Human Motion Research Articles

Preparation of PAMgA/GL/GO antifreeze conductive hydrogel for wearable strain sensor based on physically cross-linked network

Conductive hydrogels have promising prospects in wearable strain sensor applications. However, the complex and diverse application scenarios put forward higher requirements on the conductivity, anti-freezing, tensile, self-healing, adhesion, and moisture retention properties of the conductive hydrogel. In this study, a polymagnesium acrylate/glycerol/graphene oxide (PAMgA/GL/GO) conductive hydrogel is prepared by free radical polymerization method, with magnesium acrylate (AMgA) as the polymer monomer, glycerol (GL) as the antifreeze filler, and graphene oxide (GO) as the conductive filler. Adding GL not only enhances the antifreeze properties of the composite hydrogels but also improves the moisture retention. The addition of GO increases the conductivity and the gauge factor (GF) of the composite hydrogels. When the GO content is 0.6 wt.%, the PAMgA/GL/GO conductive hydrogel demonstrates excellent properties of antifreeze, conductivity, self-healing, adhesion, and sensitivity. These excellent properties make it suitable as a wearable strain sensor for accurately monitoring human joint movements and subtle physiological signals.

High-Performance Flexible Pressure Sensor with Micropyramid Structure for Human Motion Signal Detection and Electromagnetic Interference Shielding.

Flexible pressure sensors have a wide range of applications in the field of human motion signal detection, but the electromagnetic radiation generated during the monitoring process has become an unavoidable problem. Nowadays, it is still a challenge to develop high-performance pressure sensors with excellent electromagnetic interference (EMI) shielding performance. Herein, the Ti3C2TX MXene/carbon fiber/multiwalled carbon nanotube/polydimethylsiloxane (MXene/CMP) films with a micropyramidal structure were developed by the technology of vacuum high-temperature hot pressing and spray deposition. Benefiting from the dielectric loss of the conductive fillers and the presence of the microstructure, the MXene/CMP film exhibits excellent EMI shielding performance, and the EMI shielding efficiency (SE) can reach 49.37 dB. Besides, the introduction of CF effectively improves the mechanical properties of the MXene/CMP films, and the MXene nanosheets deposited on the surface of the film can reduce stress concentration, which in turn delays the expansion of cracks. Furthermore, the MXene/CMP sensor exhibits high sensitivity (89.76 kPa-1) and fast response/recovery time (61/60 ms). The deformation of microstructure and the construction of conductive networks enable the sensor to realize the monitoring of human motion signals (such as finger bending, knee bending, wrist bending, etc.). Meanwhile, the sensor maintains sensing stability even after 2000 pressure cycles, which can be attributed to the improvement in mechanical properties. In summary, the developed high-performance flexible pressure sensor with micropyramid structure has great application prospects in wearable devices and healthcare.

An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.

Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal tract (CST) from the primary motor cortex and premotor areas, and the subcortical reticulospinal tract (RST) from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas. To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory corticospinal outputs, and reticulospinal outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the corticospinal (CS) area, reticulospinal (RS) area, or both, and the recovery of finger dexterity was analyzed. In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions. This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.

Mapping of Petanque Sports Research Trends for the period 2011-2024: A Bibliometric Analysis in the Scopus Database

Objectives. This study aimed to conduct a bibliometric analysis of the trend of petanque sports from 2011 to 2024 in the Scopus database. The research focused on productivity assessment, publication type, journal ranking, frequency of publishing, number of citations, study area focus, keywords, and co-authorship. Materials and methods. This study was carried out using a bibliometric method. The research stages comprised (1) searching for articles using the Boolean “Petanque” AND “petanque in sport”, (2) inquiry with inclusion criteria of e-books, short reports, conferences, original articles, literature reviews in English, French, and Spanish from 2011 to 2024, (3) filtering 34 articles, (4) eliminating 19 articles, leaving 15 articles that were considered worthy, (5) re-filtering and producing the same number, namely 15 articles, and (6) deciding 15 final data articles. Data collection and analysis techniques used were Mendeley, VOSviewer, and Excel. Results. In terms of productivity, 3 (20.00 %) documents were identified from 2011, 2021, and 2024; the most common type of document was original articles, with a total of 13 (86.67 %); the most journal rankings were in quartile 3 and 4 (30.00 %); the journals most frequently publishing research on petanque included the Journal of Physical Education and Sport, Ethnologie Francaise, Concurrences, and the International Journal of Human Movement and Sports Sciences, and Retos, each of which had 2 (13,00 %) documents; while the largest number of citations was from Pelana et al, with 26 citations. The majority of studies focused on physical, technical, and biomechanical aspects. The most commonly appearing keywords included performance, athlete, performance characteristic, sport, optimum group performance, and psychological training plan. Regarding collaboration, 14 researchers were found to be cooperating on the same research topic. Conclusions. Petanque sports studies still require better research productivity and innovation to improve information and knowledge.

ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for Human Activity Recognition

Human Activity Recognition (HAR) is an increasingly popular field of study aimed at automatically identifying and categorizing human movements and activities using various tracking devices and measurements, such as sensors and cameras. Smartphones have emerged as a prevalent sensor modality for HAR, offering abundant data on an individual’s movements through GPS, accelerometers, and gyroscopes. In conventional convolutional neural networks (CNNs), the artificial neurons within each feature layer typically possess identical receptive fields (RFs). This paper introduces a novel model, ASK-HAR (Attention-based Multi-Core Selective Kernel Convolution Network for HAR), which enhances HAR by performing kernel selection between multiple branches with different RFs using attention mechanisms. The selective kernel mechanism is leveraged to optimize HAR performance. Additionally, the CBAM attention module is employed for time series feature extraction and activity recognition within the overall framework. Extensive experiments conducted on benchmark HAR datasets, including UCI-HAR, USC-HAD, WISDM, PAMAP2, and DSADS, demonstrate that the ASK-HAR model consistently achieves high accuracy across all datasets.

Determining the optimum number of cycles for calculating joint coordination and its variability during running at different speeds: A timeseries analysis

Understanding the intricacies of human movement coordination and variability during running is crucial to unraveling the dynamics of locomotion, identifying potential injury mechanisms and understanding skill development. Identification of minimum number of cycles for calculation of reliable coordination and its variability could help with better test organization and efficient assessment time. By adopting a cross-sectional study design, this study investigated the minimum required cycles for calculating hip-knee, hip-ankle and knee-ankle coordination and their variability using a continuous relative phase (CRP) method. Twenty-nine healthy adults ran on a treadmill at speeds of 9, 12.5, and 16 km.h−1 while 3D kinematic data of their lower limbs were recorded using 6 optoelectronic cameras. Using Intraclass Correlation Coefficient (ICC) analysis, reliability between CRP and its variability (CRPv) in different gait cycles (3, 5, 10, 20, 30) was assessed for each speed. A minimum of 10 cycles was required for CRP calculation across all speeds, whereas CRPv necessitated a minimum of 30 cycles for moderate to good reliability. While increasing the number of cycles improved ICC values for inter-joint CRP, the same trend was not consistently observed for CRPv, emphasizing the importance of separately assessing CRP and its variability metrics.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Development of Dual-Arm Human Companion Robots That Can Dance.

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human-robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint

Research shows that treadmill shock-absorbing devices can reduce the impact of ground reaction forces on the knee and ankle joints during running. Most existing treadmills use fixed or passive shock absorption, meaning their shock-absorbing systems do not actively adjust to changes in ground reaction forces (GRFs). Methods: This study establishes a mathematical model integrating human motion biomechanics and treadmill running surfaces, analyzing the relationships between various parameters affecting the system. Ultimately, an optimal shock-absorbing treadmill control system is designed, utilizing a microcontroller as the main control unit, airbags for shock absorption, and a widely used foot pressure testing system. Objective: The goal is to more effectively prevent running injuries caused by excessive foot pressure. Compared to conventional shock absorption systems, this design features an active multilevel adjustment function with higher precision in regulation. Results: The experimental results demonstrate that the ground reaction force (GRF) generated by the optimal shock-absorbing treadmill control system is reduced by up to 10% compared to that of a conventional shock-absorbing treadmill. Conclusions: This leads to a smaller impact force on the knees due to foot pressure, resulting in better injury prevention outcomes.

Recycling primary batteries into advanced graphene flake-based multifunctional smart textiles for energy storage, strain sensing, electromagnetic interference shielding, antibacterial, and deicing applications

Batteries are extensively used owing to their pivotal role in energy storage. However, their adverse environmental impact remains a significant challenge. The prevailing technologies for treating and handling battery waste are remarkably complex and expensive, highlighting the urgent need to upcycle spent battery materials into valuable resources to mitigate their environmental footprint. To that end, this paper reports a scheme for recycling graphite rods and Zn from Zn–C batteries. In this approach, polymeric composites synthesized using graphene flakes extracted from the graphite rods of spent batteries are used to fabricate multifunctional cotton textiles, and the outer Zn case of the spent batteries is employed as a Zn anode in energy storage devices. The synthesized multifunctional fabric shows excellent energy storage performance, particularly in Zn-ion hybrid supercapacitors, achieving a specific capacitance of 140 F g−1 at a scan rate of 0.5 A g−1; an electromagnetic interference shielding efficiency of ∼48 dB; wearable sensing capabilities for human motion detection; and Joule heating behavior for deicing. Moreover, the fabric exhibits remarkable antibacterial efficiency, with an approximately 27-mm-wide inhibition zone against both Gram-positive and Gram-negative bacteria. The “waste-to-wonder” strategy presented herein holds promise for environmental protection and innovative multifunctional textile fabrication.

Fully Physically Crosslinked Hydrogel with Ultrastretchability, Transparency, and Freezing-Tolerant Properties for Strain Sensor.

Nowadays, conductive hydrogels show significant prospects as strain sensors due to their good stretchability and signal transduction abilities. However, traditional hydrogels possess poor anti-freezing performance at low temperatures owing to the large number of water molecules, which limits their application scope. To date, constructing a hydrogel-based sensor with balanced stretchability, conductivity, transparency, and anti-freezing properties via simple methods has proven challenging. Here, a fully physically crosslinked poly(hydroxyethyl acrylamide)-glycerol-sodium chloride (PHEAA-Gl-NaCl) hydrogel was obtained by polymerizing hydroxyethyl acrylamide in deionized water and then soaking it in a saturated NaCl solution of glycerol and water. The PHEAA-Gl-NaCl hydrogel had good transparency (~93%), stretchability (~1300%), and fracture stress (~287 kPa). Owing to the presence of glycerol and sodium chloride, the PHEAA-Gl-NaCl hydrogel had good anti-freezing properties and conductivity. Furthermore, the PHEAA-Gl-NaCl hydrogel-based strain sensor possessed good sensitivity and cyclic stability, enabling the detection of different human motions stably and in a wide temperature range. Based on the above characteristics, the PHEAA-Gl-NaCl hydrogel has broad application prospects in flexible electronic materials.

Supramolecular Luminescent Copper-Nanocluster-Based Dough with Excellent Electrical Conductivity Sensing Properties.

In recent years, the rapid advancement of flexible conductive materials has significantly increased the demand for dough materials that offer high flexibility and conductivity for diverse applications. Here, we developed a flexible, stretchable, and self-healing dough utilizing hydrogen-bonding interactions between glutathione-stabilized copper nanoclusters (GSH-Cu NCs) and poly(acrylic acid) (PAA). The dough materials can be kneaded, readily reshaped, and further processed to create bulk materials of arbitrary form factors. The incorporation of PAA not only preserved the vibrant blue emission of GSH-Cu NCs but also enhanced their electrical conductivity and stretchability. The dough can be stretched up to 25 times its initial length and achieves complete self-healing in a short time. Moreover, the dough can automatically repair physical damage and return to its initial conductivity levels after healing. Surprisingly, the electrical conductivity of the dough can reach as high as 2.97 S/m, which is relatively superior compared to that of conventional conductive materials. This study presents a dough that serves as a highly sensitive strain sensor, capable of effectively monitoring human movement across a broad range of strains.

Next-gen strain sensors: Self-healing, ultra-sensitive, lightweight, and durable MWCNT-silicone rubber for advanced human motion tracking

Strain sensors that have flexible wearable forms can be applied in different aspects, such as in soft robotics, human-computer interaction, motion monitoring, and medical treatment process. The main challenge is creating lightweight, flexible sensors with high sensitivity. In this study, we developed flexible strain sensors using a simple sandwich manufacturing method. To achieve this, multi-walled carbon nanotubes (MWCNTs) were chosen for their excellent conductivity, while silicone rubber (SR) provided a flexible and durable substrate. Advanced SEM-EDX and FTIR analyses were conducted to assess the material structure. Furthermore, the electromechanical performance of the sensors was thoroughly evaluated, highlighting their potential in real-world applications. Electromechanical performance evaluation showed that the SR/0.7-MWCNT/SR sensor in the 0–90 % strain range had a sensitivity of 34.47, an increase of 53.35 %, and a linearity of 0.978, an increase of 10 % compared to the SR/0.5-MWCNT/SR sensor. In addition, the sensor has extremely fast response and recovery times of 65 ms and 60 ms, respectively. The sensor is also able to withstand up to 1.200 cycles of stretching, twisting, and bending. The sensor also exhibited good autonomous electromechanical self-healing properties against physical damage. The sensor developed in the present study are proven to be used to detect human movements such as finger bending, wrist, elbow, and knee movements.

Piezoelectric-electromagnetic wearable harvester for energy harvesting and motion monitoring

This paper proposes a piezoelectric-electromagnetic wearable harvester (PEWH). The device is used to harvest the energy generated when the upper limb swings and can perform the function of motion monitoring. The main structure of PEWH consists of the piezoelectric power generation module and electromagnetic sensing module. Among them, the piezoelectric sheet in the piezoelectric module deforms and outputs electric energy, and the magnetic ball in the electromagnetic module moves to generate induced electromotive force to achieve sensing and energy supply. Through experimental testing, PEWH output performance is optimal when the spring wire diameter is 0.8 mm and the distance between the spring connector where the spring located and the position of the top of the shaft is 65 mm. At a vibration excitation frequency of 2 Hz, the device’s output voltage reaches a peak-to-peak value of 57.84Vpp, accompanied by a maximum power of 115.52mW. A range of application experiments were conducted to confirm the output performance of the device, which can power 82 LEDs and the temperature and humidity sensor. The prototype can be worn on the arm to enable monitoring of the current movement status. PEWH can effectively capture the energy generated by human movement for self-powered and self-sensing motion detection.

Balancing stretchability and conductivity: Carbon nanotube layer-enhanced non-ionic conductive hydrogels with a sandwich structure

In non-ionic conductive hydrogels, conductivity often conflicts with mechanical properties, posing a challenge in creating hydrogels that strike a balance between conductivity and mechanical properties. Here, we addressed this issue by integrating carboxylated carbon nanotube (CNT) layers in a sandwich structure onto the surface of the cationic hydrogel (P(AM-co-DMDAAC), PAD), followed by dehydration for densification. This approach simultaneously improved the conductivity of the hydrogel while maintaining its stretchability. The stability of the composite structure was ensured through electrostatic interactions, hydrogen bonding, and physical entanglement between the PAD hydrogel and the CNT layer. Additionally, adjusting the water–solid ratio of the hydrogel allows fine-tuning of its conductivity and stretchability. Remarkably, at a 500 % water–solid ratio, the hydrogel achieved a conductivity of 2.3 S/m. By further reducing the water content, the conductivity could be increased to 25 S/m, significantly higher than similar hydrogels. The PAD-CNT hydrogel found applications in swelling response, tunable resistance wires, and as strain sensors. Notably, the sandwich structure enables the PAD-CNT hydrogel to output capacitive signals, exhibiting a more sensitive response in monitoring human movement. Overall, this study introduces a novel composite structure for conductive hydrogels, holding tremendous potential in flexible conductivity and smart response fields.

Fabrication of TPU-supported Ti3C2Tx/Ag2S/TiO2 electrospun mat: Dual-functional materials for human motion and gas detection

In our work, a dual-functional sensor for detecting ammonia and human motion was realized by fabrication of TPU-Supported Ti3C2Tx/Ag2S/TiO2 electrospun mat. The synthesis of Ag2S/TiO2–doped Ti3C2Tx nanocomposites was achieved through a simple hydrothermal approach, which served as the sensing component. The findings from the microscopic morphology analysis indicate that the nanocomposites were evenly dispersed across the flexible TPU. The performance of the fabricated flexible gas-sensitive and strain sensors was evaluated, the results showed a 25.2 % response to 100 ppm ammonia at room temperature (25 ℃), which was approximately five times higher than that of the pure Ti3C2Tx gas-sensitive materials. When subjected to a strain range of 40 %–60 %, the response was measured as 378.597, exhibiting a notably high level of linearity (linear fitting value of 0.9849). The stability and repeatability of the flexible sensor make it play a significant role in room temperature gas sensing and motion monitoring.

3D Network Spacer-Embedded Flexible Iontronic Pressure Sensor Array with High Sensitivity over a Broad Sensing Range.

Microstructure construction is a common strategy for enhancing the sensitivity of flexible pressure sensors, but it typically requires complex manufacturing techniques. In this study, we develop a flexible iontronic pressure sensor (FIPS) by embedding an isolated three-dimensional network spacer (3DNS) between an ionic gel and a flexible Ti3C2Tx MXene electrode, thereby avoiding complex microstructure construction techniques. By leveraging substantial deformation of the 3DNS and the high capacitance density resulting from the electrical double layer effect, the sensor exhibits high sensitivity (87.4 kPa-1) over a broad high-pressure range (400-1000 kPa) while maintaining linearity (R2 = 0.998). Additionally, the FIPS demonstrates a rapid response time of 46 ms, a low limit of detection at 50 Pa, and excellent stability over 10 000 cycles under a high pressure of 600 kPa. As practical demonstrations, the FIPS can effectively monitor human motion such as elbow bending and assist a robotic gripper in accurately sensing gripping tasks. Moreover, a real-time, adaptive 7 × 7 sensing array system is built and can recognize both numeric and alphabetic characters. Our design philosophy can be extended for fabricating pressure sensors with high sensing performance without involving complex techniques, facilitating the applications of flexible sensors in human motion monitoring, robotic tactile sensing, and human-machine interaction.

Self-Powered Tactile Sensors Embedded with Self-Healing Electrodes for Multifunctional Applications.

Tactile-based sensing technology represents one of the most promising methods for interacting with their surrounding environment. Consequently, flexible tactile sensing has garnered significant attention from researchers worldwide. In this study, triboelectricity and piezoelectricity were combined to propose a multifunctional self-powered tactile sensor (MSPTS). Notably, new and innovative self-healing electrodes were embedded and synthesized from HPDMS (poly(dimethylsiloxane)) and boric acid in the MSPTS, MSPTS demonstrated a linear detection range of 0.25-5 kPa with a sensitivity of 246.28 mV/kPa, indicating substantial improvements in sensor sensitivity, output performance, and anti-interference capability. Our method of forming hydrogen bonds between a self-healing material (SHM) and a liquid metal (LM) effectively addressed the issue of LM leakage within the flexible matrix under pressure, thus preventing sensor failure. The borate dynamic bonding conferred self-healing properties to the electrode when it was damaged. The MSPTS was successfully applied to pressure detection, material identification, and human body movement detection, significantly broadening the application range of flexible electronic devices.

Experimental Study on Human Kinetic Energy Harvesting with Wearable Lifejackets to Assist Search and Rescue

This study explores the integration of a human kinetic energy-harvesting mechanism into lifejackets to address the energy needs of aid search and rescue operations in aquatic environments. Due to the limited data on the movement patterns of drowning individuals, a human motion model has been developed to identify optimal design parameters for energy harvesting. This model is developed from computer vision analysis of underwater footage and motion capture laboratory experiments and is used to quantify the potential for power generation. The field testing experiment is conducted underwater, replicating the environment used for footage collection and analysis for the modelling. During the field testing, the participant wears a lifejacket integrated with the energy-harvesting device. Field testing data are then collected to verify the model. The efficacy of this approach is demonstrated with observed power outputs ranging from 0 mW to 754 mW in simulations and experiments. Despite challenges such as the “dead zone” in a drowning person’s motion, the success of the experiments underscores the potential of the proposed energy-harvesting mechanism to efficiently harness the kinetic energy generated by a drowning person’s movements. This study contributes to the development of sustainable, energy-efficient solutions for search and rescue operations, particularly in remote and challenging aquatic environments.