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This paper, based on the Praxis Lecture I delivered at the National Association of Kinesiology in Higher Education Conference in January 2024, articulates how my experiences at a Danish gymnastic folkehøjskole (folk high school) and højskole pedagogy inform my teaching and service. After defining højskole pedagogy, I discuss how I incorporate its elements to promote greater inclusion, equity, and diversity in kinesiology, particularly around (dis)ability and body size. Højskole pedagogy also provides a way to encourage students to embody their learning and learn about their bodies through course content and experiential lessons. I dedicate this article to Dr. Seymour (Sy) Kleinman, my advisor and mentor, who tirelessly championed human movement as the foundation of kinesiology. He emphasized the importance of teaching, exploring, performing, and experiencing human movement, particularly in the context of increasing efforts to by those within and outside kinesiology to scientize and subdivide the field. [ABSTRACT FROM AUTHOR]

Recognizing that conventional understanding of animal and human locomotion is based on a dated and reductionist machine modeling of organisms, we set out to create a theory of locomotion by reasoning from first principles. We center on the constraints necessitated by 1) the 2nd law of thermodynamics, 2) the theory of evolution, 3) a systems science view of organisms, and 4) the laws of motion, but we also look for compatibility these constraints might find in emerging areas of scientific inquiry (ecological psychology, processual biology, soft matter, biotensegrity), and in the wisdom embedded in various movement traditions and ancient philosophy. Applying and synthesizing these, we propose an updated "bouncing bones" (BB) model for walking and running, which corresponds with maximum efficiency and conservation of energy. [ABSTRACT FROM AUTHOR]

AbstractSport moves people physically, emotionally, socially and movement is central to sport. Yet, engagement with sport in mobilities studies is rare. This paper outlines an agenda for sport mobilities approaches to catalyse greater engagement in the field with the world of sport and to strengthen dialogues with, and mobilities’ position within, interdisciplinary sport studies. We first establish the promise of sport mobilities research before tracing existing engagements, and finally offering a framework for future sport mobilities approaches. [ABSTRACT FROM AUTHOR]

Recently, AI‐driven skeleton reconstruction tools that use multistage computer vision pipelines were designed to estimate 3D kinematics from 2D video sequences. In the present study, we validated a novel markerless, smartphone video‐based artificial intelligence (AI) motion capture system for hip, knee, and ankle angles during countermovement jumps (CMJs). Eleven participants performed six CMJs. We used 2D videos created by a smartphone (Apple iPhone X, 4K, 60 fps) to create 24 different keypoints, which together built a full skeleton including joints and their connections. Body parts and skeletal keypoints were localized by calculating confidence maps using a multilevel convolutional neural network that integrated both spatial and temporal features. We calculated hip, knee, and ankle angles in the sagittal plane and compared it with the angles measured by a VICON system. We calculated the correlation between both method's angular progressions, mean squared error (MSE), mean average error (MAE), and the maximum and minimum angular error and run statistical parametric mapping (SPM) analysis. Pearson correlation coefficients (r) for hip, knee, and ankle angular progressions in the sagittal plane during the entire movement were 0.96, 0.99, and 0.87, respectively. SPM group‐analysis revealed some significant differences only for ankle angular progression. MSE was below 5.7°, MAE was below 4.5°, and error for maximum amplitudes was below 3.2°. The smartphone AI motion capture system with the trained multistage computer vision pipeline was able to detect, especially hip and knee angles in the sagittal plane during CMJs with high precision from a frontal view only. Highlights: Smartphone‐based movement analysis can accurately estimate joint angles during countermovement jumps.AI‐controlled algorithms calculate joint angles in the sagittal plane from a smartphone video in a frontal view.The study demonstrates the potential of AI‐controlled movement analysis with one camera for 3D kinematics.Future research should focus on expanding the training data to enable the analysis of more complex movements and improve temporal modeling. [ABSTRACT FROM AUTHOR]

Introduction: Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables. Methods: Twenty-two healthy men (n = 11; ... + SD; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 x 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p < 0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data were subtracted from the total variability data from the present study. Results: No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability). Discussion: Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has altered urban mobility patterns as various travel restrictions were imposed at different stages of the pandemic. Such dynamics inevitably affect the structure of urban spatial interactions underlying disease spreading. Understanding the spatial interaction structure and its dynamics is crucial for disease control. Through detecting networked communities, we identified latent movement boundaries emerged from actual human movement flows. We analyzed intra- and intercommunity flows that not only capture the regional and cross-region urban mobility structures but also facilitate the expansion and relocation diffusion processes. Networks representing four snapshots (prepandemic, lockdown, transition, and normal) were analyzed. The intracommunity flow intensities indicated similar patterns across four snapshots, suggesting the relative stability of the local expansion diffusion patterns throughout the study period. The intercommunity analysis showed the changes of spatial interaction intensities within the city, signifying the dynamic of the cross-regions disease spreading. Analyzing the intra- and intercommunity network structures provided a more holistic understanding of the dynamic urban mobility structures during the pandemic, highlighting the potential disease diffusion processes. This framework can also be used for disease management strategies simulation and future mobility-related urban planning. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) on shape memory polymers result in densified, nano- to microscale wrinkles upon heat-induced shrinkage. These wrinkled CNT bundles can be transferred into soft materials similar to the human epidermis for extremely highly stretchable skin mountable strain sensors with a dynamic range of over 700%.

Context: Aberrant movement patterns among individuals with concussion history have been reported during sport-related movement. However, the acute postconcussion kinematic and kinetic biomechanical movement patterns during a rapid acceleration–deceleration task have not been profiled and leaves their progressive trajectory unknown. Our study aimed to examine single-leg hop stabilization kinematics and kinetics between concussed and healthy-matched controls acutely (≤7 d) and when asymptomatic (≤72 h of symptom resolution). Design: Prospective, cohort laboratory study. Methods: Ten concussed (60% male; 19.2 [0.9] y; 178.7 [14.0] cm; 71.3 [18.0] kg) and 10 matched controls (60% male; 19.5 [1.2] y; 176.1 [12.6] cm; 71.0 [17.0] kg) completed the single-leg hop stabilization task under single and dual task (subtracting by 6's or 7's) at both time points. Participants stood on a 30-cm tall box set 50% of their height behind force plates while in an athletic stance. A synchronized light was illuminated randomly, queuing participants to initiate the movement as rapidly as possible. Participants then jumped forward, landed on their nondominant leg, and were instructed to reach and maintain stabilization as fast as possible upon ground contact. We used 2 (group) × 2 (time) mixed-model analyses of variance to compare single-leg hop stabilization outcomes separately during single and dual task. Results: We observed a significant main group effect for single-task ankle plantarflexion moment, with greater normalized torque (mean difference = 0.03 N·m/body weight; P =.048, g = 1.18) for concussed individuals across time points. A significant interaction effect for single-task reaction time indicated that concussed individuals had slower performance acutely relative to asymptomatic (mean difference = 0.09 s; P =.015, g = 0.64), while control group performance was stable. No other main or interaction effects for single-leg hop stabilization task metrics were present during single and dual task (P ≥.051). Conclusions: Greater ankle plantarflexion torque coupled with slower reaction time may indicate stiff, conservative single-leg hop stabilization performance acutely following concussion. Our findings shed preliminary light on the recovery trajectories of biomechanical alterations following concussion and provide specific kinematic and kinetic focal points for future research. [ABSTRACT FROM AUTHOR]

In order to understand human motion object detection methods, the author proposes a research on human motion object detection method based on Gaussian model. Firstly, traditional Gaussian models are unable to detect complex scenes or slow moving targets. Therefore, an improved Gaussian model based moving object detection algorithm is proposed. Secondly, multiple Gaussian models are used to represent the features of each pixel in the moving target image, and based on the matching of each pixel in the image with the Gaussian model, it is considered as a background point. Conversely, it is based on the principle of the foreground, and the Gaussian model is updated. Finally, by updating the foreground model and calculating short-term stability indicators, the detection effect of moving targets is improved. By determining the Gaussian distribution and pixel relationship, new parameters are set to construct the background model and eliminate the impact caused by sudden changes in lighting. The experimental analysis results show that this method can effectively detect and track moving targets, with good noise resistance, high clarity, and an accuracy rate of up to 99%. Compared with traditional Gaussian model methods, the improved method can more effectively detect moving targets and has better robustness. [ABSTRACT FROM AUTHOR]

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. [ABSTRACT FROM AUTHOR]

Human detection and classification via Wi-Fi transmission have received a lot of attention in recent years as crucial facilitators in security and humancomputer interaction (HCI). The passive Wi-Fi radar (PWR) system used by previous researchers applied cross-ambiguity function (CAF) and CLEAN algorithms to process the detected signals. This paper explores the feasibility and viability of a PWR system in detecting and classifying human movements without utilizing CAF and CLEAN algorithms. The movements are performed by four participants but with comparable body sizes and heights. Three daily human movements are investigated namely walking, bending, and sitting, with each participant performing each movement 24 times, providing a total of 96 samples per activity. The system is evaluated based on the consistency of the signal pattern in a frequency domain and the percentage accuracy is assessed using an artificial neural network (ANN) classifier and trained using a leave-one-out cross-validation (LOOCV) method. The frequency domain results reveal that the signals are consistent, with no noticeable variations or changes in the voltage intensity or shape of the main lobe. The classification of the movements shows that the classifier has an overall accuracy of 97.6%. [ABSTRACT FROM AUTHOR]

ObjectiveBiomechanics represents the common final output through which all biopsychosocial constructs of back pain must pass, making it a rich target for phenotyping. To exploit this feature, several sites within the NIH Back Pain Consortium (BACPAC) have developed biomechanics measurement and phenotyping tools. The overall aims of this article were to: 1) provide a narrative review of biomechanics as a phenotyping tool; 2) describe the diverse array of tools and outcome measures that exist within BACPAC; and 3) highlight how leveraging these technologies with the other data collected within BACPAC could elucidate the relationship between biomechanics and other metrics used to characterize low back pain (LBP).MethodsThe narrative review highlights how biomechanical outcomes can discriminate between those with and without LBP, as well as among levels of severity of LBP. It also addresses how biomechanical outcomes track with functional improvements in LBP. Additionally, we present the clinical use case for biomechanical outcome measures that can be met via emerging technologies.ResultsTo answer the need for measuring biomechanical performance, our "Results" section describes the spectrum of technologies that have been developed and are being used within BACPAC.Conclusion and future directionsThe outcome measures collected by these technologies will be an integral part of longitudinal and cross-sectional studies conducted in BACPAC. Linking these measures with other biopsychosocial data collected within BACPAC increases our potential to use biomechanics as a tool for understanding the mechanisms of LBP, phenotyping unique LBP subgroups, and matching these individuals with an appropriate treatment paradigm.

Aims of this study were to investigate if changes in elbow and wrist joints loading and biomechanical characteristics of performance existed as a function of (a) different hand placement and (b) fundamental skills development in female gymnastics. Ten female gymnasts performed 54 successful trials of round-off skills (cartwheel [18], round-off [18], round-off to back handspring [18]), with three different hand positions (parallel, T-shape and reverse). Kinematic and kinetic data were collected for each trial. A two-way repeated measures ANOVA was used to analyse the injury risk factors. Findings of the current study showed that an increase in RO skill difficulty level significantly influences the mechanical load on the upper extremities. With the increase in mechanical load on the upper extremities during the reverse and parallel hand positions, this study suggests that that T-shape hand position should be used as the primary technique for the young female. Differences in vertical velocity from touchdown to take-off between the three hand positions for the RO and RO-BH suggested that the reverse position was less effective for young female gymnasts. The findings of the current study demonstrated no clear performance benefits between hand position selection. [ABSTRACT FROM AUTHOR]

The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human–machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs. [ABSTRACT FROM AUTHOR]

IntroductionAdvances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables.MethodsTwenty-two healthy men (n = 11; X¯±SD; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 × 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p 50% total variability).DiscussionCombined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement.

Respiratory infectious diseases can cause public health emergencies, threatening human well-being, social operation, and economic development. Clarifying the transmission mechanism of respiratory infectious diseases is essential for control measures. We review the main research findings on the transmission mechanism of respiratory infectious diseases in recent decades. The source characteristics of respiratory infectious diseases, the airborne transmission mechanism, the exposure of susceptible persons, and the infection risk assessment methods are discussed. Given that the dynamic scenario of respiratory infectious disease transmission has attracted wide attention in recent years, we summarize the effects of human movement on indoor airflow, pathogen diffusion, and human exposure. Considering the everyday use of facemasks, the effects of facemasks on source characteristics and infection risk are also discussed. Finally, future research prospects are proposed. The transmission mechanism of infectious diseases can be comprehensively explored by delving into patients' pathological characteristics and personnel protection measures. This exploration can be facilitated by establishing a multi-pose manikin database, enabling personalized and refined evaluations. Interdisciplinary cooperation will play a pivotal role in fostering a holistic understanding. Furthermore, it is crucial to account for the impact of individuals' activity patterns on disease transmission dynamics. This review is expected to reference public health emergency management.

This article offers a governing, trans-disciplinary theory for understanding physical activity in humans. The Unifying Theory of Physical Activity involves three aspects. First, it frames physical activity as an essential human act resulting from inherent urges: to feel, to explore, to transform and to connect. These urges prelude and compel people's involvement in physical activity and contribute to the meanings and purposes that sustain life and growth. Second, we argue the act of physical activity is made of three conditions. Physical activity possesses a potentiality, and it is distinct and integrated. Third, at the external level, there are social, political, and situated forces that interplay with the urges and shape human experience in/of physical activity. We offer conclusions about how this theory can inform research, policy, and practice about physical education, physical activity and health promotion. [ABSTRACT FROM AUTHOR]

Movement is manifested through a series of patterns at multiple spatial and temporal scales. Movement data today are becoming available at increasingly fine-grained temporal granularity. These observations often represent multiple behavioral modes and complex patterns along the movement path. However, the relationships between the observation scale of movement data and the analysis scales at which movement patterns are captured remain understudied. This article aims at investigating the role of temporal scale in movement data analytics. It takes up an important question of “how do decisions surrounding the scale of movement data and analyses impact our inferences about movement patterns?” Through a set of computational experiments in the context of human movement, we take a systematic look at the impact of varying temporal scales on common movement analytics techniques including trajectory analytics to calculate movement parameters (e.g., speed, path tortuosity), estimation of individual space usage, and interactions analysis to detect potential contacts between multiple mobile individuals.

This paper focuses on a human motion image detection and tracking method that utilizes a Gaussian mixture model and light sensor. The objective is to develop an accurate and efficient approach for detecting and tracking human motion images in various applications such as human behavior analysis and intelligent monitoring. To achieve this, the method utilizes a Gaussian mixture model to establish a background model of the scene. The background model is used to estimate the static elements in the scene. By computing the background difference of the current image, a binary image of the foreground region is obtained. Additionally, data from the light sensor is incorporated to further segment and filter the foreground area, removing noise and irrelevant motion. The detection and tracking of human motion is realized through trajectory analysis and matching techniques. Experimental evaluation confirms the effectiveness and accuracy of the proposed method. The results demonstrate that the method achieves high accuracy and robustness in human motion image detection and tracking. Compared to traditional methods, this approach significantly improves the accuracy of recognizing human body boundaries and tracking motion. [ABSTRACT FROM AUTHOR]

This essay delves into the intricate relationship between biomechanics and motor control, exploring their historical evolution and close interdependence. From the foundational works of Aristotle to the contemporary advancements achieved by esteemed members of the National Academy of Kinesiology, we describe the impactful contributions of both past and present National Academy of Kinesiology figures in the realms of motor control and biomechanics. A key theme throughout the essay is the recognition of the fundamental influence of natural laws on movement and the fundamental role of variability in unifying the realms of biomechanics and motor control. Looking ahead, we emphasize the transformative potential of strong inference as a guiding principle for substantive research in both fields, illustrating its application through our investigative endeavors. By uniting biomechanics and motor control through interdisciplinary collaboration, this pursuit of knowledge holds the promise of reshaping our comprehension of human movement and performance. [ABSTRACT FROM AUTHOR]

Vector borne diseases like malaria are spreading worldwide. They have become a major cause of morbidity and mortality. Malaria cases are increasing due to the human movement from one place to the other. Changing temperature level has significant impact on the life cycle, biting behavior and death rates of the mosquitoes which can transmit the disease. In the present work, a multi-patch SEIRS - SEI deterministic compartmental model for malaria disease is developed to study the disease transmission dynamics. The impact of temperature and human movement in transmission dynamics is investigated. Both global and local basic reproduction numbers are computed for two patches and local stability is discussed. Numerical results show that the prevalence of the disease can be reduced by managing human movement between the patches and the temperature affects the transmission of malaria disease. [ABSTRACT FROM AUTHOR]

Action quality assessment is a significant research domain in computer vision, aimed at evaluating the accuracy of human movement and providing feedback and guidance for training and rehabilitation. However, the uneven nature of the data, which has a significant impact on the labels with less samples, is not taken into consideration by the generally used approaches in this field. To address this issue, we propose using kernel density estimation (KDE) to recalculate the label density and weight the loss function by the reciprocal of the square root of each label density. Additionally, we divide the entire motion into three sub-stages, including the takeoff, aerial movement, and entry for diving, and connect the three stages using an across-staged temporal reasoning module (ASTRM). Our approach achieves a performance of 0.9222 Spearman correlation coefficient (ρ ) and 0.3304 (× 100) Relative ℓ 2 -distance (R - ℓ 2 ) on the FineDiving dataset, demonstrating competitiveness compared to other methods. Furthermore, numerous comprehensive ablation experiments validate the effectiveness of the methods and modules we adopted. [ABSTRACT FROM AUTHOR]

Understanding particle dispersion characteristics in indoor environments is crucial for revising infection prevention guidelines through optimized engineering control. The secondary wake flow induced by human movements can disrupt the local airflow field, which enhances particle dispersion within indoor spaces. Over the years, researchers have explored the impact of human movement on indoor air quality (IAQ) and identified noteworthy findings. However, there is a lack of a comprehensive review that systematically synthesizes and summarizes the research in this field. This paper aims to fill that gap by providing an overview of the topic and shedding light on emerging areas. Through a systematic review of relevant articles from the Web of Science database, the study findings reveal an emerging trend and current research gaps on the topic titled Impact of Human Movement in Indoor Airflow (HMIA). As an overview, this paper explores the effect of human movement on human microenvironments and particle resuspension in indoor environments. It delves into the currently available methods for assessing the HMIA and proposes the integration of IoT sensors for potential indoor airflow monitoring. The present study also emphasizes incorporating human movement into ventilation studies to achieve more realistic predictions and yield more practical measures. This review advances knowledge and holds significant implications for scientific and public communities. It identifies future research directions and facilitates the development of effective ventilation strategies to enhance indoor environments and safeguard public health. [ABSTRACT FROM AUTHOR]

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people's privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body's when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%. [ABSTRACT FROM AUTHOR]

New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efficiency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer field test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Significant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed significant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically significantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running. [ABSTRACT FROM AUTHOR]

Purpose: Human movement is essential for health and well-being. Understanding human movement is pivotal in physiotherapy, but also an important element of physiotherapy education. This review identified, critically appraised, and synthesized the available evidence on learning and teaching human movement in physiotherapy as perceived by students, therapists, and instructors. Methods: The databases MEDLINE, CINAHL, ERIC, PsycINFO, MEDIC and FINNA, were searched. The search was conducted in March/April 2020 and updated in March 2022. The systematic review followed the JBI methodology for systematic reviews of qualitative evidence and was conducted in accordance with an a priori protocol. Results: The overall quality of the 17 included studies was scored low on ConQual but dependability and credibility were rated as moderate. Four synthesized findings aggregated from 17 categories and 147 findings described the perceived significance of 1) being present in movement, 2) movement quality, 3) movement transfer, and 4) contextual factors for the learning or teaching of human movement in physiotherapy. Conclusion: The synthesized findings indicate that the perceived significance of contextual factors, movement quality and transfer, and being present in movement should be considered in all learning and teaching of movement in physiotherapy. However, the evidence of the review findings was evaluated as low-level, which should be considered when applying these results to physiotherapy education or practice. [ABSTRACT FROM AUTHOR]

Background: Soccer is the world's most popular sport for both men and women. Tests of athletic and functional performance are commonly used to assess physical ability and set performance goals. The Functional Movement Screen (FMSTM) is a widely used seven-test battery developed by practitioners to provide interpretable measure of movement quality. The main objective of the present study was twofold, to analyze the relationship between FMSTM results from male and female soccer players and to compare their physical performance in different tests. Methods: A total of twenty-eight semi-professional soccer players: fourteen male (age: 21.29 ± 1.64 years; weight: 70.66 ± 5.29 kg; height: 171.86 ± 5.35 cm; BMI: 20.90 ± 2.22 kg/m²) and fourteen females (age: 20.64 ± 1.98 years; weight: 63.44 ± 5.83 kg; height: 166.21 ± 12.18 cm; BMI: 23.02 ± 2.50 kg/m²) were recruited for this study. A paired sample t-test was used for determining differences as a repeated measures analysis. All the participants conducted the following tests: The Functional Movement Test (FMS™), 10-m linear sprint, 5-0-5 COD Test and Yo-Yo Intermittent Recovery Test--Level 1 (YYIRT Level 1). Results: A t-test with data from 505 COD (change of direction) test showed significant differences between groups, p = 0.001, d = 1.11, revealing faster times in male soccer players (2.50 ± 0.19) in respect with female soccer players (2.70 ± 0.17). Crucially, a t-test with data from FMS did not reveal significant differences between groups. Multiple regression for V02max revealed significant effects (r = 0.55, r² = 0.30, adjusted r² = 0.24, F = 5.21, p = 0.04 and standard error = 2.20). On the other hand, multiple regression for 10-m sprint showed significant effects (r = 0.58, r² = 0.33, adjusted r² = 0.28, F = 5.98, p = 0.03). The impact of these factors on the correlation between FMSTM scores and physical performance measures can vary among individuals. Discussion/Conclusion: This study demonstrates the necessity of utilizing and applying multiple field-based tests to evaluate the movement and capabilities of physical performance in sports. Crucially, consider individual variations and factors such as training background, fitness level, and sport-specific demands when interpreting the relationship between the FMS℄ and physical performance in both sexes. [ABSTRACT FROM AUTHOR]

In recent years, people have been committed to studying how to recognize human behavior through the use of wearable devices and understand the intentions expressed by human actions, which has also attracted much attention in human-computer interaction. At present, research on dance mainly relies on computer simulation, while wearable devices can more realistically reflect the details and dynamic processes of dance movements. In order to realize the recognition of dancing human movements, this paper proposes a system that combines Motion capture technology and wearable devices, and uses wearable devices for recognition. Before the experiment began, we established a process for collecting and preprocessing dance action data. On the basis of in-depth research and analysis of Motion capture technology, we have successfully constructed a human Motion capture data-driven system, which can obtain the data needed by the human body to dance through wearable devices. After experimental verification, the system designed in this article performs excellently in recognizing human movements in dance. The recognition results not only contribute to dance training and correcting errors, but also gradually become an important way for dance learning, providing new ideas for dance learning and inheritance.

In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during movement is the presence of artefacts due to cranial muscle contraction; the objectives of this study therefore focused on two main aspects: (1) validating a tool capable of decreasing movement artefacts, while developing a reliable method for the quantitative analysis of EEG data; (2) using this method to analyse the EEG signal recorded during a particular motor activity (bi- and monopodalic postural control). Methods: The EEG sampling frequency was 512 Hz; the signal was acquired on 16 channels with monopolar montage and the reference on Cz. The recorded signals were processed using a specifically written Matlab script and also by exploiting open-source software (Eeglab). Results: The procedure used showed excellent reliability, allowing for a significant decrease in movement artefacts even during motor tasks performed both with eyes open and with eyes closed. Conclusions: This preliminary study lays the foundation for correctly recording EEG signals as an additional source of information in the study of human movement.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

Context: Laboratory-based movement assessments are commonly performed without cognitive stimuli (ie, single-task) despite the simultaneous cognitive processing and movement (ie, dual task) demands required during sport. Cognitive loading may critically alter human movement and be an important consideration for truly assessing functional movement and understanding injury risk in the laboratory, but limited investigations exist. Objective: To comprehensively examine and compare kinematics and kinetics between single- and dual-task functional movement among healthy participants while controlling for sex. Design: Cross-sectional study. Setting: Laboratory. Patients (or Other Participants): Forty-one healthy, physically active participants (49% female; 22.5 ± 2.1 y; 172.5 ± 11.9 cm; 71.0 ± 13.7 kg) enrolled in and completed the study. Intervention(s): All participants completed the functional movement protocol under single- and dual-task (subtracting by 6s or 7s) conditions in a randomized order. Participants jumped forward from a 30-cm tall box and performed (1) maximum vertical jump landings and (2) dominant and (3) nondominant leg, single-leg 45° cuts after landing. Main Outcome Measures: The authors used mixed-model analysis of variances (α =.05) to compare peak hip, knee, and ankle joint angles (degrees) and moments (N·m/BW) in the sagittal and frontal planes, and peak vertical ground reaction force (N/BW) and vertical impulse (Ns/BW) between cognitive conditions and sex. Results: Dual-task resulted in greater peak vertical ground reaction force compared with single-task during jump landing (mean difference = 0.06 N/BW; 95% confidence interval [CI], 0.01 to 0.12; P =.025) but less force during dominant leg cutting (mean difference = −0.08 N/BW; 95% CI, −0.14 to −0.02; P =.015). Less hip-flexion torque occurred during dual task than single task (mean difference = −0.09 N/BW; 95% CI, −0.17 to −0.02). No other outcomes were different between single and dual task (P ≥.053). Conclusions: Slight, but potentially important, kinematic and kinetic differences were observed between single- and dual-task that may have implications for functional movement assessments and injury risk research. More research examining how various cognitive and movement tasks interact to alter functional movement among pathological populations is warranted before clinical implementation. [ABSTRACT FROM AUTHOR]

Background Soccer is the world’s most popular sport for both men and women. Tests of athletic and functional performance are commonly used to assess physical ability and set performance goals. The Functional Movement Screen (FMS™) is a widely used seven-test battery developed by practitioners to provide interpretable measure of movement quality. The main objective of the present study was twofold, to analyze the relationship between FMS™ results from male and female soccer players and to compare their physical performance in different tests. Methods A total of twenty-eight semi-professional soccer players: fourteen male (age: 21.29 ± 1.64 years; weight: 70.66 ± 5.29 kg; height: 171.86 ± 5.35 cm; BMI: 20.90 ± 2.22 kg/m2) and fourteen females (age: 20.64 ± 1.98 years; weight: 63.44 ± 5.83 kg; height: 166.21 ± 12.18 cm; BMI: 23.02 ± 2.50 kg/m2) were recruited for this study. A paired sample t-test was used for determining differences as a repeated measures analysis. All the participants conducted the following tests: The Functional Movement Test (FMS™), 10-m linear sprint, 5-0-5 COD Test and Yo-Yo Intermittent Recovery Test—Level 1 (YYIRT Level 1). Results A t-test with data from 505 COD (change of direction) test showed significant differences between groups, p = 0.001, d = 1.11, revealing faster times in male soccer players (2.50 ± 0.19) in respect with female soccer players (2.70 ± 0.17). Crucially, a t-test with data from FMS did not reveal significant differences between groups. Multiple regression for V02max revealed significant effects (r = 0.55, r2 = 0.30, adjusted r2 = 0.24, F = 5.21, p = 0.04 and standard error = 2.20). On the other hand, multiple regression for 10-m sprint showed significant effects (r = 0.58, r2 = 0.33, adjusted r2 = 0.28, F = 5.98, p = 0.03). The impact of these factors on the correlation between FMS™ scores and physical performance measures can vary among individuals. Discussion/Conclusion This study demonstrates the necessity of utilizing and applying multiple field-based tests to evaluate the movement and capabilities of physical performance in sports. Crucially, consider individual variations and factors such as training background, fitness level, and sport-specific demands when interpreting the relationship between the FMS™ and physical performance in both sexes.

Purpose Human movement is essential for health and well-being. Understanding human movement is pivotal in physiotherapy, but also an important element of physiotherapy education. This review identified, critically appraised, and synthesized the available evidence on learning and teaching human movement in physiotherapy as perceived by students, therapists, and instructors. Methods The databases MEDLINE, CINAHL, ERIC, PsycINFO, MEDIC and FINNA, were searched. The search was conducted in March/April 2020 and updated in March 2022. The systematic review followed the JBI methodology for systematic reviews of qualitative evidence and was conducted in accordance with an a priori protocol. Results The overall quality of the 17 included studies was scored low on ConQual but dependability and credibility were rated as moderate. Four synthesized findings aggregated from 17 categories and 147 findings described the perceived significance of 1) being present in movement, 2) movement quality, 3) movement transfer, and 4) contextual factors for the learning or teaching of human movement in physiotherapy. Conclusion The synthesized findings indicate that the perceived significance of contextual factors, movement quality and transfer, and being present in movement should be considered in all learning and teaching of movement in physiotherapy. However, the evidence of the review findings was evaluated as low-level, which should be considered when applying these results to physiotherapy education or practice.

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes. [ABSTRACT FROM AUTHOR]

Many areas in computer science are facing the need to analyze, quantify and reproduce movements expressing emotions. This paper presents a systematic review of the intelligible factors involved in the expression of emotions in human movement and posture. We have gathered the works that have studied and tried to identify these factors by sweeping many disciplinary fields such as psychology, biomechanics, choreography, robotics and computer vision. These researches have each used their own definitions, units and emotions, which prevents a global and coherent vision. We propose a meta-analysis approach that cross-references and aggregates these researches in order to have a unified list of expressive factors quantified for each emotion. A calculation method is then proposed for each of the expressive factors and we extract them from an emotionally annotated animation dataset: Emilya. The comparison between the results of the meta-analysis and the Emilya analysis reveals high correlation rates, which validates the relevance of the quantified values obtained by both methodologies. The analysis of the results raises interesting perspectives for future research in affective computing. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNTs) on shape memory polymers result in densified, nano- to microscale wrinkles upon heat-induced shrinkage. These wrinkled CNT bundles can be transferred into soft materials similar to the human epidermis for extremely highly stretchable skin mountable strain sensors with a dynamic range of over 700%.

Total knee arthroplasty (TKA) surgery improves knee joint kinematics and kinetics during gait for most patients, but a lack of evidence exists for the level and incidence of improvement that is achieved. The objective of this study was to quantify patient-specific improvements in knee biomechanics relative to osteoarthritis (OA) severity levels. Seventy-two patients underwent 3-dimensional (3D) gait analysis before and 1 year after TKA surgery, as well as 72 asymptomatic adults and 72 with moderate knee OA. A combination of principal component analysis and discriminant analyses were used to categorize knee joint biomechanics for patients before and after surgery relative to asymptomatic, moderate, and severe OA. Post-TKA, 63% were categorized with knee biomechanics consistent with moderate OA, 29% with severe OA, and 8% asymptomatic. The magnitude and pattern of the knee adduction moment and angle (frontal plane features) were the most significant contributors in discriminating between pre-TKA and post-TKA knee biomechanics. Standard of care TKA improves knee biomechanics during gait to levels most consistent with moderate knee OA and predominately targets frontal plane features. These results provide evidence for the level of improvement in knee biomechanics that can be expected following surgery and highlight the biomechanics most targeted by surgery. [ABSTRACT FROM AUTHOR]

The progress in mobile technologies of Internet of Things (IoT) effectively supports the digitalization of sports training equipment. In this demo, we show an assistant mobile application (digital arbiter) to track the quality of exercise performance by the user. We experiment with sports training equipment produced by the MB Barbell company. The exercise is bench press from the chest or similar. The equipment has a mechanism to attach user’s smartphone. The problem is to estimate the lower and upper bounds for each round of execution. The measurement is based on data coming in real-time from the smartphone accelerometer. First, the mobile application can check the correctness as reaching the bounds. Second, the distance and time are estimated that the user has overcome during the exercise rounds. Since each user has own characteristics (e.g., arm length, grip method), individual calibration of the sensor system is needed before exercise performance on a given sports training equipment. Our experiments show that the mobile application has good accuracy and real-time performance, so the digital arbiter can be used as in individual daily physical activity or in mass sports events.

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality among study participants. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality among study participants. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, WiFi can effectively reduce the cost of radar sensors. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a rapid increase in applications of multibody system dynamics to the predictive simulation of human movement. Using predictive "what-if" human dynamic simulations that do not rely on experimental testing or prototypes, new medical interventions and devices can be developed more quickly, cheaply, and safely. In this paper, we provide a comprehensive review of research into the predictive multibody dynamic simulation of human movements, with applications in clinical practice, medical and assistive device design, sports, and industrial ergonomics. Multibody models of human neuromusculoskeletal systems are reviewed, including models of joints, contacts, and muscle forces or torques, followed by a review of simulation approaches that use optimal control methods and a cost function to predict human movements. Modelling and optimal control software are also reviewed, and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Movement dataset reviews exist but are limited in coverage, both in terms of size and research discipline. While topic-specific reviews clearly have their merit, it is critical to have a comprehensive overview based on a systematic survey across disciplines. This enables higher visibility of datasets available to the research communities and can foster interdisciplinary collaborations. We present a catalogue of 704 open datasets described by 10 variables that can be valuable to researchers searching for secondary data: name and reference, creation purpose, data type, annotations, source, population groups, ordinal size of people captured simultaneously, URL, motion capture sensor, and funders. The catalogue is available in the supplementary materials. We provide an analysis of the datasets and further review them under the themes of human diversity, ecological validity, and data recorded. The resulting 12-dimension framework can guide researchers in planning the creation of open movement datasets. This work has been the interdisciplinary effort of researchers across affective computing, clinical psychology, disability innovation, ethnomusicology, human-computer interaction, machine learning, music cognition, music computing, and movement neuroscience. [ABSTRACT FROM AUTHOR]