Human Motion Activity Research Articles

Self‐Powered and 3D Printable Soft Sensor for Human Health Monitoring, Object Recognition, and Contactless Hand Gesture Recognition

AbstractA new galvanic cell design of a self‐powered and 3D‐printable soft sensor showing health monitoring, object recognition, and contactless hand gesture recognition, is reported. The soft sensor consists of a 3D‐printed poly(acrylic acid) (PAA) hydrogel electrolyte layer, a soft anode layer, and a soft cathode layer. The anode layer is a 3D‐printed and Cu2+ cross‐linked poly(N,N‐dimethylacrylamide‐co‐3‐alanine‐2‐hydroxypropylmethacrylate) (PDA) hydrogel dispersed with Cu metal particles (PDA/Cu2+/Cu hydrogel), while the cathode layer is a bottom thin layer of the PAA hydrogel containing MnO2 (PAA/MnO2). Using graphite films as the soft electrodes, the soft sensor is finally assembled. The soft sensor has high force and temperature sensitivities. It gives different electric current responses under stretching, bending, pressing, and impact loading. The soft sensor is demonstrated to be useful in detecting human motion and physiological activities, e.g., breath. Based on the force and contactless temperature sensitivities, the soft sensor is used to recognize human hand gestures and plastic balls with different diameters. This 3D printable soft sensor with self‐powering, contactless motion capturing, and multi‐pimulus sensing capabilities illustrates a new pathway to make soft sensory devices for healthcare and human‐machine interaction applications.

The effectiveness of core stabilization and schroth exercise on pain reduction in adolescents with scoliosis

Background: Disorders of the spine, such as scoliosis, kyposis, and lordosis, are often complained about by many people. Currently, disorders of the spine are not only limited to adults and the elderly. They can also affect children and adolescents, where the prevalence of adolescent idiopathic scoliosis in the world ranges from 0.47% to 5.2%. The spine has a crucial role in human movement activities, playing a role as a support and enforcer of the body. In this regard, appropriate intervention for people with scoliosis is very important to be carried out. Objective: To find out how effective core stabilization and schroth exercise are at reducing pain in scoliosis in adolescents aged 13-15 years. Research Method: The type of research used is quasi-experimental research with a pre-test and post test two-group design. Results: After 4 weeks of intervention with a frequency of two times a week, there was a significant reduction in scoliosis pain in adolescents. Both models of exercise core stabilization and schroth exercise – are equally effective. Suggestion: The researcher is expected to expand this research by increasing the number of samples and extending the duration of the study.

Human motion activity recognition and pattern analysis using compressed deep neural networks

ABSTRACT This work presents an on-device machine learning model with the ability to identify different mobility gestures called human activity recognition (HAR), which includes running, walking, squatting, jumping, and others. The data is collected through an Arduino Nano 33 BLE Sense board with a sampling rate of 119 Hz, which is embedded with an Inertial Measurement Unit (IMU) sensor. The same board is used as a microcontroller to identify human gestures by developing an end-to-end edge computing application. A deep neural network model is trained and then compressed for deployment on the board to create a self-contained, embedded device capable of identifying the type of gesture performed. Three deep learning models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network – Long Short Term Memory (CNN-LSTM) & CNN-Gated Recurrent Unit (CNN-GRU), are evaluated in the identification of the mobility gestures. The observed accuracy of the models is 96%, 97.1% and 97.8%, respectively, MLP, CNN-LSTM & CNN-GRU across the different gesture categories. The study shows the utility of embedded devices with deep neural network-based models, which can provide low cost, minimal power usage, and meet data privacy requirements in HAR.

Bacterial cellulose nanofiber-reinforced PVA conductive organohydrogel for flexible strain sensors with high sensitivity and durability

Flexible strain sensors based on conductive organohydrogels have aroused wide range of interests owing to their potential utilization in human health and motion detecting. However, the mechanical performance and sensitivity of the organohydrogel sensors need to be further optimized. Herein, we have designed a dual-network organohydrogel utilizing poly(vinyl alcohol) (PVA) as raw materials, bacterial cellulose (BC) nanofibers as reinforcements, tannic acid (TA) as crosslinkers and multiwall carbon nanotubes (CNTs) as conducting fillers. The constructed C-PVA/BC/CNT organohydrogel shows good mechanical properties and high sensitivity. The resistance of the C-PVA/BC/CNT organohydrogel can be remarkably changed during deformation, demonstrating superior synchronicity between mechanical stimulus and electric signals, and thus can be used as piezoresistive strain sensors. In addition, the C-PVA/BC/CNT organohydrogel also exhibits favorable durability under different strains, and excellent cycling stability during loading/unloading processes. This C-PVA/BC/CNT organohydrogel can be used to monitor human motion and physiological activities, and has great potential applications in wearable electronic devices.

Investigating Optimal Smartphone Placement for Identifying Stairs Movement using Machine Learning

The identification of gait activities such as stair ascending and descending poses a significant challenge due to the proximity of data provided by the sensory pathway. Accurate identification of gait activities is crucial in conveying essential gait information to users for the recognition of human movement activities. However, gait patterns can vary significantly between individuals, making it challenging to develop a generalized algorithm for identifying incline surface gait activity. Factors such as walking speed, stride length, and body mechanics can all influence gait patterns, making it difficult to establish a consistent framework. Despite various research on gait event detection for level ground walking, the identification of gait activities on an inclined surface such as stairs, especially using smartphones as sensors, is currently lacking. The goal of this study is to investigate and develop a reliable and accurate method for detecting gait activities on an inclined surface such as stairs using smartphones as the sensing device. Specifically, this study focuses on investigating optimal placement of smartphones to extract tri- axis accelerometer data from the inertial sensors during stair movement. The inertial sensor data was collected from the smartphone at two different positions and two different orientations. The data was trained against 6 machine learning algorithms namely Decision Tree, Logistic Regression, Naive Bayes, Random Forest, Neural Networks and KNN. It was observed that, by using Decision Tree and Random Forest algorithm 100% accuracy was achieved, when smartphone was placed at the thigh during stair movement. Successful identification of stair movement activity by using a smartphone can significantly contribute to future research and could also prove useful to the wider community such as amputees and those with pathological gait. In addition, since smartphones are available to a wide group of people, a low-cost solution for gait activity identification can be realized, without requiring the use of external sensors and circuitry.

Scapula manipulation on frozen shoulder condition: a case study

Background: Frozen shoulder or adhesive capsulitis is a stiffness in the shoulder joint, namely the thickening of the shoulder joint capsule tissue, resulting in pain and limited shoulder joint movement. In Indonesia, 2-11% of frozen shoulder sufferers have diabetes. Complaints in the shoulder also often accompany human movement activities due to gaps in body functions, including a frozen shoulder.
 Case description: This was a case study of a patient aged 56 years with a diagnosis of frozen shoulder; this case was taken at Dr. Harjono Ponorogo Hospital, held from March 3, 2023, to March 23, 2023. The physiotherapy problems encountered in this case included pain, limited joint motion, weakness in muscle strength, and limited functional activity.
 Conclusion: At three meetings, the patient was given intervention in the form of scapular mobilization, which decreased pain and increased the range of motion on the shoulder. A decrease in limitations can also be seen from the measurements used by the shoulder pain and disability index (SPADI), but in the SPADI assessment, there were no significant changes.

Inside Front Cover

This cover image demonstates an artificial intelligence‐enabled piezo‐yarn device developed by Kim et al. for wearable intelligent sensing. It utilizes BaTiO3‐doped P(VDF‐TrFE) fibers, yielding remarkable shape adaptability, enhanced piezoelectric performance, and mechanical robustness. The AI yarn device enables real‐time monitoring and classification of human motion activities, achieving 99.6% accuracy using a convolutional neural network with multi‐kernel algorithms. image

High‐performance piezoelectric yarns for artificial intelligence‐enabled wearable sensing and classification

AbstractPiezoelectric polymer fibers offer a fundamental element in intelligent fabrics with their shape adaptability and energy‐conversion capability for wearable activity and health monitoring applications. Nonetheless, realizing high‐performance smart polymer fibers faces a technical challenge due to the relatively low piezoelectric performance. Here, we demonstrate high‐performance piezoelectric yarns simultaneously equipped with structural robustness and mechanical flexibility. The key to substantially enhanced piezoelectric performance is promoting the electroactive β‐phase formation during electrospinning via adding an adequate amount of barium titanate (BaTiO3) nanoparticles into the poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)). When transformed into a yarn structure by twisting the electrospun mats, the BaTiO3‐doped P(VDF‐TrFE) fibers become mechanically strengthened with significantly improved elastic modulus and ductility. Owing to the tailored convolution neural network algorithms architected for classification, the as‐developed BaTiO3‐doped piezo‐yarn device woven into a cotton fabric exhibits monitoring and identifying capabilities for body signals during seven human motion activities with a high accuracy of 99.6%.image

Hydroxypropyl methyl cellulose reinforced conducting polymer hydrogels with ultra-stretchability and low hysteresis as highly sensitive strain sensors for wearable health monitoring

Conducting polymer hydrogels have emerged as promising materials to fabricate highly sensitive strain sensors. However, due to weak bindings between conducting polymer and gel network, they usually suffer from limited stretchability and large hysteresis, failing to achieve wide-range strain sensing. Herein, we combine hydroxypropyl methyl cellulose (HPMC), poly (3,4-ethylenedioxythiophene):poly (styrene sulfonic acid) (PEDOT: PSS) with chemically cross-linked polyacrylamide (PAM) to prepare a conducting polymer hydrogel for strain sensors. Owing to abundant hydrogen bonds between HPMC, PEDOT:PSS and PAM chains, this conducting polymer hydrogel exhibits high tensile strength (166 kPa), ultra-stretchability (>1600 %) and low hysteresis (<10 % at 1000 % cyclic tensile strain). The resultant hydrogel strain sensor shows ultra-high sensitivity, wide strain sensing ranges of 2–1600 %, and excellent durability and reproducibility. Finally, this strain sensor can be used as wearable sensor to monitor vigorous human movement and fine physiological activity, and services as bioelectrodes for electrocardiograph and electromyography monitoring. This work provides new horizons to design conducting polymer hydrogels for advanced sensing devices.

The Enforcement of International Human Rights Law: Challenges and Solutions

In recent decades, the rise of human rights principles has attracted the attention of governments, organizations, and scholars worldwide. The expanding institutionalization of human rights has led to the formation of various international human rights laws, ranging from binding treaties to nonbinding declarations at both regional and international levels. However, existing scholarship has demonstrated that all international human rights laws, regardless of their form, have limited capacity for enforcement. Using a systematic review method based on existing literature, governmental articles, and non-governmental organization declarations and publications, this paper determines three primary challenges to the enforcement of international human rights laws: (1) inconsistency between states' agreements and practices, which means symbolic ratification by rights-violating governments; (2) professionalization of human rights movement activities, which may diminish the influence of social movements and result in less commitment and impacts; (3) overextension of movement objectives, which provoke adverse reactions against human rights principles. This paper concludes by providing constructive advice on the implementation of international human rights laws.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data.

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

State of the art of sports spirituality in metabolic processes and immunity: a systematic review

Introduction: Religion, spirituality, and sport are increasingly popular disciplines in the sport. As a historical memorial to the Greek Olympic Games, human movement activities were part of religious cults and rites in ancient societies. The modern athlete normally follows his or her religious tradition privately. Thus, the practice of sports activities linked to spirituality/religiosity (S/R) has acquired increasing importance, becoming an important cultural paradigm. Objective: To carry out a systematic review on the main considerations of spirituality and religiosity in sport, showing the state of the art. Methods: The present study followed a concise systematic review model, following the rules of the systematic review – PRISMA. The search strategy was carried out in PubMed, Cochrane Library, and Scopus databases, in addition to Google Scholar. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 156 articles were found, of which 76 articles were fully evaluated and 10 were included and evaluated in the present study to compose the systematic review. Based on the literary findings, spirituality/religiosity plays a vital role in improving sports performance and contributes to the well-being of athletes. In addition, spirituality/religiosity practices represent a driving force in the sport, helping athletes to deal with the uncertainties that can make them stressed/anxious, making them more motivated, confident, and responsible to play to the best of their ability. capacity as they perceive their athletic talent as a gift from God, thus reducing the pressure placed on them to cope with the competitive environment, better coping with sports injuries, and building trust between coaches and athletes.

Reverse Electrowetting-on-Dielectric Energy Harvesting Using 3-D Printed Flexible Electrodes for Self-Powered Wearable Sensors

Reverse electrowetting-on-dielectric (REWOD) presents a unique low-frequency motion energy harvesting method to self-power wearable sensors for human health monitoring in real-time. However, previous studies in REWOD energy harvesting have mainly focused on enhancing power density using rigid electrodes, which due to their rigidity, are not ideal to be implemented as wearable energy harvesting or motion sensing applications from human motion activities. To be considered as the next generation self-powered wearable sensors, self-powered wearable sensors should operate continuously and sustainably in any kind of electrode bending motion and without an external power source. Herein, we demonstrate ac current generation by repeatedly modulating a liquid droplet of microliter volume on an elastomer-based three-dimensional printed flexible electrode without any external bias source. Flexible electrodes were fabricated by deposition of ∼200 nm of titanium (Ti) and an additional ∼200 nm of aluminum oxide (Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) along with ∼50 nm of chromium (Cr) as an interlayer between the metal and the dielectric layers, and all of these on a flexible polydimethylsiloxane substrate. Using a 50 μL electrolyte droplet and 2.5 mm of electrode displacement at a low-frequency motion of 1–2 Hz, a maximum peak-to-peak ac current of ∼340 nA was measured. Although significant progress has yet to be made toward implementing REWOD energy harvesting as a reliable source of power for wearable self-powered systems, this letter signifies the proof-of-concept in implementing flexible electrodes in REWOD energy harvesting.

Human Activity Classification Using On-Body Miniaturized Antennas.

EM waves are extremely powerful when it comes to propagation of information during communication. There is no alternative to EM waves in such applications. However, the use of EM waves or antennas in general has not been explored fully as sensors for measuring the change in physical environment. This particular aspect has been exploited and the present work focuses on the application of antennas into the domain of classification. We propose accurate human activity classification (HAC) using on body miniaturized antennas. A simple patch antenna has been designed in order to be employed on human body for HAC. The antenna has been tested with respect to its Specific Absorption Ratio (SAR) values in order to make it body-mountable. The antenna has been fabricated and tested on human body while performing daily activities. The reflection co-efficient of antennas is a function of human motion activities and this principle is exploited to achieve the desired results. To explore different approaches, a miniaturized circularly polarized antenna is then designed and tested. The results of the two antennas are then compared. Dynamic Time Warping (DTW) algorithm has been used for the analysis of the Reflection co-efficient of the antenna. Excellent activity classification performance has been obtained using both the antennas, the miniaturized antenna giving better results.

Scenario-Based Sensed Human Motion Editing and Validation Through the Motion-Sphere

Synthesizing realistic human motion data using a real-time motion capture system in a controlled environment is a critical challenge. In addition, effectively manipulating the existing motion data is another primary concern and using such modified data in human motion analysis and activity recognition systems are prone to errors. This paper presents a simplistic and comprehensive system to effortlessly author, edit, and validate human motion data. The system enables a naive user to edit the existing motion data interactively using a humanoid model in a three-dimensional space, based on user-defined scenarios and synthesize numerous variations of the motion sequences. A modular concept of scenario-based sensed unit motion editing has been adopted to demonstrate the proposed system. We employed an efficient analytical kinematic and constraint solver to enforce the inherent body joint limits and external constraints while editing to synthesize complete and meaningful motion sequences. Furthermore, we substantiated the proposed sensed unit motion editing framework through a visual validation study using an open-source intuitive visualization tool called the Motion-Sphere. Finally, we compared the resultant synthesized motion against the real-time motion capture system data to verify the body segments’ orientation and position accuracy deviations.

A Dataset of Human Motion and Muscular Activities in Manual Material Handling Tasks for Biomechanical and Ergonomic Analyses

Manual Material Handling (MMH) activities represent a large portion of the workers’ tasks in the tertiary sector. The ability to monitor, model, and predict human behaviours are crucial to both the design of productive human-robot collaboration and an efficient physical exposure assessment system that can prevent Work-related Musculoskeletal Disorders (WMSDs), with the ultimate goal of improving workers’ quality of life. The combined use of wearable sensors and machine learning (ML) techniques can fulfil these purposes. Inertial Measurement Units (IMUs) and surface Electromyography (sEMG) allow collecting kinematic data and muscular activity information that can be used for biomechanical analyses, ergonomic risk assessment, and as input of ML algorithms aimed at joint torque/load estimation, and Human Activity Recognition (HAR). The latter needs a large amount of annotated training samples, and the use of publicly available datasets is the way forward. Nowadays, the majority of them concern Activities of Daily Life (ADLs) and, including only kinematic data, have limited applications. This paper presents a fully labelled dataset of working activities that include full-body kinematics from 17 IMUs and upper limbs sEMG data from 16 channels. Fourteen subjects participated in the experiment performed in laboratory settings for overall 18.6 hours of recordings. The activities are divided into two sets. The first includes lifting, lowering, and carrying objects, MMH activities suitable for ergonomic risk assessment, and HAR. The second includes isokinetic arm movements, mainly targeting load and joint torque estimation.

Reflections on Research in Kinesiology

This paper provides reflections on the progress to date and current status of research in kinesiology. The accompanying overview articles in this special issue of Kinesiology Review show that the contemporary disciplinary/professional foci of kinesiology remain, by and large, the same as the initial research and teaching structures of 50 years ago, as outlined in the inaugural overviews. Nevertheless, within this prevailing disciplinary/professional structure, there have been many new developments in movement-related research, including the juxtaposition of novel alignments and integrations of certain specializations of kinesiology. There is general consensus that the quality and quantity of research in kinesiology have advanced substantially, albeit unevenly, on multiple fronts, both within and between the areas of specialization. The research agenda in kinesiology has benefitted from the growing realization of the centrality of human movement and physical activity in contributing to a healthy lifestyle for individuals and societies.

Environmental impact of COVID-19 led lockdown: A satellite data-based assessment of air quality in Indian megacities

The strategies to contain the spread of COVID-19 pandemic, including restricted human movement and economic activities, have shown positive impacts on the environment. Present research analysed the effects of COVID-19 led lockdown on air quality with special reference to major pollutants, namely nitrogen dioxide (NO2), carbon monoxide (CO), sulphur dioxide (SO2) and aerosol optical depth (AOD). The assessment has been conducted for megacities of India (Delhi, Mumbai, Bengaluru, Chennai and Kolkata) for four months, that is, March and April in 2019 and 2020 using Sentinel 5P and MCD19A2 data. A decrease in concentrations of air pollutants, specifically NO2 and SO2, has been observed during the lockdown period in all the cities; whereas CO and AOD have exhibited discrete pattern of spatio-temporal variation. Four megacities except Kolkata have revealed a positive correlation between NO2 concentration and population density. The results conclude overall improvement in air quality during COVID-19 led lockdown. The current situation provides a unique opportunity to implement a structural economic change that could help us move towards a city with low emission economy. Realizing the achievable improvement of air quality, the study suggests further in-depth research on source attribution of individual pollutants to assess the prospect of emission reduction actions.

Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 in the United States

Background: Ever since the introduction of COVID-19 into the US, governments from the local to the federal levels have implemented combinations of responses to COVID-19. It is unclear how changes in human mobility across the country in response to intervention policies have shaped the transmission dynamic of COVID-19.Methods: Coupling a Bayesian hierarchical spatiotemporal model with the Google mobility data, we aimed to quantify the impact of human movement on COVID-19 incidence rate at the county level while accounting for spatial autocorrelation. We used both single-lag and distributed lag models to assess how movement today can affect incidence rates in the following weeks.Findings: On average, an increase in travel from home to other places was associated with an increase in reported COVID-19 incidence rate about 5 to 7 weeks later. A 10% increase in workplace movement 7 weeks ago was associated with 5% [95% confidence interval (CI): 1-9%] increase in COVID-19 incidence rate, 8% [95% CI:4-12%] for retail and recreation stores, 5% [95% CI:1-9%] for transits, and 5% [95% CI:29%] for grocery and pharmacies in the single-lag model analysis. No significant impact on incidence was found for human movement in parks. A 10% increase in the total duration at residential places was associated with a 13% (95% CI:6-20%) reduction in incidence rate 7 weeks after.Interpretation: Human movement activities substantially affected COVID-19 incidence rates at local levels but with delays varying from state to state. Policymakers should anticipate such a delay when planning intervention strategies restricting human movement.Funding Information: YY was supported by grant R56 AI148284 from the US National Institutes of Health. PJ was supported by grant R01 HL150119 from the US National Institutes of Health.Declaration of Interests: The Authors declare that there is no conflict of interest.Ethics Approval Statement: The study only involves publicly accessible deidentified and aggregated data and is therefore exempt from IRB approval.

Substantial Changes of Gaseous Pollutants and Health Effects During the COVID-19 Lockdown Period Across China.

The human movement and economic activities have been drastically reduced due to the Coronavirus Disease 2019 (COVID‐19) outbreak, leading to the sharp decreases of pollutant emissions and remarkable air quality improvement. Nevertheless, however, the changes of gaseous pollutant concentrations and health effects across China during the COVID‐19 lockdown period remained poorly understood. Here, a random forest model was applied to assess the impact of COVID‐19 lockdown on pollutant concentrations and potential health effects. The results suggested that estimated NO2, SO2, and CO concentrations in China during January 23–March 31, 2020 decreased by 13.68%, 25.71%, and 7.42%, respectively compared with the same periods in 2018–2019. Nonetheless, the predicted 8‐h O3 concentrations across China suffered from 1.29% increases during this period. The avoided premature all‐cause, cardiovascular disease (CVD), respiratory disease (RD), and chronic obstructive pulmonary disease (COPD) mortalities induced by NO2 decrease during COVID‐19 lockdown period reached 3,954 (3,076–4,832), 635 (468–801), 612 (459–765), and 920 (653–1,186) cases. However, the increases of all‐cause, CVD, RD, and COPD mortalities due to O3 increase during COVID‐19 lockdown period achieved 462 (250–674), 79 (29–129), 40 (−25–105), and 52 (−34–138) cases. The natural experiment demonstrated the drastic emission reduction measures could significantly decrease the NO2, SO2, and CO concentrations, while they significantly elevated the O3 concentration. It is highly imperative to propose more coordinated air pollution control strategies to control O3 pollution.