Search Results (297)

This article presents the design of a perovskite photovoltaic (PV)-based power management system, which uses a power converter (a four-stage bootstrap charge pump) to boost the output of the solar cell and supply selectable rectified power rails to CMOS image sensor circuit blocks. A perovskite photovoltaic, also known as a perovskite solar cell (PSC) was fabricated in the laboratory. The PSC has an open-circuit voltage of 1.14 V, short-circuit current of 1.24 mA, maximum power of 0.88 mW, and a current density of 20.68 mA/cm² at 62% fill factor. These measured forward scan parameters were closely reproduced with a solar cell simulation model. In a Cadence simulation that used 180 nm CMOS process, the power converter efficiently boosts the maximum output voltage of the PSC from 0.85 V to a rectified 3.7 V. Stage modulation and level shifting enable selectable output rails in the 1.2–3.3 V range to supply the image sensor circuit blocks. Keeping the output capacitance of the power converter much larger than the flying capacitance reduces the ripple voltage to approximately 73 µV, much smaller than the typical 1 mV in several other literatures. Through simulation, this work demonstrates the concept of directly using PSC (to be implemented on an outer ‘packaging’, not on a die) to supply CMOS image sensor power rails, in the same sense as in wearable devices and other consumer devices. This work highlights a path toward self-powered image sensors with improved conversion efficiency, compactness, and adaptability in low-light and variable operating environments. Full article

Background: Wearable EEG devices offer an accessible and unobtrusive system for regular brain health monitoring outside clinical settings. However, due to the current lack of data available from wearable low-density EEG devices, we need to anticipate the extraction of biomarkers for brain health evaluation from available clinical datasets. Methods: This study evaluates multiclass dementia classification of Alzheimer’s disease, frontotemporal dementia, and healthy controls using features derived from low-density temporal EEG electrodes as a proxy for wearable EEG setups. The feature set comprises power-based metrics, including the 1/f spectral slope, and complexity metrics such as Hjorth parameters and multiscale sample entropy. Results: Our results show that multiclass differentiation of dementia, using low-density electrode configurations restricted to temporal regions, can achieve results comparable to a full-scalp configuration. Notably, electrode T5, positioned over the left temporo-posterior region, consistently outperformed other configurations, achieving a subject-level accuracy of 83.3% and an F1 score of 82.4%. Conclusions: These findings highlight the potential of single-site EEG measurement for wearable brain health devices. Full article

The development of Micro-electro-magnetic Vibration Energy Harvesters (MEMVEHs) plays a crucial role in advancing self-powered nanophotonic, nanoelectronic, and nanosensor systems. As energy autonomy becomes critical for miniaturized devices, MEMVEHs offer a sustainable power source for low-power nanodevices operating in wireless sensor networks, wearable electronics, and biomedical implants. This study provides a comparative assessment of MEMVEH technologies and evaluates their integration potential within next-generation nanoscale systems, enabling enhanced performance, longevity, and energy efficiency of emerging nanotechnologies. Electromagnetic vibration energy harvesters (EMEHs) based on microelectromechanical system (MEMS) technology are promising solutions for powering small-scale, autonomous electronic devices. In this study, two electromagnetic vibration energy harvesters based on microelectromechanical (MEMS) technology are presented. Two models with distinct vibration structures were designed and fabricated. A permanent magnet is connected to a silicon vibration structure (resonator) and a tiny wire-wound coil as part of the energy harvester. The coil has a total volume of roughly 0.8 cm³. Two energy harvesters with various resonators are tested and compared. Model A’s maximum load voltage is 163 mV, whereas Model B’s is 208 mV. A maximum load power of 59.52 μW was produced by Model A at 347 Hz across a 405 Ω load. At 311.4 Hz, Model B produced a maximum load power of 149.13 μW while accelerating by 0.4 g. Model B features a larger working bandwidth and a higher output voltage than Model A. Model B performs better than Model A in comparable experimental settings. Simple study revealed that Model B’s electromagnetic energy harvesting produced superior outcomes. Additionally, it indicates that a nonlinear spring may be able to raise the output voltage and widen the frequency bandwidth. Full article

The global increase in dementia cases, which is predicted to exceed 152 million by 2050, poses substantial challenges to healthcare systems and caregiving structures. Concurrently, the expansion of mobile health (mHealth) technologies offers scalable, cost-effective opportunities for dementia care. This study systematically reviews 100 publicly available dementia-related mobile applications on the Apple App Store (iOS) and the Google Play Store (Android), categorised using the Mobile App Rating Scale (MARS), as well as the targeted end-users, Internet of Things (IoT) integration, data protection, and cost burden. Applications were evaluated for their utility in cognitive training, memory support, carer education, clinical decision-making, and emotional well-being. Findings indicate a predominance of carer resources and support tools, while clinically integrated platforms, cognitive assessments, and adaptive memory aids remain underrepresented. Most apps lack empirical validation, inclusive design, and integration with electronic health records, raising ethical concerns around data privacy, transparency, and informed consent. In parallel, the study identifies promising pathways for energy-optimised IoT systems, Artificial Intelligence (AI), and Ambient Assisted Living (AAL) technologies in fostering dementia-friendly, sustainable environments. Key gaps include limited use of low-power wearables, energy-efficient sensors, and smart infrastructure tailored to therapeutic needs. Application domains such as cognitive training (19 apps) and carer resources (28 apps) show early potential, while emerging innovations in neuroadaptive architecture and emotional computing remain underexplored. The findings emphasize the need for co-designed, evidence-based digital solutions that align with the evolving needs of people with dementia, carers, and clinicians. Future innovations must integrate sustainability principles, promote interoperability, and support global aging populations through ecologically responsible, person-centred dementia care ecosystems. Full article

Zero-heat-flux (ZHF) thermometry is a clinically validated method for non-invasive core body temperature monitoring, yet its broad adoption in wearable applications is constrained by the high power consumption of the heater element. In this study, we numerically investigate the role of microporous insulation in minimizing energy demand while preserving measurement accuracy. A three-dimensional finite element model of a ZHF probe was implemented in COMSOL Multiphysics 5.4, consisting of a resistive heater, a microporous insulation shell, and a skin-equivalent substrate regulated by proportional–integral–derivative (PID) control. A Taguchi L9 orthogonal array was utilized to systematically investigate the effects of porosity (0–90%), insulation thickness (2–4 mm), and the convective heat transfer coefficient (5–15 W/m²·K) on the thermal performance of the ZHF thermometry system. Two performance metrics—heater energy consumption and settling time—were analyzed using analysis of variance (ANOVA). The results indicated that porosity accounted for more than 95% of the variance in heater power and over 80% of the variance in settling time. The configuration with φ = 90% and t = 3 mm demonstrated a balanced trade-off between energy efficiency and transient response for low-power ZHF thermometry. These findings provide design insights for energy-efficient wearable temperature sensors. Full article

The development of wearable devices continues to be a growing trend. The mobile health wearables market is extremely fast-moving, with wearable ECG designs demanding increasingly complex features from manufacturers, such as size reduction, high accuracy, low weight, power efficiency, and good signal quality. The AD823X integrated circuits for ECG miniaturization, as an analog front-end (AFE), provide an amplified and filtered analog signal for subsequent digitization. The aim of this work is the development of expansion boards for portable ECG applications based on the AD823X microchip and the Arduino platform. This study includes three different circuit designs for specific ECG applications: cardiac monitor, ECG fitness, and Holter monitor. It also presents designs using both AD823X integrated circuits. After performing tests with analog stage, the Atmega328 microcontroller was used for the analog-to-digital conversion of the ECG signals, and a miniaturized custom breakout board was developed for each ECG application, incorporating a CSR BC417143 chip for Bluetooth connectivity. The digitized signals can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. Other performed tests included measuring the noise induced during the analog-to-digital conversion stage of the Atmega328 microcontroller. This work evaluated, compared, and determined the best of the applications proposed by the manufacturer of the AD8232X for a wearable ECG monitor, addressing the current needs of the devices and emerging trends in mobile health. Full article

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with a higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for the real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force-Sensing Resistor (FSR) attached to a Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to its adequate throughput, high accessibility, and the possibility of power consumption reduction. In addition to the sensing mechanism, the device includes a compact, energy-efficient micro-controller and a three-axis accelerometer that captures body movement. Power is supplied by a rechargeable Lithium-ion Polymer (LiPo) battery, and energy usage is optimized using a buck converter. For comfort and usability, the enclosure was 3D printed using Stereolithography (SLA) technology to ensure a smooth, ergonomic shape. This setup allows the device to operate reliably over long periods without disturbing the user. Altogether, the design supports continuous respiratory tracking in both clinical and home settings, offering a practical, low-power, and portable solution. Full article

Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful health benefits. Accurate, real-time monitoring of cardio-exercises is essential to ensure that these workouts meet recommended intensity and rest guidelines. This paper proposes a Tiny Machine Learning (TinyML) wearable system that tracks the duration and type of common cardio-exercises in real time. A compact device containing a six-axis inertial measurement unit (IMU) is worn on the arm. The device streams accelerometer data to an on-device neural network model, which classifies exercises such as jumping jacks, squat jumps and jogging in place and resting states. The TinyML model is trained with labelled motion data and deployed on a microcontroller using quantization to meet memory and latency constraints. Preliminary tests with ten participants show that the system correctly recognizes the targeted exercises with around 95% accuracy and an average F1 score of 0.93 while maintaining inference latency below 100 ms and a memory footprint under 60 KB. By prompting users to alternate 30–60 s of high-intensity exercise with rest periods, the device can structure effective interval routines. This work demonstrates how TinyML can enable low-cost, low-power wearables for personalized cardiovascular exercise monitoring. Full article

With the global ageing population and the increasing prevalence of mobility impairments, the demand for effective and comfortable rehabilitation and assistive solutions has grown rapidly. Soft exoskeletons have emerged as a key direction in the development of wearable rehabilitation devices. This review examines how these systems are designed and controlled, as well as how they differ from the rigid exoskeletons that preceded them. Made from flexible fabrics and lightweight components, soft exoskeletons use pneumatic or cable mechanisms to support movement while keeping close contact with the body. Their compliant structure helps to reduce joint stress and makes them more comfortable for long periods of use. The discussion in this paper covers recent work on lower-limb designs, focusing on actuation, power transmission, and human–robot coordination. It also considers the main technical barriers that remain, such as power supply limits, the wear and fatigue of soft materials, and the challenge of achieving accurate tracking performance, low latency, and resilience to external disturbances. Studies reviewed here show that these systems help users regain functionality and improve rehabilitation, while also easing caregivers’ workload. The paper ends by outlining several priorities for future development: lighter mechanical layouts, better energy systems, and adaptive control methods that make soft exoskeletons more practical for everyday use as well as clinical therapy. Full article

At present, energy harvesting technologies are gradually replacing batteries and have become a research hotspot as power sources for low-power components in wearable electronic devices. To collect and utilize the energy generated by skiers during the process of pushing against the skis, a piezoelectric energy harvesting system (PEHS) for self-powered ski boots was proposed and designed to supply power for low-power wearable devices. The output voltage of the PEHS was modeled and simulated using the finite element method, and the causes of the simulation results were analyzed. An energy harvesting experiment of the prototype was conducted under loading conditions using a universal testing machine. Under a uniform sinusoidal load of 800 N at 1 Hz, the prototype of the PEHS for self-powered ski boots achieved a maximum output power of 57.44 mW with an optimal matching load resistance of 404 kΩ. A skiing tester wearing the self-powered ski boots conducted real-motion experiments, performing three different actions: (1) alternating single-foot stepping for propulsion, (2) alternating left and right ski edge stepping for propulsion, and (3) alternating forefoot and heel stepping for propulsion. The instantaneous peak voltages measured in these tests were statistically analyzed, and the corresponding peak power values were calculated through theoretical computation to be 6.48 ± 0.27 mW, 4.47 ± 0.21 mW, and 13.21 ± 0.48 mW for the three actions, respectively (expressed with a 95% confidence interval). Full article

Monitoring electrocardiogram (ECG) and respiration continuously and non-invasively is essential for managing cardiopulmonary health. An effective wearable device can be used to regularly monitor key vitals, reducing the need for clinical visits. In this work, we propose a custom device for real-time continuous ECG by inkjet printed (IJP) dry electrodes and respiration monitoring by using a novel single 6-axis inertial measurement unit (IMU). The proposed system can extract the heart rate (HR) and respiration rate (RR) during static and dynamic postures. The respiration process implements a quaternion-based update and multiple filtering stages to estimate the signal. The custom device uses Bluetooth protocol to send the raw and processed data to a mobile application. The RR is investigated in stationary, i.e., sitting and standing, and dynamic, i.e., walking, running, and cycling, postures. The proposed device is evaluated with commercial Go Direct^® respiration belt from Vernier^® for RR and offers an overall accuracy of 99.3% and 98.6% for static and dynamic conditions, respectively. The wearable also offers 98.9% and 97.9% accuracy for HR measurements, respectively, in static and active postures when compared with the Kardia^® device. Furthermore, the device is assessed in an ambulatory monitoring setup in both indoor and outdoor environments. The low-power wearable consumes an average of only 7.4 mA of current during data processing. The device performs effectively and efficiently in both stationary and active states, offering a low complexity, portable solution for real-time monitoring. The proposed system can benefit from the continuous monitoring and early detection of pulmonary and cardio-respiratory health issues. Full article

This work presents the development and experimental validation of a low-cost, piezoelectret-based energy harvesting system integrated into a custom insole, as a promising alternative for future self-powered wearable electronics. The design utilizes eight thermoformed Teflon piezoelectrets, strategically positioned in high-impact regions (heel and forefoot), to convert footstep-induced mechanical motion into electrical energy. The sensors, fabricated using Fluorinated Ethylene Propylene (FEP) and Polytetrafluoroethylene (PTFE) layers via thermal pressing and aluminum sputtering, were connected in parallel to enhance signal consistency and robustness. A solenoid-actuated mechanical test rig was developed to simulate human gait under controlled conditions. The system consistently produced voltage pulses with peaks up to 13 V and durations exceeding ms, even under limited-force loading (10 kgf). Signal analysis confirmed repeatable waveform characteristics, and a Delon voltage multiplier enabled partial conversion into usable DC output. While not yet optimized for maximum efficiency, the proposed setup demonstrates the feasibility of using piezoelectrets for energy harvesting. Its simplicity, scalability, and low cost support its potential for future integration in applications such as fitness tracking, health monitoring, and GPS ultimately contributing to the development of autonomous, self-powered smart footwear systems. It is important to emphasize that the present study is a proof-of-concept validated exclusively under controlled laboratory conditions using a mechanical gait simulator. Future work will address real-time insole application tests with human participants. Full article

Triboelectric nanogenerators (TENGs) have emerged as a promising technology for harvesting mechanical energy via contact electrification (CE) at diverse interfaces, including solid–liquid, liquid–liquid, and gas–liquid phases. This review systematically explores fluid-based TENGs (Flu-TENGs), introducing a foundational and novel classification framework based on direct versus indirect charge transfer to the charge-collecting electrode (CCE). This framework addresses a critical gap by providing the first unified analysis of charge transfer mechanisms across all major fluid interfaces, establishing a clear design principle for future device engineering. We comprehensively compare the underlying mechanisms and performance outcomes, revealing that direct charge transfer consistently delivers superior energy conversion—with specific studies achieving up to 11-fold higher current and 8.8-fold higher voltage in solid–liquid TENGs (SL-TENGs), 60-fold current and 3-fold voltage gains in liquid–liquid TENGs (LL-TENGs), and 34-fold current and 10-fold voltage enhancements in gas–liquid TENGs (GL-TENGs). Indirect mechanisms, relying on electrostatic induction, provide stable Alternating Current (AC) output ideal for low-power, long-term applications such as environmental sensors and wearable bioelectronics, while direct mechanisms enable high-efficiency Direct Current (DC) output suitable for energy-intensive systems including soft actuators and biomedical micro-pumps. This review highlights a paradigm shift in Flu-TENG design, where the deliberate selection of charge transfer pathways based on this framework can optimize energy harvesting and device performance across a broad spectrum of next-generation sensing, actuation, and micro-power systems. By bridging fundamental charge dynamics with application-driven engineering, this work provides actionable insights for advancing sustainable energy solutions and expanding the practical impact of TENG technology. Full article

Wearable devices can be developed using hardware platforms such as Application Specific Integrated Circuits (ASICs), Graphics Processing Units (GPUs), Digital Signal Processors (DSPs), Micro controller Units (MCUs), or Field Programmable Gate Arrays (FPGAs), each with distinct advantages and limitations. ASICs offer high efficiency but lack flexibility. GPUs excel in parallel processing but consume significant power. DSPs are optimized for signal processing but are limited in versatility. CPUs provide low power consumption but lack computational power. FPGAs are highly flexible, enabling powerful parallel processing at lower energy costs than GPUs but with higher resource demands than ASICs. The combined use of FPGAs and CPUs balances power efficiency and computational capability, making it ideal for wearable systems requiring complex algorithms in far-edge computing, where data processing occurs onboard the device. This approach promotes green electronics, extending battery life and reducing user inconvenience. The primary goal of this work was to develop a versatile framework, similar to existing software development frameworks, but specifically tailored for mixed FPGA/MCU platforms. The framework was validated through a real-world use case, demonstrating significant improvements in execution speed and power consumption. These results confirm its effectiveness in developing green and smart wearable systems. Full article

Nanogenerators (NGs), especially triboelectric nanogenerators (TENGs), represent an emerging technology with great potential for self-powered lumbar motion monitoring. Conventional wearable systems for assessing spinal kinematics are often limited by their reliance on external power supplies, hindering long-term and real-time clinical applications. NGs can convert biomechanical energy from lumbar motion into electrical energy, providing both sensing and power-generation capabilities in a single platform. This review summarizes the fundamental working mechanisms, device architectures, and current progress of NG-based motion monitoring technologies, with a particular focus on their applications in lumbar spine research and clinical rehabilitation. By enabling high-sensitivity, continuous, and battery-free monitoring, NG-based systems may enhance the diagnosis and management of low back pain (LBP) and postoperative recovery assessment. Furthermore, the integration of NGs with wearable electronics, the Internet of Things (IoT), and artificial intelligence (AI) holds promise for developing intelligent, self-sustaining monitoring platforms that bridge biomedical engineering and spine medicine. Full article