Walk-to-Charge Technology: Exploring Ef ﬁ cient Energy Harvesting Solutions for Smart Electronics

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Introduction
Wearable sensors have gained significant attention in recent years due to their potential to provide continuous, real-time physiological information, making them invaluable for various applications.
They have revolutionized fields such as health monitoring, sports and fitness, rehabilitation, environmental monitoring, and human-computer interaction.In the realm of health monitoring: (a) Wearable sensors have become widely used for tracking physical activity, heart rate, blood pressure, and sleep patterns.Researchers have conducted studies, such as the one by Murakami et al. [1], to assess the accuracy of wearable devices in measuring physical activity and energy expenditure.Similarly, in sports and fitness applications, wearable sensors play a crucial role in tracking performance metrics like speed, distance, and acceleration.(b) A study by Derungs and Amft [2] looked into the use of wearable sensors to track and examine athletes' biomechanics during sprinting.(c) Moreover, wearable sensors have proven instrumental in rehabilitation settings by monitoring patients' movements and providing feedback to enhance their motor skills.This application has opened new possibilities for improving the effectiveness of rehabilitation programs.(d) In the realm of environmental monitoring wearable sensors have been employed to monitor air pollution levels, particularly in urban areas.A notable study by Maity et al. [3] explored the use of wearable sensors for monitoring air pollution exposure, highlighting their potential in assessing environmental risks.(e) Furthermore, wearable sensors have found applications in human-computer interaction, enabling hands-free control and user behavior monitoring.Research conducted by Ates et al. [4] delved into the utilization of wearable sensors for managing smart home equipment, showcasing their potential for enhancing daily life experiences.
The significance of wearable sensors lies not only in their diverse applications, but also in their ability to provide realtime monitoring, delivering valuable insights into individuals' wellness and health.They have the potential to improve chronic disease management and promptly alert both users and medical professionals to abnormal or unforeseen situations, thus obviating the need for invasive procedures like blood sampling.To ensure user comfort and seamless integration into daily routines, wearable biosensors require advanced materials and smart designs that provide flexibility, stretchability, and direct contact with sampled biofluids without inducing discomfort.Recent developments have focused on achieving this body compliance, ensuring a wearer's comfort while maintaining accurate sensing capabilities [5,6].
The lifespan of sensors is crucial due to its significant impact on various applications, from environmental monitoring to health care and industrial automation.A longer lifespan in sensors leads to cost-effectiveness, sustainability, and uninterrupted operations.
Battery life is a critical factor in sensor lifespan, especially for wireless or portable sensor applications.A longer battery life ensures extended autonomy and reduces the need for frequent replacements or recharging.This is vital in remote or inaccessible locations where maintenance is challenging.Battery life directly affects the efficiency, reliability, and operational cost of sensor networks.A short battery life can result in data gaps and system failures, compromising data accuracy and integrity.
Researchers are actively working on the following to improve sensor battery life and endurance [7][8][9][10]: (i) Energy-efficient designs: Efforts are focused on developing low-power and energyefficient sensor circuits and architectures to minimize power consumption during sensing, data processing, and communication.

(i) Advanced battery technologies:
Researchers explore innovative battery materials, such as solid-state batteries or lithium-sulfur batteries, which offer higher energy densities and longer lifespans than the traditional battery technologies.

(i) Energy harvesting techniques:
Integrating energy harvesting mechanisms, like solar cells, piezoelectric elements, or thermoelectric generators, can supplement or replace batteries, especially for low-power sensors.
(i) Wireless power transfer: Wireless charging or power transfer techniques are being investigated to recharge sensors remotely, eliminating the need for physical intervention.
(i) Sleep and wake-up strategies: Smart algorithms are developed to enable sensors to enter low-power sleep modes when inactive and wake-up efficiently when needed, conserving energy during idle periods.

(i) Data compression and transmission optimization:
Research focuses on data compression techniques and optimizing communication protocols to reduce energy consumption during data transmission.
These research initiatives aim to extend sensor lifespan, enhance efficiency, and improve overall performance to meet the growing demand for long-lasting, reliable, and sustainable sensor networks.
In addition, there is research being conducted on developing better battery technologies for sensors.This includes exploring the use of new types of batteries, such as solid-state batteries, which have the potential to provide higher energy density and longer lifetimes than traditional batteries.Some energy solutions that can be employed to improve the battery life of wearable sensors are given in Figure 1.
As of a few years ago, the adoption of wearable sensors in medical contexts was increasing steadily.Estimates from market research and industry reports suggested that the global market for medical wearable devices was experiencing strong growth.This growth was driven by factors like the increasing prevalence of chronic diseases, a growing aging population, advancements in sensor technology, and the increasing trend of people taking proactive approaches to their health [11][12][13][14][15][16][17][18].
The lifetime of sensors is very important because it has a big effect on wearable sensors used for medical purposes.These sensors are crucial tools that gather important data from the body to help with health monitoring and care (refer Figure 2 for a graphical illustration of the many uses for onbody sensors).If the sensors do not last long, it can lead to problems and affect the usefulness of the device.For instance, if a wearable sensor stops working too soon, it might not be able to give accurate information about a person's health.This can be risky, especially for people with chronic illnesses who rely on these sensors to keep track of their condition.If the sensor dies quickly, it might mean not getting the right treatment or missing important health changes.In addition, the lifespan of the sensors can affect their cost and practicality.If they need to be replaced frequently, it can become expensive for both the individuals and health care systems.It can also be frustrating for users to constantly replace or recharge the sensors.

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Journal of Sensors In summary, the lifespan of wearable medical sensors is critical for maintaining a steady stream of accurate and reliable data.A longer lifespan ensures that health care professionals can make well-informed decisions, provide timely interventions, and offer more effective medical care to patients.

Optimizations of the Hardware Components of Wearable Sensors
Optimizing the hardware components of wearable sensors typically involves findings balance between the sensor's functionality, power consumption, size, weight, and cost.Some common Sensor sleep mode reduces the sampling rate of the data to be transmitted.
Reducing the clock speed using power efficient components for optimizing the firmware.The use of solar panels, thermoelectric generators utilizing piezoelectric material.
Placing the sensor on the charging pad or wireless charging case.
Optimizing the charging cycle for monitoring the battery health to avoid over or deep charging.hardware components in wearable sensors include sensors (e.g., accelerometer, gyroscope, heart rate monitor, etc.), microcontrollers, wireless communication modules, and power sources (e.g., batteries).To optimize the functionality of the sensor, the selection of hardware components must be carefully considered.For example, selecting a high-quality accelerometer with a high sampling rate can improve the accuracy of motion tracking.Similarly, selecting a reliable and energy-efficient microcontroller can help ensure that the sensor can collect and process data without consuming excessive power.
To optimize power consumption, the designers of wearable sensors often choose low-power hardware components, use power management techniques, and optimize software algorithms.For example, sensors with lower power consumption may be chosen, and sleep modes may be implemented to minimize power usage when the sensor is not actively collecting data.Size and weight are also important considerations for wearable sensors, as these factors can affect the user's comfort and willingness to wear the device.Designers may opt for smaller and lighter components, such as microcontrollers and batteries with a small form factor to reduce the overall size and weight of the sensor.Finally, cost is an important factor to consider, as wearable sensors must be affordable enough to be accessible to consumers.Designers may opt for more affordable components, such as those with lower accuracy or performance, in order to reduce the overall cost of the sensor.Here are some examples of studies that have investigated the optimization of hardware components in wearable sensors: (i) Power optimization of wearable sensors: wearable sensors often have limited battery life due to their small size, power-hungry components, continuous data processing, and the challenge of integrating rechargeable batteries without compromising comfort and usability.A study by Zhang et al. [19] proposed a power optimization strategy for wearable sensors that involves dynamic voltage and frequency scaling (DVFS) and adaptive duty cycling.The results showed that the proposed strategy can significantly reduce power consumption while maintaining the accuracy of the sensors.(ii) Sensor placement optimization: accurate measurements in wearable sensors rely on proper sensor placement to ensure proximity to target areas, optimal contact with the body, and mitigation of artifacts or interference from surrounding tissues or movements.A study by Guo et al. [20] investigated the optimal placement of sensors for measuring muscle activity using electromyography (EMG).
The results showed that the optimal placement of the sensors depends on the specific muscle being measured.(iii) Optimization of sensor materials: the choice of sensor materials impacts the accuracy and reliability of wearable sensors due to considerations like sensitivity, stability, biocompatibility, flexibility, and compatibility with the target measurement parameters, environmental conditions, and the wearer's comfort and safety.A study by Lee et al. [21] investigated the optimization of sensor materials for measuring skin temperature.The results showed that a sensor made of polyethylene terephthalate (PET) with a silver nanowire (AgNW) electrode had the highest sensitivity and stability.(iv) Optimization of sensor fusion: sensor fusion enhances accuracy by cross-validating measurements, compensating for limitations or biases of individual sensors, capturing a more comprehensive picture of the wearer's physiology, and enabling advanced data fusion and analysis techniques.A study by Li et al. [22] investigated the optimization of sensor fusion for monitoring human gait.The results showed that fusing data from an accelerometer and a gyroscope can significantly improve the accuracy of gait detection.
2.1.Low-Power Wearable Sensors.Low-power wearable sensors are crucial for several reasons.First and foremost, they enable extended battery life, which is vital for uninterrupted and prolonged use of wearable devices.Longer battery life enhances the user experience by reducing the frequency of recharging or replacing batteries, making wearables more convenient and practical in everyday life.Moreover, lowpower consumption is essential for wearables designed for continuous monitoring, such as health and fitness trackers, where users expect the devices to operate for extended periods without interruptions.However, achieving low-power consumption in wearable sensors presents several challenges.The miniaturization of wearable devices limits the available space for bulky batteries, thereby necessitating the use of power-efficient components.Furthermore, wearable sensors typically perform continuous data acquisition and processing, which can quickly drain the battery if not managed efficiently.The constant transmission of data wirelessly to connected devices also consumes significant power.Additionally, wearable sensors need to strike a balance between power consumption and maintaining accurate measurements, as reducing power too much may compromise the quality and reliability of the captured data.
To overcome these challenges, several methods and techniques can be employed.One approach is to utilize powerefficient components and integrated circuits.Low-power microcontrollers, which optimize energy consumption during data processing, can be employed in wearable sensor designs.These microcontrollers are specifically designed to operate on minimal power, allowing for efficient data acquisition and analysis while minimizing power drain.Additionally, low-leakage transistors can be used to minimize power loss during signal amplification and conditioning.
Energy harvesting is another method to reduce power consumption in wearable sensors.It involves capturing and converting ambient energy sources, such as solar, thermal, or kinetic energy, into usable electrical power.By incorporating energy harvesting technologies, wearable sensors can supplement or even recharge their batteries, reducing reliance on external power sources.For instance, solar panels can be integrated into the wearable device, harnessing sunlight to generate electrical energy.
Effective power management strategies are essential to optimizing power consumption in wearable sensors.Duty cycling is a technique where the sensor periodically switches between active and sleep modes.During sleep mode, power consumption is significantly reduced, extending the battery life.The duty cycle can be intelligently adjusted based on usage patterns or triggered by specific events, ensuring power efficiency while still capturing relevant data.Sleep modes and power gating can also be implemented for individual components within the wearable device to further minimize power drain.
The choice of materials plays a significant role in designing low-power wearable sensors.Energy-efficient sensors, such as low-power accelerometers or heart rate monitors, can be utilized to minimize power consumption during data acquisition.These sensors are designed to provide accurate measurements while operating at lower power levels.Lowpower wireless communication modules, such as Bluetooth Low Energy (BLE), can be employed to reduce power consumption during data transmission.These modules consume significantly less power compared to traditional wireless communication protocols, ensuring efficient data transfer without compromising battery life.Figure 3 shows low-power wearable sensors and their estimated power requirements available in the literature.

Optimized Wearable Sensor Placement.
Wearable sensor placement optimization is the process of determining the optimal locations for sensors on a wearable device in order to obtain accurate and reliable measurements of the physiological and biomechanical signals that are of interest.
Challenges in optimized wearable sensor placement include determining the optimal sensor locations for accurate data collection, addressing user comfort and mobility, and managing interference between sensors.Overcoming methods involve conducting thorough user studies and data analysis to identify optimal sensor placement locations, designing ergonomic, and flexible sensor housing for user comfort, utilizing sensor fusion techniques to mitigate interference and improve data accuracy, and employing machine learning algorithms to dynamically adjust sensor placement based on user activities and preferences.
The optimal placement of sensors depends on the specific application and the signals that need to be measured.Here are some general considerations for optimizing wearable sensor placement [23][24][25][26][27][28][29][30][31][32][33]: (a) Identify the physiological and biomechanical signals of interest: before deciding on the sensor placement, it is important to identify the signals that need to be measured.This will help determine the number and type of sensors that are required.Considering the anatomy and biomechanics of the target area, the placement of the sensors should consider the anatomy and biomechanics of the target area.For example, if the target area is the wrist, the sensors should be placed where the skin is relatively thin and where there is less movement.sensor placement in wearable devices, challenges arise in ensuring both comfort and usability.The sensors need to be positioned in a way that does not cause discomfort or hinder the wearer's movements.Achieving this requires careful consideration of the device's form factor, flexibility, and materials used.Soft and flexible materials, such as silicone or fabric-based sensors, can enhance comfort and conform to the body's contours.Additionally, integrating sensors into adjustable bands or utilizing adhesive patches can provide flexibility and customization options for optimal sensor placement, ultimately enhancing user comfort and usability.Users may not adopt wearable technology that is uncomfortable or challenging to use.(d) Test and validate the sensor placement: once the sensor placement has been determined, it is important to test and validate the accuracy and reliability of the measurements.This may involve comparing the sensor measurements to gold standard measurements or to other sensors placed in different locations.Overall, optimizing the placement of wearable sensors is a complex process that requires careful consideration of multiple factors.The goal is to obtain accurate and reliable measurements while maximizing user comfort and usability.

Optimization of Wearable Sensor Materials.
The optimization of wearable sensor materials involves identifying and selecting materials that can provide accurate and reliable sensing capabilities while also being comfortable and durable for use in wearable devices (refer Figure 4).Here are some considerations for optimizing wearable sensor materials:  FIGURE 4: Medical wearable sensor's-wearable graphene-hybrid electrochemical device on a subject's skin with perspiration for glucose monitoring; wearable patch integrated with a pH sensor and a temperature sensor for glucose detection; self-healing sensing layer of multifunctional electronic skin that is composed of three different sensors (temperature, pressure, and pH); hand-held breathalyzer system.6 Journal of Sensors can optimize wearable sensor materials to improve their accuracy, comfort, and durability.
Wearable sensor materials encompass a wide range of options that offer flexibility, durability, and compatibility with the human body.Some notable examples include conductive textiles, which integrate conductive fibers into fabric for comfortable and unobtrusive wear.Graphene, a single layer of carbon atoms, provides excellent conductivity, mechanical strength, and flexibility for sensor applications.Organic semiconductors, made of carbon-based materials, offer flexibility and can be printed on various substrates.These materials enable the development of wearable sensors with conformable and stretchable properties [34][35][36].
Optimization techniques play a crucial role in improving the performance and energy efficiency of wearable sensors.Low-power designs, such as using power-efficient microcontrollers and sensors, help extend battery life and reduce overall power consumption.Signal processing algorithms are optimized to extract relevant information from sensor data while minimizing computational requirements.Data compression techniques are employed to reduce the amount of data transmitted and stored, conserving energy, and enhancing data transfer speeds.These wearable sensor materials and optimization techniques find applications in a multitude of fields.In sports and fitness, wearable sensors can track biometric data, monitor exercise performance, and provide realtime feedback for athletes and fitness enthusiasts.In health care, they enable remote patient monitoring, early disease detection, and personalized medicine by continuously monitoring vital signs and collecting health-related data.Industrial applications involve the use of wearable sensors to enhance worker safety by monitoring physiological indicators, detecting fatigue or stress levels, and identifying potential workplace hazards [37,38].

Optimizations of Sensor Fusion.
Optimization of wearable sensor fusion involves improving the performance of a wearable device that uses multiple sensors to collect and process data.This optimization can be achieved through several approaches, as follows: (a) Sensor selection: one of the critical factors that affect the performance of wearable sensors is the type and quality of sensors used.Therefore, choosing the appropriate sensor for the intended application can optimize the performance of wearable sensors.(b) Calibration: the accuracy of wearable sensors can be improved by calibrating them to ensure that they provide consistent and reliable data.(c) Data fusion: the fusion of data from multiple sensors can improve the accuracy and reliability of the collected data.This can be achieved through various techniques, including statistical methods, machine learning algorithms, and signal processing techniques.(d) Power management: the battery life of wearable devices is a limitation, and improving power management can increase the operational time of wearable sensors.(e) Algorithm optimization: the algorithm that processes the data collected by the sensors can be optimized to improve the accuracy and reliability of the data.(f ) User experience: the design of the wearable device can be optimized to improve the user experience, which can lead to increased usage and better performance.(g) Sensor fusion involves combining data from multiple sensors to improve accuracy, reliability, and robustness in capturing and interpreting information about the environment.Commonly used techniques for sensor fusion include Kalman filtering, particle filtering, and Bayesian networks.Kalman filtering is widely used for estimating the true state of a system by combining measurements from different sensors while considering noise and uncertainties.Particle filtering, also known as Monte Carlo filtering, utilizes a probabilistic sampling approach to estimate the state based on sensor measurements.Bayesian networks employ probabilistic models to represent relationships between sensors and make inferences based on available data [39][40][41].
Sensor fusion often involves the integration of different types of sensors to capture complementary information.For example, in autonomous vehicles, sensor fusion combines data from light detection and ranging (LiDAR), radar, and cameras to enhance perception capabilities.LiDAR provides precise distance measurements; radar detects objects' speed and motion; and cameras offer visual information.Optimization techniques associated with sensor fusion include sensor calibration and synchronization to ensure accurate and aligned measurements.Furthermore, data alignment, time stamping, and synchronization are crucial to integrating sensor data accurately.Examples of sensor types used in sensor fusion applications include inertial sensors (such as accelerometers and gyroscopes) for measuring motion and orientation, environmental sensors (such as temperature, humidity, and pressure sensors) for capturing environmental conditions, and imaging sensors (such as cameras) for visual information.Optimization techniques include sensor selection and placement to maximize coverage and minimize redundancy, as well as data fusion algorithms tailored to the specific application to optimize information extraction and system performance [42,43].

Energy Harvesting Techniques
Wearable sensors are small electronic devices that can be attached to the body to collect data on physical activity, vital signs, and other health-related information.To operate, these sensors require a source of energy.Traditional batteries can be cumbersome and add extra weight to the device, so energy harvesting techniques have been developed to power wearable sensors.Here are some of the energy harvesting techniques (Figure 5) used in wearable sensors [34][35][36][37][38]: 3.1.Solar-Powered Wearble Sensor.A solar-powered wearable sensor is a device that can be worn on the body and is powered by solar energy.It is designed to collect data on various physiological parameters such as heart rate, body temperature, blood oxygen levels, and activity levels, among others.The device can be equipped with solar cells that absorb sunlight and convert it into electrical energy, which is then used to power the sensor.The solar cells can be integrated into the wearable device's design or can be detachable and attached to the clothing or accessory that the sensor is attached to.The sensor can transmit the collected data wirelessly to a smartphone app or a cloud-based platform for further analysis and interpretation.This can be useful for tracking fitness and health metrics, monitoring patients remotely, or conducting research studies.Challenges in solar-powered wearable sensors include optimizing solar energy conversion efficiency, managing variable light conditions, and integrating solar panels into wearable form factors. Overcoming methods involve utilizing high-efficiency photovoltaic cells, implementing maximum power point tracking algorithms to enhance energy extraction, designing wearable-friendly solar panels with flexibility and durability, and integrating energy storage solutions to mitigate power variations.Additionally, advanced power management techniques, such as energy-aware scheduling and adaptive power usage, can be employed to optimize energy consumption and extend the operational time of solar-powered wearable sensors.
Solar-powered wearable sensors have several advantages over traditional wearable sensors.They are environmentally friendly as they do not require batteries or electricity from the grid, and they can operate for extended periods without needing to be recharged.Additionally, they provide a sustainable solution to the growing demand for wearable technology, which is projected to increase significantly in the coming years [44,45].
Wearable devices incorporate various sensor types to capture and monitor different aspects of the user's environment   Journal of Sensors and body.Common sensor types found in wearables include heart rate sensors, which measure the user's pulse and provide insights into cardiovascular activity.Accelerometers and gyroscopes detect motion and orientation, enabling activity tracking, and gesture recognition.Temperature sensors monitor the body's ambient temperature, while ambient light sensors measure the light levels in the environment.These sensors collectively enable wearables to gather valuable data for health monitoring, fitness tracking, and environmental sensing.Solar cells offer a sustainable energy solution for wearable devices.Different types of solar cells can be used, including monocrystalline silicon, polycrystalline silicon, and thin-film solar cells.Monocrystalline silicon cells provide high efficiency, while polycrystalline silicon cells offer a cost-effective option.Thin-film solar cells, such as amorphous silicon or organic solar cells, are flexible and can be integrated into various form factors.In terms of possible locations, wearable devices can be designed to incorporate solar cells in strategic areas to maximize sunlight exposure.For example, solar cells can be integrated into the wristband of a smartwatch, the shoulder straps or fabric of a fitness tracker, or the brim of a solar-powered hat.These locations allow the solar cells to receive direct sunlight, optimizing their energy conversion efficiency.Additionally, transparent solar cells can be applied to the display or cover glass of a wearable device without obstructing the user's view [46][47][48].

Thermal Energy
Harvested by a Wearable Sensor.Thermal energy-harvesting wearable sensors are devices that can generate electricity from the temperature difference between the wearer's body and the environment.These sensors can be used for a variety of applications, such as health monitoring, fitness tracking, and environmental sensing.The principle behind thermal energy harvesting is the Seebeck effect, which involves the conversion of a temperature difference into an electric voltage [49].
Challenges in thermal energy-harvested wearable sensors include maximizing energy conversion efficiency, managing temperature differentials, and optimizing power generation from limited heat sources.Overcoming methods involve developing high-performance thermoelectric materials, designing thermoelectric modules with improved heat transfer characteristics, implementing power management circuits for efficient energy extraction, and utilizing heat sinks or insulation techniques to enhance temperature differentials.Additionally, innovative thermal energy-harvesting designs, such as flexible and wearable thermoelectric devices, can be explored to overcome challenges and improve the performance of thermal energyharvested wearable sensors.
The sensor is made up of two materials with different thermal conductivities, which generate an electric current when exposed to a temperature difference.There are various types of materials that can be used to make thermal energyharvesting wearable sensors, such as thermoelectric materials like bismuth telluride, lead telluride, and silicon-germanium.The design of the sensor also plays a critical role in its efficiency and performance.Thermal energy-harvesting wearable sensors have several advantages over traditional battery-powered sensors.They are environmentally friendly, as they do not require the use of toxic materials found in batteries.They also eliminate the need for battery replacements, making them more cost-effective in the long run.Additionally, they can operate in a wider range of environments and conditions, making them ideal for outdoor, and remote applications.Overall, thermal energy-harvesting wearable sensors have enormous potential for a range of applications and are a promising area of research and development for the future [50][51][52].
Wearable devices come in various forms and serve diverse purposes.Examples include smartwatches, fitness trackers, augmented reality glasses, smart clothing, and medical monitoring devices.Smartwatches provide features like notifications, fitness tracking, and health monitoring.Fitness trackers focus on activity tracking, heart rate monitoring, and sleep analysis.Augmented reality glasses overlay digital information onto the user's view, enabling immersive experiences.Smart clothing integrates sensors for tracking biometrics and movement.Medical monitoring devices can monitor vital signs, detect abnormalities, and aid in remote patient monitoring.Energy harvesting technologies play a crucial role in powering wearable devices sustainably.Solar energy harvesting utilizes the photovoltaic cells to convert sunlight into an electrical energy.This technology is commonly used in solar-powered wearables like watches or fitness trackers.Kinetic energy-harvesting harnesses the user's motion or body movements, such as piezoelectric materials that generate electricity when subjected to mechanical stress.This technology can be employed in wearable devices that capture energy from footsteps or body motion.Thermal energy harvesting utilizes temperature differentials to generate electricity, often through thermoelectric materials.This technology can be applied to wearables that capture body heat or temperature gradients.The applications of wearable devices powered by energy harvesting technologies are diverse.Solar-powered wearables are found in outdoor activities where users can rely on sunlight for continuous power supply.Kinetic energy harvesting can be utilized in sports and fitness wearables, allowing users to generate energy during physical activities.Thermal energy harvesting is beneficial in medical wearables, where body heat can be harvested to power medical monitoring devices.Energy harvesting technologies enable wearables to operate without the need for frequent battery replacements or recharging, offering convenience, extended usage, and environmental sustainability [53,54].
Mechanical energy can be harvested by a wearable sensor.A mechanical energy-harvesting wearable sensor is a device that captures mechanical energy from the user's movements and converts it into an electrical energy to power the sensor.These sensors can be used for a variety of applications, including health monitoring, fitness tracking, and environmental monitoring.The technology behind mechanical energy-harvesting sensors is based on the principle of converting mechanical energy into electrical energy using a piezoelectric material.Piezoelectric materials generate an electrical charge when subjected to mechanical stress, such Journal of Sensors as bending or stretching.By incorporating these materials into a wearable device, the mechanical energy generated by the user's movements can be captured and converted into electrical energy.There are several types of mechanical energy-harvesting wearable sensors currently available on the market, such as motion-powered watches, activity trackers, and smart clothing.
These sensors can track various metrics, such as steps taken, distance traveled, calories burned, and heart rate.Some sensors can even monitor environmental factors such as air quality and temperature.Mechanical energy-harvesting wearable sensors offer several benefits over traditional battery-powered sensors.They are more sustainable and ecofriendlier, as they do not require regular battery replacements.They are also more convenient to use, as they do not need to be charged or connected to a power source.Additionally, they can provide a continuous stream of data if the user is wearing the sensor and moving.
Wearable sensors can capture various types of mechanical energy to power themselves or supplement their energy needs.One source of mechanical energy is human motion, which can be harnessed through kinetic energy-harvesting techniques.For example, piezoelectric materials can convert mechanical vibrations or impacts caused by body movements into electrical energy.These materials can be integrated into wearable devices, such as smartwatches or fitness trackers, to generate power from the user's motion.Another source of mechanical energy is the ambient vibrations present in the environment.Wearable sensors can utilize piezoelectric or electromagnetic transducers to convert these vibrations into an electrical energy, enabling self-powered operation.Additionally, body heat can be used as a source of mechanical energy through thermoelectric energy harvesting [55,56].Thermoelectric materials can convert the temperature gradient between the body and the ambient environment into an electrical energy, providing a sustainable power source for wearables.By harnessing these mechanical energy sources, wearable sensors can reduce or eliminate the need for conventional power sources like batteries, enhancing their usability, and promoting energy efficiency.This opens opportunities for wearable sensors in various applications, including health monitoring, sports and fitness tracking, environmental sensing, and more [57][58][59].

RF-Harvested Wearable
Sensor.An RF-harvesting wearable sensor is a device that can capture and convert RF signals into electrical energy that can power the device or charge a battery.The wearable sensor is designed to be worn on the body, such as on the wrist, and can capture RF signals from sources such as Wi-Fi, Bluetooth, and cellular networks.The technology behind RF harvesting involves the use of antennas that are tuned to specific frequencies.When RF signals are captured by the antenna, they create an electrical current that can be used to power the device.The amount of energy that can be harvested depends on the strength of the RF signal and the efficiency of the harvesting process.
Challenges in RF-harvested wearable sensors include maximizing power harvesting efficiency, managing interference, and signal strength, and optimizing energy storage capabilities.Overcoming methods involve designing efficient RF energyharvesting circuits, utilizing antenna designs that enhance signal reception, implementing advanced power management techniques to handle variable RF power levels, and integrating energy storage solutions like supercapacitors or thin-film batteries.Additionally, careful placement and orientation of the wearable sensor can help improve RF energy-harvesting performance and mitigate challenges associated with signal interference and strength.
RF-harvesting wearable sensors have potential applications in various fields, including health care, sports, and energy management.For example, in health care, they can be used to power sensors that monitor vital signs such as heart rate and blood pressure.In sports, they can be used to power sensors that track athletic performance.In energy management, they can be used to harvest energy from the environment and reduce the need for batteries or other power sources.While RF-harvesting technology is still in its early stages, it has the potential to revolutionize wearable technology and enable a new generation of devices that can be powered by the environment.
RF-harvesting techniques offer a promising approach to powering wearable sensors by capturing and converting ambient RF energy into usable electrical power.This energy harvesting method is particularly useful in environments where wireless signals are abundant, such as urban areas or indoor settings.Wearable sensors can leverage RF harvesting to extend battery life or eliminate the need for batteries altogether.For example, Radio Frequency Identification (RFID) tags can harvest energy from nearby RF readers, enabling wireless identification, and tracking in applications like inventory management or access control systems.Another application is in health care, where RF-powered wearable sensors can continuously monitor vital signs or body parameters without the need for frequent battery replacements.These sensors can capture RF signals emitted by medical equipment or infrastructure, enabling nonintrusive and long-term monitoring.Additionally, in smart cities, RF harvesting enables wearable sensors to collect environmental data, such as air quality or noise levels, without relying on external power sources.Overall, RF harvesting techniques offer a promising solution for wearable sensors, reducing dependence on batteries, extending operational lifetimes, and enabling continuous monitoring in various applications ranging from logistics and health care to environmental sensing and smart cities [60][61][62][63][64].

Piezoelectric-Harvested Wearable Sensor.
A piezoelectricharvested wearable sensor is a type of sensor that generates electrical energy through mechanical pressure or vibrations.Piezoelectric materials, such as crystals or ceramics, can convert mechanical energy into an electrical energy.This type of sensor can be incorporated into wearable technology, such as clothing or jewelry, to measure various physical parameters, such as motion, pressure, or strain.The sensor works by converting the mechanical energy generated by the wearer's movements into an electrical energy, which can be stored and 10 Journal of Sensors used to power the wearable device or transmitted wirelessly to a separate device.Challenges in piezoelectric-harvested wearable sensors include optimizing energy conversion efficiency, managing power fluctuations, and ensuring reliable energy storage.Overcoming methods involve designing efficient piezoelectric materials and structures, optimizing power conditioning circuits for maximum energy extraction, implementing energy management algorithms to handle variable power inputs, and incorporating energy storage devices such as supercapacitors or rechargeable batteries.Additionally, advanced signal processing techniques can be employed to improve energy harvesting performance and enhance the overall reliability and usability of piezoelectrically harvested wearable sensors.
This technology has the potential to revolutionize the field of wearable devices, as it allows for a self-sustaining power source without the need for bulky batteries or frequent charging.Piezoelectric-harvested wearable sensors have many potential applications, including fitness tracking, health monitoring, and gesture recognition.They can also be used in industrial settings to monitor machinery and equipment for signs of wear and tear.Overall, piezoelectric-harvested wearable sensors have the potential to revolutionize the field of wearable technology by providing a self-sustaining power source and enabling new types of functionalities.

Software Optimization in Reducing the Energy Consumption of Wearable Sensors
Reducing the energy consumption of wearable sensors is crucial for improving their battery life and usability.Here are some software optimization techniques that can help achieve this goal: (a) Power-efficient algorithms: the algorithms used by wearable sensors should be optimized for power efficiency.For example, by using low-power signal processing algorithms, such as Fourier transform or wavelet transform, the energy consumption can be significantly reduced.(b) Duty cycling: duty cycling is a technique where the sensor is turned on and off periodically to save power.By turning the sensor off when it is not needed, the energy consumption can be reduced.However, the duty cycle should be chosen carefully to ensure that the sensor remains responsive when required.Table 1 shows various piezoelectric-harvested wearable sensors available in the literature.(c) Adaptive sampling: adaptive sampling is a technique that adjusts the sampling rate based on the activity level of the user.For example, if the user is stationary, the sensor can sample at a lower rate, and if the user is active, the sensor can sample at a higher rate.This technique can save energy by reducing the number of samples taken when the user is inactive.(d) Data compression: data compression techniques can be used to reduce the amount of data transmitted by the sensor.By compressing the data, the energy consumption can be reduced, as less data need to be transmitted.(e) Energy-efficient communication protocols: the communication protocols used by wearable sensors should be optimized for power efficiency.For example, BLE is a low-power wireless communication protocol that is commonly used in wearable devices.(f ) Power management: power management techniques can be used to optimize the power consumption of the sensor.For example, the sensor can be programmed to enter a low-power mode when it is not in use.
4.1.Power-Efficient Algorithms for Wearable Sensors.Challenges in power-efficient algorithms for wearable sensors include balancing algorithm complexity with energy consumption, optimizing computational efficiency, and addressing real-time processing requirements.Overcoming methods involve developing lightweight algorithms that minimize computational complexity, implementing intelligent data filtering and processing techniques to reduce unnecessary computations, leveraging low-power hardware architectures or specialized processors, and utilizing energy-aware scheduling and task offloading strategies to optimize energy consumption while meeting real-time processing demands in wearable sensor systems.
There are several power-efficient algorithms that can be used with wearable sensors to prolong battery life and improve the overall efficiency of the device.Here are a few examples: (1) Low-power signal processing: This technique involves using algorithms that consume minimal power to process signals from the sensors.By reducing the amount of processing required, the device can operate more efficiently and conserve power; (2) Adaptive sampling: This technique involves adjusting the sampling rate of the sensors based on the activity levels of the user.For example, if the user is inactive, the sampling rate can be reduced to save power; (3) Compressed sensing: This technique involves using algorithms to extract information from data that has been compressed or reduced in size.By reducing the amount of data that needs to be transmitted, the device can conserve power; (4) Event-based sensing: This technique involves using sensors that only activate when certain events occur.For example, an accelerometer sensor may only activate when the user starts walking or running.This reduces the amount of data that needs to be transmitted and processed, resulting in lower power consumption; and (5) Machine learning-based algorithms: Machine learning algorithms can be used to identify patterns and trends in the sensor data, allowing the device to make more accurate predictions and reduce the amount of data that needs to be transmitted and processed.Overall, the key to designing power-efficient algorithms for wearable sensors is to minimize the amount of processing and data transmission required while still providing accurate and reliable results.
Power-efficient algorithms for wearable sensors have been summarized here based on the references [70][71][72][73][74][75].Adaptive sampling algorithms dynamically adjust the Journal of Sensors 12 Journal of Sensors sampling rate of sensor data based on the user's activity level, finding applications in activity monitoring and motion tracking.Data compression algorithms reduce the size of sensor data before transmission or storage, conserving energy in data transmission, and storage tasks.Context-aware processing algorithms optimize sensor data processing by considering contextual information, reducing computational load, and power consumption in environmental sensing and contextual awareness applications.Duty cycling algorithms activate and deactivate sensor measurements or wireless communication periodically, conserving power in continuous monitoring and sensing scenarios.Collaborative processing algorithms offload computational tasks to nearby devices or cloud servers, reducing power consumption in distributed sensing, and edge computing scenarios.Machine learning-based classification algorithms perform real-time classification on the wearable device itself, reducing the need for continuous data transmission, and saving power in activity recognition and event detection applications.These power-efficient algorithms enhance the energy efficiency of wearable sensors, enabling longer battery life, and improving performance in various application domains.

Duty
Cycling for Wearable Sensors.Duty cycling is a technique used to reduce power consumption in wearable sensors.Challenges in duty cycling for wearable sensors include finding the right balance between active and sleep periods to minimize energy consumption while ensuring timely data collection, managing trade-offs between duty cycling and real-time data processing requirements, and handling latency issues.Overcoming methods involve optimizing duty cycling algorithms to adaptively adjust duty cycle parameters based on sensor data dynamics, employing low-power wake-up mechanisms for timely data capture, and integrating efficient data buffering and transmission strategies to mitigate the impact of sleep periods on data availability and latency in wearable sensor applications.In duty cycling, the sensor is turned on for a certain period and then turned off for another period.This approach helps to save battery life and increase the overall lifespan of the wearable device.There are different ways to implement duty cycling in wearable sensors.One common method is to use a sleep mode, where the sensor is put into a low-power state during the off period.This reduces the energy consumption of the sensor and extends the battery life.Another method is to use a wake-up signal to turn on the sensor only when necessary.This approach can be useful for sensors that need to monitor infrequent events, such as heart rate variability.The duty cycle for wearable sensors can vary depending on the application and the sensor type.Some sensors may need to be on continuously to provide real-time monitoring, while others may only need to be on intermittently to capture periodic data.The duty cycle can also be adjusted based on the battery life and power requirements of the wearable device.It is important to note that while duty cycling can help to save power, it can also introduce latency in the sensor data.The sensor may miss events that occur during the off period, which can affect the accuracy of the data.Therefore, it is important to carefully consider the trade-offs between power consumption, battery life, and data accuracy when implementing duty cycling in wearable sensors.
Duty cycles reported in the literature for different types of wearable sensors have been reviewed comprehensively based on the references [76][77][78][79].Accelerometers, commonly used for activity monitoring and gesture recognition, exhibit duty cycles ranging from 1% to 10%.By activating the sensor intermittently, power conservation is achieved while still collecting relevant data.Heart rate monitors, crucial for continuous heart rate monitoring and fitness tracking, display duty cycles varying from 1% to 50%.These duty cycles enable intermittent measurements to conserve power while providing regular updates on heart rate.Temperature sensors, employed in environmental monitoring and body temperature tracking, demonstrate duty cycles between 1% and 10%.They periodically measure and report temperature readings while operating in a low-power state otherwise.Gyroscopes, essential for orientation tracking and motion sensing, are often duty cycled to capture intermittent motion data at regular intervals, resulting in power consumption reduction.Ambient light sensors, used for brightness adjustment and activity recognition, can be duty cycled with duty cycles ranging from 1% to 10%.They periodically measure the light intensity in the environment while spending the rest of the time in a low-power state.These duty cycles allow wearable sensors to optimize power consumption without sacrificing essential functionality in their respective applications.

Adaptive Sampling for Wearable Sensors.
Adaptive sampling is a technique used in wearable sensors to optimize the sampling rate of sensor data.The goal of adaptive sampling is to reduce the amount of data collected by the sensor while still maintaining the accuracy of the measurements.Challenges in adaptive sampling for wearable sensors include determining the optimal sampling rate based on varying data dynamics, balancing energy efficiency with data accuracy, and handling data irregularities and sudden changes.Overcoming methods involve developing adaptive sampling algorithms that dynamically adjust the sampling rate based on data characteristics, implementing intelligent triggering mechanisms to capture relevant events, utilizing sensor fusion techniques to optimize sampling across multiple sensors, and leveraging machine learning algorithms to predict and adapt sampling rates based on user behavior or context, ensuring efficient energy utilization without compromising the data quality [80][81][82][83][84].
The basic idea behind adaptive sampling is to adjust the sampling rate based on the level of activity detected by the sensor.For example, if the sensor is detecting low levels of activity, such as when the wearer is sitting or standing still, the sampling rate can be reduced to conserve battery life and storage space.Conversely, if the sensor detects high levels of activity, such as during exercise or physical activity, the sampling rate can be increased to capture more detailed information.There are several methods for implementing adaptive sampling in wearable sensors.One approach is to use threshold-based sampling, where the sampling rate is adjusted Journal of Sensors based on a predetermined threshold level of activity.Another approach is to use machine learning algorithms to analyze the sensor data in real-time and adjust the sampling rate accordingly.
Some commonly used data compression techniques have also been observed for wearable sensors [85][86][87][88]: 4.4.Data Compression for Wearable Sensors.Data compression is an important technique for reducing the amount of data generated by wearable sensors.Challenges in data compression for wearable sensors include finding the right balance between compression ratio and preserving data accuracy, managing the trade-off between compression and computational complexity, and adapting compression algorithms to diverse sensor data types.Overcoming methods involve developing efficient compression algorithms specifically designed for wearable sensor data, leveraging advanced compression techniques like wavelet-based or predictive coding, and optimizing compression parameters based on the specific requirements of the wearable sensor application, ensuring minimal loss of relevant information while maximizing compression efficiency [89][90][91].
One common approach is to use lossless compression techniques, which can reduce the size of the data without losing any information.Lossless compression techniques include techniques such as run-length encoding, Huffman coding, and arithmetic coding.Another approach to data compression is to use lossy compression techniques, which can reduce the size of the data by sacrificing some information.Lossy compression techniques include techniques such as wavelet transform compression, discrete cosine transform compression, and predictive coding.The choice of compression technique will depend on the specific application and the requirements for the compressed data.For example, if the compressed data must be transmitted over a low-bandwidth network, lossy compression may be necessary to reduce the size of the data to a manageable level.On the other hand, if the compressed data are used for analysis or machine learning, the lossless compression may be preferable to preserve the information in the data [92][93][94].

Energy-Efficient Communication Protocols for Wearable
Sensors.Energy efficiency is a critical concern in the design of communication protocols for wearable sensors.Challenges in developing energy-efficient communication protocols for wearable sensors include limited power resources, bandwidth constraints, and reliable data transmission.Overcoming methods involve optimizing communication protocols for low-power consumption, implementing efficient data compression and aggregation techniques, utilizing adaptive transmission schemes based on sensor data characteristics, and exploring energy harvesting and wireless charging solutions to extend battery life and ensure seamless and reliable communication in wearable sensor networks.
Therefore, communication protocols for wearable sensors should be designed to minimize energy consumption while maintaining reliable communication.
There are several energy-efficient communications protocols that can be used for wearable sensors.Some of these protocols are suggested by Gomez et al. [90], Li et al. [91], Wang et al. [92], Haselsteiner and Breitfuß [93], Sharma and Namuduri [94], Perez et al. [95], Bahillo et al. [96], Centenaro et al. [97], and Catarinucci et al. [98] Challenges in power management for wearable sensors include limited battery capacity, dynamic power requirements, and energy-intensive sensing tasks.Overcoming methods involve implementing low-power hardware designs, employing intelligent power management techniques such as duty cycling and power gating, optimizing sensor data processing algorithms for energy efficiency, utilizing energy harvesting technologies, and exploring wireless charging and energyefficient communication protocols to maximize battery life and ensure sustained operation of wearable sensors.
Here are some tips for effective power management in wearable sensors [99][100][101][102][103][104]: (a) Use low-power components: Choose components that consume low power, such as low-power microcontrollers, low-power sensors, and efficient power management ICs.This can help reduce the power consumption of the overall system.(b) Optimize data transmission: Data transmission is a significant source of power consumption in wearable sensors.Reduce the amount of data transmitted by compressing data, aggregating data, and using lowpower wireless protocols.(c) Use energy harvesting: Energy harvesting is the process of collecting energy from the environment and converting it into an electrical energy.It can be used to power wearable sensors, such as solar panels or kinetic energy harvesters.(d) Utilize sleep modes: Put the sensor into sleep mode when it is not in use to conserve power.This can be done by turning off unnecessary components or reducing their power consumption.
(e) Implement duty cycling: Duty cycling is the technique of turning the sensor on and off at specific intervals.This can reduce power consumption by allowing the sensor to conserve power when it is not needed.(f ) Optimize software algorithms: Optimize software algorithms to reduce the number of computations required.

Importances of User Behaviour and Engagement in Maximizing the Energy Efficiency of Wearable Sensors
The importance of user behavior and engagement in maximizing the energy efficiency of wearable sensors lies in the potential benefits it brings.By actively engaging in energysaving practices, users can significantly extend battery life, reduce energy consumption, and enhance the overall usability of wearable devices.However, challenges exist, such as user awareness, adherence to energy-saving practices, and balancing user preferences with energy-efficiency requirements.Overcoming these challenges requires educating users about energy-saving techniques, providing intuitive interfaces for energy management, and incorporating feedback mechanisms to encourage energy-conscious behaviors.Additionally, leveraging data analytics and machine learning algorithms can help tailor energy-saving recommendations based on user behavior patterns, further optimizing energy efficiency in wearable sensor systems.Users can be encouraged to: (a) Wear the device consistently: One of the most critical factors in optimizing the energy efficiency of wearable sensors is to ensure that users wear the device consistently.If users only wear the device sporadically, the battery will drain quickly, and the device may not collect enough data to be useful.(b) Turn off unnecessary features: Wearable sensors often come with a range of features, some of which may not be necessary for the user's needs.Turning off unnecessary features can help to conserve battery life and extend the device's operating time.(c) Charge the device regularly: It is essential to charge the device regularly to ensure that the battery is fully charged and can operate for as long as possible.(d) Use the device in a way that conserves energy: Users can be encouraged to use the device in a way that conserves energy, such as turning off the display when not in use, using the device in low-power mode, and avoiding unnecessary data transfers.
By encouraging users to engage with the device in a way that conserves energy, the lifespan of the battery can be extended, and the energy efficiency of the wearable sensor can be maximized.This, in turn, will help to ensure that the device can provide useful data over an extended period, making it a more valuable tool for monitoring the user's health and fitness.
Journal of Sensors

Medical Wearable Sensor Industrial Trends
The landscape of medical wearable sensors is witnessing a surge in emerging industrial trends that have the potential to revolutionize health care practices (refer Figure 6).
One notable trend is the ongoing miniaturization of sensors, which enables the development of compact and unobtrusive devices that can be comfortably worn by patients.This trend not only enhances user comfort, but also facilitates continuous and long-term monitoring, allowing for more accurate and comprehensive health assessments.Here are some examples of the medical wearable sensors available in the literature and summarized in Figure 7: (a) Li et al. [105]    16 Journal of Sensors develop a flexible and stretchable thermoelectric generator that can convert body heat into an electrical energy.They integrate the generator into a wearable ECG system, enabling continuous monitoring of the user's heart activity without the need for an external power source.The self-powered ECG system demonstrates reliable performance and compatibility with daily activities.This innovative approach offers a promising solution for energy harvesting in wearable health care devices, eliminating the reliance on batteries or external power supplies.The study contributes to the advancement of self-powered wearable electronics, providing a sustainable and convenient means of monitoring vital signs for health care applications.(e) Jinno et al. [109] introduce a self-powered ultraflexible photonic skin for continuous biosignal detection using air-operation-stable polymer light-emitting diodes (PLEDs).The researchers fabricate a flexible and stretchable photonic skin by integrating PLEDs with organic photodetectors and organic thin-film transistors.This innovative design allows for the detection of various biosignals, such as pulse waves and oxygen saturation levels, without the need for an external power source.The self-powered photonic skin demonstrates exceptional performance, with high sensitivity and stability during prolonged use.This advancement in wearable technology holds significant potential for applications in health care monitoring, providing a noninvasive and comfortable solution for continuous biosignal detection.The research contributes to the development of self-powered and flexible electronics, paving the way for advanced wearable devices that can seamlessly integrate with the human body.

Conclusions and Recommendations
After a rigorous review, the following recommendations are proposed: (i) Energy solutions for wearable sensors: The paper concludes that wearable sensors hold immense potential in sports, fitness, and medicine; however, their limited battery life remains a significant challenge.To address this, a multifaceted approach is essential, combining hardware and software improvements, as well as energy harvesting and wireless power transfer techniques.
(i) Hardware improvements: The first part of the paper highlights the importance of employing low-power sensors, energy-efficient microcontrollers, and power management circuits in wearable sensors.The recommendation is to continue research and development in these areas, aiming for ultralow-power components to further extend battery life.

(i) Energy harvesting techniques:
The utilization of solar, thermal, and kinetic energy harvesting techniques is promising for powering wearable sensors.The paper recommends exploring innovative ways to  Journal of Sensors integrate these methods effectively into wearable devices.Additionally, investigating hybrid energy harvesting solutions could provide a more reliable and continuous power source.
(i) Wireless power transfer: The third part emphasizes the potential of wireless power transfer techniques, such as radio frequency and magnetic resonance, in providing continuous power to wearable sensors without the need for physical connections.Further research is encouraged to improve the efficiency and practicality of these technologies for real-world applications.
(i) Software Optimization: Software optimization plays a crucial role in reducing energy consumption.Efficient algorithms for data processing and transmission should be developed to minimize energy usage during operation.The paper recommends continuous research in software engineering to identify new ways to optimize sensor data handling.
(i) Sleep and wake-up modes: The implementation of sleep and wake-up modes is crucial for conserving power during idle periods.The paper suggests refining these modes to ensure seamless transitions between active and low-power states, ultimately prolonging battery life.
(i) User interaction and behavior: The paper underscores the significance of user interaction and behavior in improving energy efficiency.Recommendations include developing user-friendly interfaces that empower users to monitor their energy usage.Providing feedback on energy consumption could incentivize users to adopt energy-efficient habits.
In conclusion, enhancing the battery life of wearable sensors is a multifaceted task that requires a holistic approach, combining hardware improvements, energy harvesting techniques, wireless power transfer, software optimization, and user behavior considerations.The successful implementation of these recommendations could significantly extend the lifespan of wearable sensors, revolutionizing their widespread use in sports, fitness, and medical applications while enabling a more sustainable and usercentric wearable technology ecosystem.

FIGURE 2 :
FIGURE 2: Futuristic application of different wearable sensors with connectivity towards energy optimization.

FIGURE 3 :
FIGURE 3: A graphical illustration of the many uses for on-body sensors.
(a) conductivity: wearable sensors require materials that are conductive in order to detect changes in electrical signals.Conductive materials such as metals, carbon, graphene, and conductive polymers can be used for this purpose; (b) flexibility: wearable sensors need to be flexible and stretchable to conform to the body's movements without losing their sensing capabilities.Materials such as elastomers, polymers, and fabrics can provide the required flexibility; (c) biocompatibility: wearable sensors must be safe and compatible with the body to avoid irritation or injury.Biocompatible materials such as silicone, polyethylene, and polyurethane are commonly used for this purpose; (d) durability: wearable sensors must be able to withstand wear and tear from daily use, washing, and exposure to sweat and other bodily fluids.Materials such as silicone and polyurethane are known for their durability and resistance to degradation; (e) sensitivity: wearable sensors must be able to detect small changes in electrical signals to provide accurate readings.Materials with high sensitivity to electrical signals, such as carbon nanotubes and graphene, are ideal for this purpose; and (f ) cost: wearable sensors need to be affordable for widespread use.Materials that are readily available and costeffective, such as polymers and fabrics, are often preferred for wearable sensors.By considering these factors, researchers (a) Solar power: wearable sensors can be equipped with small solar panels that convert sunlight into electrical energy.This technique is ideal for outdoor activities where the device is exposed to direct sunlight.(b) Thermal energy harvesting: this technique converts the temperature difference between the body and the surrounding environment into electrical energy.The body generates heat, and the device can harvest this energy to power itself.(c) Mechanical energy harvesting: this technique converts kinetic energy, such as the movement of the body, into electrical energy.For example, when a person walks, the movement of their body can generate energy that can be used to power the device.(d) Radiofrequency (RF) harvesting: this technique captures electromagnetic waves emitted by sources such as Wi-Fi routers, mobile phones, and other electronic devices.The device can use this energy to power itself.(e) Piezoelectric harvesting: this technique converts mechanical stress into electrical energy.For instance, when a person moves, the device can harness the energy from the motion and use it to power itself.
d u c t i v e p o l y m e r s G a s s e n s o rs S t r a i n s e n s o r s S tr a in se n so rs Graphene M e ta l-o r g a n ic fr a m e w o r k s S i l v e r n a n o w i r e s C a rb o n n a n o tu b e s F le x ib le e le c tr o d e s

FIGURE 5 :
FIGURE 5: Materials for wearable sensors that are available in the literature.

8
(a) Lossless compression: lossless compression algorithms compress data without any loss of information, allowing for perfect reconstruction of the original data.Popular lossless compression techniques include run-length encoding (RLE), Huffman coding, and Lempel-Ziv-Welch (LZW) compression.These techniques exploit patterns and redundancies in the data to achieve compression.(b) Differential compression: differential compression algorithms exploit the correlation between consecutive data samples.Instead of encoding the absolute values of each sample, they encode the differences or changes between successive samples.This technique is particularly useful for sensor data that exhibits small variations over time.Differential pulse code modulation (DPCM) and delta encoding are examples of differential compression techniques.(c) Transform-based compression: transform-based compression techniques convert the data from the time domain to a transformed domain where it may exhibit greater compressibility.The most widely used transform-based technique is discrete cosine transform (DCT), which is commonly employed in image and video compression.The transformed data is quantized and encoded using entropy coding methods such as Huffman coding.(d) Prediction-based compression: prediction-based compression algorithms estimate the current data sample based on past samples and encode the difference between the predicted and actual values.These algorithms use prediction models such as linear regression, autoregressive models, or adaptive filtering to estimate the data.(e) The encoded prediction error, along with the prediction model parameters, is then transmitted or stored.Adaptive differential pulse code modulation (ADPCM) is an example of a prediction-based compression technique.(f) Sparse representation compression: sparse representation compression exploits the sparsity or the presence of a small number of significant values in the sensor data.Techniques such as sparse coding and compressed sensing leverage the sparse nature of the data to achieve compression by representing the data using a reduced set of basic functions or measurements.
properties of these sensors, which are made using flexible substrates and utilize ion-sensitive materials.The authors discuss the challenges and advancements in the field of wearable pH sensors and provide insights into their potential applications in health care and environmental monitoring.The flexible nature of these sensors allows for comfortable and unobtrusive integration into wearable devices, opening up new possibilities for real-time pH monitoring in various domains.(c) Zhao et al.[107] propose a safe and flexible selfpowered wristband system by integrating defective MnO 2-x nanosheet-based zinc-ion batteries with perovskite solar cells.The researchers fabricate the MnO 2-x nanosheets with defects to enhance the electrochemical performance of the zinc-ion batteries.They also develop efficient perovskite solar cells to harvest solar energy.The integrated wristband system demonstrates excellent performance, providing a sustainable and self-powered energy source for wearable devices.The combination of the zinc-ion batteries and perovskite solar cells offers enhanced safety and flexibility, making it a promising solution for practical applications in wearable technology.The findings of this study contribute to the development of self-powered wearable systems and pave the way for advancements in wearable energy harvesting and storage technologies.(d) Kim et al.[108] present their work on self-powered wearable electrocardiography (ECG) using a wearable thermoelectric power generator.The researchers

FIGURE 6 :
FIGURE 6: Energy harvesting techniques used in wearable sensors.

FIGURE 7 :
FIGURE 7: Low power wearable sensors and their estimated power requirements.

TABLE 1 :
Piezoelectric-harvested wearable sensors reported in the literature.