomedical signals, LPTs, and wearable devices. They have a close connection to physiological monitoring and are typically integrated into healthcare applications. Wearable devices consume power during the acquisition to the transmission of physiological information to detect vital signs. As mentioned in
Table 3, the most common physiological signals are PPG, ECG, EMG, EOG, EEG, and PCG, which are typically measured using wearable devices and used to detect chronic diseases, injury or disorder, eye movements, brain damage, and others. Moreover, evaluating LPTs for reducing power usage in medical wearable devices also depends on the vital signs that are obtained, analyzed, and transmitted during operations. Vital signs serve as crucial indicators for identifying chronic illnesses through the measurement of physiological data. Heart rate (HR), blood oxygen saturation (SpO2), blood pressure (BP), heart rate variability (HRV), respiratory rate (RR), blood glucose, and various other physiological parameters are commonly observed.
The majority of the examined studies have utilized LPTs for analyzing ECG signals in medical contexts [
35,
38,
94], while some works also explored LPTs for PPG signals [
19,
21,
76,
95,
96]. Similarly, some other works have explored the use of LPTs in EMG [
97], EEG [
30], EOG [
98], and PCG signals [
9]. The predominant use of LPTs (40.3%) is for the acquisition of ECG signals via wearable devices, as illustrated in
Figure 4. The breakdown of LPT distribution for medical applications in wearable devices is as follows: 23.3% of work uses PPG signals, 16.3% of work uses EEG, 10.1% of work uses EMG, 6.2% of work uses EOG and 3.9% of work uses PCG signals.
As depicted in
Figure 5, the majority of studies in wearable devices employ signal compression as one of the methods to conserve power in their design. The subcategories within these classifications are determined by their shared characteristics for enhancing power efficiency in wearable devices, as explained in
Section 5.1. Task scheduling, signal compression, clock, and power management are the most frequently utilized strategies in wearable systems. Signal compression is commonly utilized in wearables, as it has been found to be the most frequently used strategy. This is due to the fact that compression and sampling techniques are effective in reducing power consumption [
36].
Figure 6 presents an elaborate categorization, offering a thorough classification of power-saving strategies and the frequently employed LPTs to reduce power usage in wearable devices.
Of the strategies, duty cycle adjustment is the most frequently exploited strategy in some works [
76,
95,
96]. Around 53% of the work utilizes the duty cycle optimization approach while acquiring a PPG signal for continuous physiological monitoring. For instance, the power is typically dominated by the light-emitting diode (LED) driver in the PPG sensor, which can be optimized by the duty cycle ratio of the LED as low as possible, as reported in [
39,
99]. Thus, duty cycle optimization involves switching the LED to regulate the frequency at which signals are acquired. Accordingly, the percentage distribution of LPTs in various biosignals is presented.
An ECG signal exploits a CS approach to process signals through wearable devices, as elaborated in many works. Accordingly, 42% of the work exploits the CS technique in ECG signals. Similarly, 43% of the work applies the CS approach to EEG signals. Furthermore, 54% of the work utilizes the CS technique in EMG signals, while 20% of the work capitalizes on this technique in PCG signals. The aforementioned strategy is also utilized for various other physiological signals, including the PPG signal, as reported in [
19,
100]. Nearly 16% of the works apply self-awareness and clock gating, 10% of the works apply power gating, 4% of the works apply JCS and knowledge-based adaptive sampling, 15% of the works apply duty cycle optimization, and 13% of the works apply frequency scaling in ECG signals. Similarly, a summary of the respective LPTs, the percentage of their application, and various biomedical signals are presented, as depicted in
Figure 6.
In comparison, it is observed that clock gating, power gating, and knowledge-based adaptive sampling are not extensively employed in PPG signals to the same degree as in ECG signals. This is because these strategies do not directly manage the LED flashlight of the PPG, as the duty cycle can be achieved. CS is the most frequently utilized and extensively adopted technique for obtaining ECG in medical wearable devices. Due to its ability to handle sparsity, it is utilized in medical applications and physiological monitoring [
101].
Furthermore, the recent trend in wearable medical devices to minimize power consumption involves combining LPTs with other low-power strategies [
102,
103]. This analysis highlights the effective utilization of combining various LPTs to exploit the strengths of their effects in several works [
18,
104,
105,
106]. The low-power wearable may require the implementation of multiple strategies during the design stage. The integration of multiple low-power methods during the power optimization procedure leads to an extended lifespan of the battery. For example, the duty cycle adjustment is the strategy that maximizes the sleeping time of the sensors to reduce power, whereas the compressive sensing strategy can lower the sampling rate for the transmission time. Therefore, the combination of these strategies would contribute to further power reduction. As shown in
Table 4, some of the combined LPTs applied in some works are presented as an eye-opener. The analysis of this review indicates that utilizing a single LPT does not significantly reduce power consumption, as power is utilized in various components of the modules, such as the MCU, ADC, LCD, sensors, and wireless communication.
This suggests that combined LPTs are more feasible regarding power consumption reduction, as they are applied to signal acquisition, processing, and transmission in wearables [
109]. Hence, it is advisable to employ a combination of LPTs to address power consumption challenges on devices with limited resources. This concept involves utilizing hybrid approaches or combining different LPTs to achieve power reductions. As the number of LPTs is combined, the rate of power consumption decreases. This review concluded that the implementation of combined LPTs yielded promising results in reducing power consumption for wearable devices.
The other important contribution of this work is to provide a research guideline for designing low-power strategies for wearable devices. An efficient approach to enhance performance is by optimizing the duty cycle, which involves periodically switching between active and passive periods for a component or sensor node. Once the necessary operation is completed, the respective component can be deactivated. This indicates that the optimization of the duty cycle is a successful strategy that can be employed to control the devices during their active period. For instance, the duty cycle applied to the heart rate monitoring periodically takes readings and then enters a sleep mode between measurements. This periodic activation reduces power consumption compared to continuous monitoring, extending battery life. Another similar approach is distributing workloads over processors and shutting down physical machines after the execution of a task. An effective strategy for successfully executing tasks and powering down physical devices is to ensure an equitable distribution of the computing workload.
Similarly, task pipelining is a method used to decrease power consumption by executing multiple operations simultaneously. An effectively structured task pipeline is essential for organizing tasks from different applications within pipeline features. For example, by applying task pipelining, the smartwatch efficiently uses power, balancing the need for real-time monitoring with the overall battery life.
Likewise, adjusting the frequency significantly influences the power usage of devices, as altering frequencies can impact power consumption. For instance, a device can operate at a low frequency during less intensive tasks and switch to a higher frequency for high workload data analysis or transmission, hence saving power. Clock and power gating are commonly employed methods to reduce power consumption in electronic circuits. Clock gating is a technique that allows for the selective disabling of the clock source in specific blocks of a circuit when those blocks are not in use. For example, the clock of the circuitry responsible for processing heart rate can be disabled when it is inactive. On the other hand, power gating is a highly efficient method for reducing power consumption by blocking the power supply to idle portions and non-operational components of the design. For instance, it can shut off power to a specific component, when blood glucose monitoring is not in use.
On the other hand, CS is effective in reconstructing sparse signals from limited samples; however, a major drawback is the additional power consumption required to reconstruct the compressively sampled signals. For example, compression algorithms are used in a spirometer to minimize the size of respiratory data before transmission. CDS provides a reliable method for eliminating drift and reducing low-frequency noise in sensitive measurements, while also allowing for offset subtraction. Furthermore, the implementation of a JCS method is crucial in order to minimize power usage in medical wearable devices effectively. This technique takes into account and leverages the correlations present in both the signal acquisition and reconstruction processes. Additionally, it is imperative to determine the ideal and flexible sampling frequencies for the sensor, considering that the power consumption of the sensor is greater than that of other components. Hence, employing knowledge-based adaptive sampling proves to be a successful approach to attain this objective. For example, an activity tracker can dynamically adjust its sensor-sampling rate in knowledge-based adaptive sampling.
In addition, it can be quite challenging to uphold and recharge a battery in demanding circumstances. Hence, self-awareness is utilized in healthcare applications during crucial circumstances. In cases where a patient is facing severe medical conditions or when it is not feasible to recharge a battery, self-awareness becomes essential. The technique offers patients pertinent details regarding the battery’s condition prior to reaching a crucial point [
16]. It is imperative to have a self-power manager in place when establishing power-reduction policies to ensure the continuous operation of the system. This power strategy is an intelligent approach that effectively manages power by utilizing parameters and policy.
We have also noticed that different works utilize various computing platforms from different vendors. Some vendors offer low-power tuning features, such as voltage scaling and frequency scaling, while others do not. Additionally, these features are not available for fine-grain-level tuning and only operate at a coarse level. Moreover, the flexibility to shut down internal modules of different computing platforms is limited, leading to unnecessary power consumption. Providing the ability to turn off these internal modules could help mitigate this issue. Furthermore, the analysis shows that using a single LPT does not significantly reduce power consumption. Combining multiple LPTs can lead to greater power savings, but their effectiveness is limited by the lack of flexibility in some computing platforms.
Finally, this analysis ultimately identified the constraints associated with employing LPTs in medical wearable devices. Managing the total power consumption of medical wearable devices is a challenging task due to the consumption and dissipation of power across different components involved in signal acquisition, processing, and transmission. Hence, every low-power method faces a limitation when implemented to decrease power usage in wearable devices. An analysis of the strengths and drawbacks of using LPTs in devices is provided, as depicted in
Table 5.
7. Conclusions
The analysis of this review represents a significant achievement in the development of energy-efficient wearable devices for medical healthcare purposes. The continuous monitoring of a patient’s physiological parameters heavily relies on the power optimization capabilities of wearable medical devices. Hence, this review discovered the impact of LPTs in reducing power consumption in wearable medical devices used for obtaining and tracking a patient’s physiological parameters. The continuous monitoring of specific physiological parameters necessitates a particular emphasis on the design of low-power wearables. Therefore, the findings presented in this work help developers, designers, and researchers design low-power wearable devices for healthcare applications. The elaboration encompasses the specifics of various LPTs, the classifications of signals, the continuous monitoring of parameters, the integration of combined LPTs, and the challenges that must be taken into account when suggesting LPTs for wearable devices. Additionally, this review introduces an LPT classification system aimed at minimizing power consumption in wearable medical devices.
However, the implication of task scheduling is periodically entering power-saver modes, which has no guarantee for continuous operations, for example, during surgical procedures. Furthermore, signal compression has implications during complex and noisy signals, because it is difficult to accurately reconstruct the full signal without losing important information, for instance during the medical image acquisition of the brain or lungs. Likewise, clock management leads to potentially hindering the device’s capacity to promptly detect and respond to arrhythmias due to a decrease in the clock’s speed, for instance, during critical health data communication in emergencies; it needs to operate at full speed to ensure timely and reliable data transmission. Furthermore, self-awareness restricts the effectiveness and applicability in certain medical applications due to the need for additional resources like memory or data communication during severe mental health conditions such as disorders or respiratory conditions.
The findings of this work argue that duty cycle optimization is a feasible technique in PPGs and PCGs; furthermore, CS is for ECGs, EEGs, and EMGs; self-awareness and clock gating are for ECGs; and frequency scaling is for EOGs and EEGs. Furthermore, the review concluded that utilizing a combination of LPTs could lead to a decrease in power consumption across various medical applications. This work explored LPTs in wearable devices in terms of an application, hardware, and software perspective. Therefore, this article provides relevant information regarding LPTs for researchers and developers to design a low-power wearable medical device for continuous physiological monitoring of patient health status.
The experimental evaluation of each technique using vital signs with different simulations is crucial for analyzing their power consumption. The future direction of this research is to assess the appropriate techniques using various simulations to demonstrate their power profiles.