Human Physical Activity Research Articles

Physical culture and sports activity of Russians: the tasks of increasing in order to improve health and dynamics over 20 years (analytical review).

Justification. In the XXI century. The Russian Federation has made important decisions on the formation of a healthy lifestyle of the population and the development of physical culture and sports (FCS) in the country. The analysis of sociological research on the physical culture and sports activity of Russians is of interest from the point of view of the results achieved in the development of mass sports in Russia. Goal. To trace the dynamics of the FCS Russians' activities over two decades and their opinions on creating conditions for such activities. Materials and methods. Using the method of classical analysis and generalization of information, the results of various sociological surveys and scientific publications from the RSCI database on the topic (n = 43) were analyzed, the IBM SPSS Statistics 26 program was used for calculations Results. Surveys from 2006-2018 revealed a positive trend in the prevalence of FCS activities among Russians. Respondents began to note more often the presence of sports facilities in their place of residence. Recent studies show a growing satisfaction of citizens with the created conditions for FCS classes, but do not reveal a positive trend in their prevalence and in the use of places for such classes. Conclusion. To further involve Russians in FCS classes, it is important to educate them about the recommendations of specialists regarding human physical activity, taking into account the specifics of the motives for FCS classes and the reasons for refusing them in various socio-demographic groups. The wishes of citizens regarding the construction of new sports facilities should be linked to the installations for their use and with opportunities, including free time and financial resources to pay for attractive sports services. Comprehensive studies of these issues and analysis of statistics on the receipt of sports and wellness services can become the basis for a policy of providing benefits that stimulate physical culture and sports activity of Russians and for adjusting the standards of provision of the population with sports facilities.

An IoT solution that detects human physical activities to support health assessment based on sensors and supervised learning algorithms

At the moment, human physical activities can be detected by activity recognition systems to help with health status evaluation. One of the key issues in human activity recognition (HAR) systems is cost, speed, and accuracy. With the advancement of mobile devices and increasingly sophisticated microprocessors, along with the integration of various sensors including accelerometers, human activity recognition has become easier and faster. However, the accuracy of activity classification based on accelerometer data depends on many different factors, which can lead to potential deviations in results. Through various experiments, we have found that selecting optimal features is crucial for the performance of classification. In this paper, we propose a simple feature set that yields very promising results with an accuracy of up to 99%.

A novel hybrid deep learning approach with GWO–WOA optimization technique for human activity recognition

The effectiveness of Human Activity Recognition (HAR) models can be largely attributed to the components derived from domain expertise. The classification system swiftly and effectively categorizes human physical activity by utilizing a comprehensive collection of variables. To construct HAR models and categorize different activities, deep learning algorithms have recently seen increased application in academic research for autonomously extracting features from raw sensory data. The primary goal of this study is to develop a strong and accurate activity classification model by first extracting features from an input dataset and then using deep learning models. In this research, we look at how well the model does in the field of HAR. Analyzing time-series data from smartphones and wearable sensors, algorithms based on deep learning (DL) have been used to forecast various human actions. Applying DL-based methods to time-series data presents a variety of challenges, notwithstanding their usage for activity identification. If the suggested approach is put into practice, the concerns mentioned before may be alleviated. To construct HAR classification techniques using data collected from wearable sensors, this study presents two Hybrid Learning Algorithms (HLA). This research employs Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods to teach the model sequential and spatial patterns. The process of selecting characteristics has been enhanced through the use of optimization techniques. Together, the Whale Optimization Algorithm and the Grey Wolf Optimizer form a Hybrid Optimization approach, which is introduced in this study. By combining and capitalizing the benefits of both approaches, the hybrid technique enhances the optimization process as a whole. This research may also assist individuals who have undergone amputations or experienced strokes in selecting an appropriate model and developing a personalized gait reference trajectory.

Gui Qi Zhuang Jin Decoction ameliorates mitochondrial dysfunction in sarcopenia mice via AMPK/PGC-1α/Nrf2 axis revealed by a metabolomics approach

ObjectiveSarcopenia, as a condition of muscle mass loss and functional decline typically diagnosed in elderly individuals, severely affects human physical activity, metabolic homeostasis, and quality of life. Gui Qi Zhuang Jin Decoction (GQZJD), an approved hospital-based prescription with years of clinical application, has been demonstrated to have a notable therapeutic effect on sarcopenia. However, its potential mechanism of action in the treatment of sarcopenia remains uncertain. MethodsUltra-performance liquid chromatography paired with Q Exactive™ HF-X mass spectrometry (UPLC-QE-MS) was used to identify the ingredients of GQZJD. Subsequently, GQZJD observed the basic growth and muscles of the sarcopenia mouse, while the behavioral indicators were also tested. Muscle histopathology and serum oxidative stress biochemicals were also detected, and mitochondrial function and energy metabolism-related indicators in the gastrocnemius muscle were examined. Then, a metabolomics strategy was applied to predict possible pathways involving mitochondria by which GQZJD could improve sarcopenia. Finally, quantitative real-time polymerase chain reaction and western blot analyses were carried out to validate the effects of GQZJD on sarcopenia-induced mitochondrial dysfunction, together with uncovering the associated mechanisms. ResultsTwenty-seven ingredients absorbed into the blood (IAIBs) of GQZJD were identified using UPLC-QE-MS, which were regarded as the main active ingredients behind its sarcopenia treatment effects. GQZJD administration increased the body weight, gastrocnemius muscle mass, and autonomic activity, mitigated muscle tissue morphology and pathology; and alleviated the oxidative stress levels in sarcopenia mice. Treatment with GQZJD also decreased the mitochondrial reactive oxygen species level and serum lipid peroxide Malonaldehyde concentration. and increased the mitochondrial membrane potential, adenosine triphosphate level, 8‑hydroxy-2-deoxyguanosine content, mitochondrial DNA copy number, and the mitochondrial fission factor dynamin-related protein 1. Non-targeted metabolomics suggested that the sarcopenia therapeutic effect of GQZJD on sarcopenia may occur through the glycerophospholipid metabolism, choline metabolism in cancer, phenylalanine metabolism and tyrosine metabolism pathways, implying an association with AMP-activated protein kinase (AMPK) and related signals. Further, the molecular docking results hinted that AMPK performed well in terms of binding energy with the 27 IAIBs of GQZJD (average binding energy, -7.5 kcal/mol). Finally, we determined that GQZJD significantly activated the key targets of the AMPK/peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α)/nuclear factor erythroid 2-related factor 2 (Nrf2) axis.. ConclusionsOur results demonstrated that GQZJD ameliorated d-galactose-induced sarcopenia by promoting the animal behaviours, facilitating mitochondrial function and restoring mitochondrial energy metabolism. with its effects mediated by the AMPK/PGC-1α/Nrf2 axis. Over all, GQZJD represents a promising therapeutic candidate that ameliorated sarcopenia in aging mice.

Wearable Smart Silicone Belt for Human Motion Monitoring and Power Generation.

Human physical activity monitoring plays a crucial role in promoting personalized health management. In this work, inspired by an ancient Chinese belt, a belt-type wearable sensor (BWS) based on a triboelectric nanogenerator (TENG) is presented to monitor daily movements and collect the body motion mechanical energy. The developed BWS consists of a soft silicone sheet and systematically connected sensing units made from triboelectric polymer materials including polytetrafluoroethylene (PTFE) and polyamide (PA). A parameter study of the sensing units is firstly conducted to optimize the structure of BWS. The experimental studies indicate that the parameter-optimized BWS unit achieves a maximum output voltage of 47 V and a maximum current of 0.17 μA. A BWS with five sensing units is manufactured to record body movements, and it is able to distinguish different physical activities including stillness, walking, running, jumping, normal breathing, cessation of breathing, and deep breathing. In addition, the developed BWS successfully powers electronic devices including a smartphone, digital watch, and LED lights. We hope this work provides a new strategy for the development of wearable self-powered intelligent devices.

Perceived built environment as a mediator linking objective built environment and leisure-time physical activity in Chinese cities

Numerous studies have suggested that the perceived built environment is shaped by the objective built environment and influences human physical activity. However, the empirical examination of this pathway remains scant. Addressing this gap, our study investigates whether the built environment affects leisure-time physical activity through its impact on the perceived built environment, utilizing data collected from 760 residents in Fuzhou, China. Structural Equation Modeling results reveal a modest correlation between objective and perceived built environment elements, with the objective built environment being a stronger predictor of leisure-time physical activity. Notably, perceived recreational facilities significantly mediate the relationship between objective recreational facilities and leisure-time physical activity, accounting for 15% of the variance in physical activity due to objective recreational facilities. This mediation effect is consistent across subpopulations, irrespective of residential self-selection biases. These results highlight the imperative for urban planning and policy to extend beyond mere spatial allocation of amenities to enhancing both the actual and perceived accessibility of these facilities, thus underlining the study's profound implications for public health and urban development strategies.

Developing a computational representation of human physical activity and exercise using open ontology-based approach: a Tai Chi use case.

Many studies have examined the impact of exercise and other physical activities in influencing the health outcomes of individuals. These physical activities entail an intricate sequence and series of physical anatomy, physiological movement, movement of the anatomy, etc. To better understand how these components interact with one another and their downstream impact on health outcomes, there needs to be an information model that conceptualizes all entities involved. In this study, we introduced our early development of an ontology model to computationally describe human physical activities and the various entities that compose each activity. We developed an open-sourced biomedical ontology called the Kinetic Human Movement Ontology that reused OBO Foundry terminologies and encoded in OWL2. We applied this ontology in modeling and linking a specific Tai Chi movement. The contribution of this work could enable modeling of information relating to human physical activity, like exercise, and lead towards information standardization of human movement for analysis. Future work will include expanding our ontology to include more expressive information and completely modeling entire sets of movement from human physical activity.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo-Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., Enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach.

Semi-supervised ensemble learning for human activity recognition in casas Kyoto dataset

—The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.

Plant‐Inspired Dual‐Functional Sensor for Monitoring Pulse and Sweat Volume

AbstractWearable health monitoring sensors, when employed to monitor pulse and sweat volume in real‐time during human physical activity, serve as an effective preventative measure against abnormal health conditions, such as elevated heart rate or excessive dehydration. Herein, a dual‐functional sensor based on a sandwich structure composed of polyvinylidene fluoride hexafluoropropylene / conductive carbon black perforated film (named PCp film) and corncob sponge is designed to monitor pulse and sweat volume. The PCp film prepared by the breath figure method serves as a piezoresistive sensor with high sensitivity (0.06 kPa−1), a fast response time (0.9 ms), and high reusability for pulse monitoring. Furthermore, the PCp film has excellent water permeability (27.3 L m−2 h−1); thus, the sweat permeating through the PCp film can be absorbed by the corncob sponge. The sandwich‐structured capacitive sensor is also highly sensitive (6.021 pF nL−1) to the absorbed sweat and is highly reusable. In addition, the dual‐functional sensor is successfully integrated into a sports bracelet system to display real‐time pulse and sweat‐volume data of the wearer. This approach provides a dependable foundation for the advancement of multifunctional sensors in the field of sports monitoring.

Evaluation of tactile sensation for fabric handle – evidence from event related potentials

PurposeIn this paper, the brain potential changes caused by touching fabrics for handle evaluation were recorded by event related potential (ERP) method, compared with subjective evaluation scores and physical index of KES, explore the cognitive mechanism of the transformation of tactile sensation into neural impulses triggered by subtle mechanical stimuli such as material, texture, density and morphology in fabrics. By combining subjective evaluation of fabric tactile sensation, objective physical properties of fabrics and objective neurobiological signals, explore the neurophysiological mechanism of tactile cognition and the signal characteristics and time process of tactile information processing.Design/methodology/approachThe ERP technology was first proposed by a British psychologist named Grey Walter. It is an imaging technique of noninvasive brain cognition, whose potential changes are related to the human physical and mental activities. ERP is different from electroencephalography (EEG) and evoked potentials (EP) on the fact that it cannot only record stimulated physical information which is transmitted to brain, but also response to the psychological activities which related to attention, identification, comparison, memory, judgment and cognition as well as to human’s neural physiological changes which are caused by cognitive process of the feeling by stimulation.FindingsAccording to potential changes in the cerebral cortex evoked by touching four types of silk fabrics, human brain received the physical stimulation in the early stage (50 ms) of fabrics handle evaluation, and the P50 component amplitude showed negative correlation with fabric smoothness sensations. Around 200 ms after tactile stimulus onset, the amplitude of P200 component show positive correlation with the softness sensation of silk fabrics. The relationship between the amplitude of P300 and the sense of smoothness and softness need further evidence to proof.Originality/valueIn this paper, the brain potential changes caused by touching fabrics for handle evaluation were recorded by event related potential (ERP) method, compared with subjective evaluation scores and physical index of KES, the results shown that the maximum amplitude of P50 component evoked by fabric touching is related to the fabrics’ smoothness and roughness emotion, which means in the early stage processing of tactile sensation, the rougher fabrics could arouse more attention. In addition, the amplitude of P200 component shows positive correlation with the softness sensation of silk fabrics.

PAR-Net: An Enhanced Dual-Stream CNN-ESN Architecture for Human Physical Activity Recognition.

Physical exercise affects many facets of life, including mental health, social interaction, physical fitness, and illness prevention, among many others. Therefore, several AI-driven techniques have been developed in the literature to recognize human physical activities. However, these techniques fail to adequately learn the temporal and spatial features of the data patterns. Additionally, these techniques are unable to fully comprehend complex activity patterns over different periods, emphasizing the need for enhanced architectures to further increase accuracy by learning spatiotemporal dependencies in the data individually. Therefore, in this work, we develop an attention-enhanced dual-stream network (PAR-Net) for physical activity recognition with the ability to extract both spatial and temporal features simultaneously. The PAR-Net integrates convolutional neural networks (CNNs) and echo state networks (ESNs), followed by a self-attention mechanism for optimal feature selection. The dual-stream feature extraction mechanism enables the PAR-Net to learn spatiotemporal dependencies from actual data. Furthermore, the incorporation of a self-attention mechanism makes a substantial contribution by facilitating targeted attention on significant features, hence enhancing the identification of nuanced activity patterns. The PAR-Net was evaluated on two benchmark physical activity recognition datasets and achieved higher performance by surpassing the baselines comparatively. Additionally, a thorough ablation study was conducted to determine the best optimal model for human physical activity recognition.

Hierarchical organization of human physical activity

Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity (‘quanta’) that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.

A holistic methodology for mitigating awkward postural risks: Evidence from South Indian small-scale industries.

Small-scale industries (SSI) are the global economy's backbone since most industrial workers are connected. Most of these workers are contractual and temporary without appropriate training. Also, the SSI does not have a standard workplace with an appropriate layout and infrastructure, as they manage with minimum resources. Therefore, the work hazards, i.e., musculoskeletal disorders and fatigue, often go unnoticed as holistic postural risk methodology is still scarce for identifying the awkward postures in SSI. The present study proposes a novel holistic methodology to track and mitigate awkward postural risks in human-physical activities in SSI. To determine the effectiveness of the proposed methodology, a case study is presented in the South Indian Pump industry, wherein a critical workstation with a complex ergonomic work environment is employed. An ergonomic evaluation was conducted empirically and numerically in the workplaces using Digital Human Models. In numerical evaluation, three virtual workspaces have been created to redesign the identified crucial workstation, focusing on ergonomics and workflow. The results obtained from the case study are encouraging for to use of the novel methodology in SSI. The case study reports that the proposed design significantly reduced the REBA score and WISHA lifting index by 6 and 1.20, respectively, without significant investment. The proposed methodology could encourage research to identify awkward posture in SSI.

Measuring regularity of human physical activities with entropy models

Regularity is an important aspect of physical activity that can provide valuable insights into how individuals engage in physical activity over time. Accurate measurement of regularity not only advances our understanding of physical activity behavior but also facilitates the development of human activity modeling and forecasting. Furthermore, it can inform the design and implementation of tailored interventions to improve population health outcomes. In this paper, we aim to assess the regularity of physical activities through longitudinal sensor data, which reflects individuals’ all physical activities over an extended period. We explore three entropy models, including entropy rate, approximate entropy, and sample entropy, which can potentially offer a more comprehensive evaluation of physical activity regularity compared to metrics based solely on periodicity or stability. We propose a framework to validate the performance of entropy models on both synthesized and real-world physical activity data. The results indicate entropy rate is able to identify not only the magnitude and amount of noise but also macroscopic variations of physical activities, such as differences on duration and occurrence time. Simultaneously, entropy rate is highly correlated with the predictability of real-world samples, further highlighting its applicability in measuring human physical activity regularity. Leveraging entropy rate, we further investigate the regularity for 686 individuals. We find the composition of physical activities can partially explain the difference in regularity among individuals, and the majority of individuals exhibit temporal stability of regularity.

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training.

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

Adapted Physical Activity Scholarship: Evolving From Corrective to Inclusion and Anti-Ableist

Kinesiology and adapted physical activity (APA) share a common history rooted in the medical model approach to physical activity, movement, and the human body. The evolution of APA was influenced by these early roots and later by special-education legislation, sensory-motor perspectives, inclusion movement, and the disability-rights movement. Originally identified as adapted physical education, APA emerged as a professional field and an academic discipline. Since the 1950s, the research and scholarship has increased and cuts across the specialization areas (subdisciplines) of kinesiology. The multidisciplinary nature of APA scholarship has also reached beyond the discipline of kinesiology informed by disability studies and sociology. Reflection about APA and kinesiology reveals the ableist nature of the medical model, which informed early professional practice and scholarship. Thus, it is critical that APA and kinesiology engage in anti-ableist scholarship to better understand human physical activity and movement inclusive of individuals with disabilities.

Translation Motion-Based Backpack for Indonesian Army Soldiers as a Source of Energy for Military Communication Devices

Background: Energy is an aspect that is very necessary in human life, because every activity we do consumes and produces energy. Among various sources of energy, human physical activity is a potential energy source that can be utilized.Objective: This research aims to design a backpack with translation motion as an energy source for military communication devices.Methods: The design of this tool uses the research and development method.Result: The results of the study indicate that walking produces a voltage of approximately 3.863 volts, climbing stairs produces 4.56 volts, and running generates 5.39 volts. The voltage generated increases with the level of human movement involved.Conclusion: In conclusion, the more human movement is involved, the higher the voltage generated. The calculated spring constant is Ktotal = 22.612 N/m, with a unit spring constant of 11.306 N/m. F

Comparative performance of machine learning models for the classification of human gait

The efficacy of human activity recognition (HAR) models mostly relies on the characteristics derived from domain expertise. The input of the classification algorithm consists of many characteristics that are utilized to accurately and effectively classify human physical activities. In contemporary research, machine learning techniques have been increasingly employed to automatically extract characteristics from unprocessed sensory input to develop models for Human Activity Recognition (HAR) and classify various activities. The primary objective of this research is to compare and contrast several machine learning models and determine a reliable and precise classification model for classifying activities. This study does a comparison analysis in order to assess the efficacy of 10 distinct machine learning models using frequently used datasets in the field of HAR. In this work, three benchmark public human walking datasets are being used. The research is conducted based on eight evaluating parameters. Based on the study conducted, it was seen that the machine learning classification models Random Forest, Extra Tree, and Light Gradient Boosting Machine had superior performance in all the eight evaluating parameters compared to specific datasets. Consequently, it can be inferred that machine learning significantly enhances performance within the area of Human Activity Recognition (HAR). This study can be utilized to provide suitable model selection for HAR-based datasets. Furthermore, this research can be utilized to facilitate the identification of various walking patterns for bipedal robotic systems.