Human Motion Research Articles

Evaluating the Effect of Human Activity on Air Quality using Bayesian Networks and IDW Interpolation

As the world's human population continues to grow, it's crucial to also consider sustainability when addressing the need for living standards. While global warming has multiple causes, air pollution significantly contributes to it. The Air Quality Index (AQI) is a tool that determines the air's quality in certain areas by evaluating six primary pollutants, including sulphur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), carbon monoxide (CO), and particulate matter (PM2.5 and PM10). The AQI ranges from "good" to "severe," with scores ranging from 0 to 500, indicating the level of pollution. The AQI is also influenced by human activity in the environment. This study utilizes real-time data from the TNPCB (Tamil Nadu Pollution Control Board) on Madurai's AQI at three locations over the year 2021, during the COVID-19 pandemic. The Bayesian network is used to illustrate how human movement impacts the Air Quality Index through probabilistic analysis. Additionally, an IDW Interpolation chart is presented to demonstrate the impact of human activity on the AQI levels at the three stations.

From Movement to Meaning: Real-time Detection of Behavioural Patterns in Human Psychology using Deep Learning and Time Series Sequence

Understanding human behaviour and psychology is complex and involves various fields, including cognitive science, neurology, and artificial intelligence (AI). In this study, we introduce a cutting-edge system that detects both manual (like hand gestures) and non-manual (such as facial expressions or body movements) features in real time, using advanced deep learning techniques and time-series analysis. This system translates these human movements into text, providing a powerful tool to analyse subtle behavioural and psychological signals. By processing real-time data, our system significantly advances psychology by detecting small expressions, involuntary gestures, and behaviour patterns often associated with psychological and neurological conditions. For instance, it can help identify repetitive actions or atypical gestures in people with autism spectrum disorder (ASD), which are critical for early diagnosis. It can also track signs of neurodegenerative diseases like Parkinson’s or Alzheimer’s, detecting tremors, posture changes, or facial expressions, and offer insights for early intervention

Alternatives to Friction Coefficient: Fine Touch Perception Relies on Frictional Instabilities.

Fine touch perception is often correlated to material properties and friction coefficients, but the inherent variability of human motion has led to low correlations and contradictory findings. Instead, we hypothesized that humans use frictional instabilities to discriminate between objects. We constructed a set of coated surfaces with physical differences which were imperceptible by touch but created different types of instabilities based on how quickly a finger is slid and how hard a human finger is pressed during sliding. We found that participant accuracy in tactile discrimination most strongly correlated with formations of steady sliding, and response times negatively correlated with stiction spikes. Conversely, traditional metrics like surface roughness or average friction coefficient did not predict tactile discriminability. Identifying the central role of frictional instabilities as an alternative to using friction coefficients should accelerate the design of tactile interfaces for psychophysics and haptics.

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

AbstractVirtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training. This paper proposes a rehabilitation robot system. The system integrates a VR environment, the exoskeleton entity, and research on rehabilitation assessment metrics derived from surface electromyographic signal (sEMG). Employing more realistic and engaging virtual stimuli, this method guides patients to actively participate, thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation. Furthermore, this study introduces a muscle activation model that merges linear and non‐linear states of muscle, avoiding the impact of non‐linear shape factors on model accuracy present in traditional models. A muscle strength assessment model based on optimised generalised regression (WOA‐GRNN) is also proposed, with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%, serving as critical assessment indicators for the effectiveness of rehabilitation. Finally, the system is preliminarily applied in human movement experiments, validating the practicality and potential effectiveness of VR‐centred rehabilitation strategies in medical recovery.

Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population.

Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems.

A Highly Sensitive Strain Sensor with Self-Assembled MXene/Multi-Walled Carbon Nanotube Sliding Networks for Gesture Recognition

Flexible electronics is pursuing a new generation of electronic skin and human–computer interaction. However, effectively detecting large dynamic ranges and highly sensitive human movements remains a challenge. In this study, flexible strain sensors with a self-assembled PDMS/MXene/MWCNT structure are fabricated, in which MXene particles are wrapped and bridged by dense MWCNTs, forming complex sliding conductive networks. Therefore, the strain sensor possesses an impressive sensitivity (gauge factor = 646) and 40% response range. Moreover, a fast response time of 280 ms and detection limit of 0.05% are achieved. The high performance enables good prospects in human detection, like human movement and pulse signals for healthcare. It is also applied to wearable smart data gloves, in which the CNN algorithm is utilized to identify 15 gestures, and the final recognition rate is up to 95%. This comprehensive performance strain sensor is designed for a wide array of human body detection applications and wearable intelligent systems.

Investigation of a Magnetic Levitation Architecture with a Ferrite Core for Energy Harvesting

This work presents the development of a magnetic levitation system with a ferrite core, designed for electromagnetic energy harvesting from mechanical vibrations. The system consists of a fixed enamel-coated copper coil and five neodymium-iron-boron permanent magnets housed within a PVC spool. To enhance magnetic flux concentration, a manganese-zinc ferrite (Mn-Zn) ring was employed within the spool. Experimental tests were conducted at frequencies up to 20 Hz, demonstrating the device’s potential for harvesting energy from small vibrations, such as those generated by human biomechanical movements, achieving operating voltages up to 3 V. Additionally, the architecture is scalable for larger systems and allows for the integration of multiple transducers without magnetic field interference, independent of the frequency or excitation phase of each transducer.

AI-Assisted Automated Identification of Human Activities Using Ficus religiosa Leaf-Based Triboelectric Nanogenerator.

Movement monitoring and effective identification of different actions are the keys that help in fitness services, health status, clinical studies, etc. In this technological era, Internet of Things (IoT) technologies, including smart wireless devices and sensors, are very effectively used for monitoring human activities, but the demand for sustainable and green power sources is a crucial issue with these devices. Triboelectric nanogenerators (TENG) are proven to be promising applications in these devices because they harvest energy from the surrounding environment and eliminate the use of batteries as power sources. As a green energy source, this study emphasizes the fabrication of biodegradable materials-based TENGs, which are eco-friendly and are related to clean and green energy as per the UN's sustainable development goals SDG 7 (affordable and clean energy). In the present work, a natural Ficus religiosa leaf (FRL) of the F. religiosa tree is used in designing and fabricating a TENG (FRL-TENG). Also, an approach is discussed to compare the performance of FRL-TENG with TENGs fabricated from other waste biodegradable materials such as garlic tunic, onion tunic, and eggshell membrane (ESM). During the experimental study, it is observed that the FRL-based TENG produced maximum voltage in comparison to other material combinations selected in this study. The generated electric output from these TENG combinations is also used to power an array of tens of green-light-emitting diodes (LEDs). Furthermore, this paper also proposes the use of FRL-TENG as a wearable sensor to collect information and monitor the physical activities of the user, viz., walk, jump, and run. To recognize the movement status, the FRL-TENG sensor is integrated with an extra randomized tree-based machine learning model for accurately distinguishing the user's three activities with an accuracy of 96%. The work showcases an innovative approach to encourage customized uses of TENG sensors in human motion monitoring and permits the development of intelligent, self-powered systems for new applications.

High-Performance Triboelectric Nanogenerator with Double-Side Patterned Surfaces Prepared by CO2 Laser for Human Motion Energy Harvesting

The triboelectric nanogenerator (TENG) has demonstrated exceptional efficiency in harvesting diverse forms of mechanical energy and converting it into electrical energy. This technology is particularly valuable for powering low-energy electronic devices and self-powered sensors. Most traditional TENGs use single-sided patterned friction pairs, which restrict their effective contact area and overall performance. Here, we propose a novel TENG that incorporates microwave patterned aluminum (MC-Al) foil and microcone structured polydimethylsiloxane (MC-PDMS). This innovative design utilizes two PMMA molds featuring identical micro-hole arrays ablated by a CO2 laser, making it both cost-effective and easy to fabricate. A novel room imprinting technique has been employed to create the micromorphology of aluminum (Al) foil using the PMMA mold with shallower micro-hole arrays. Compared to TENGs with flat friction layers and single-side-patterned friction layers, the double-side-patterned MW-MC-TENG demonstrates superior output performance due to increased cone deformation and contact area. The open-circuit voltage of the MW-MC-TENG can reach 141 V, while the short-circuit current can attain 71.5 μA, corresponding to a current density of 2.86 µA/cm2. The power density reaches 1.4 mW/cm2 when the resistance is 15 MΩ, and it can charge a 0.1 μF capacitor to 2.01 V in 2.28 s. In addition, the MW-MC-TENG can function as an insole device to harvest walking energy, power 11 LED bulbs, monitor step speed, and power a timer device. Therefore, the MW-MC-TENG has significant application potential in micro-wearable devices.

Flexible Magnetoelectric Fiber for Self-Powered Human-Machine Interactive.

Flexible large strain sensors are an ideal choice for monitoring human motion, but the current use of flexible strain gauges is hindered by the need for external power sources and long-term operation requirements. Fiber-based sensors, due to their high flexibility, excellent breathability, and the ease with which they can be embedded into everyday clothing, have the potential to become a novel type of wearable electronic device. This paper proposes a flexible self-powered strain sensing material based on the electromagnetic induction effect, composed of a uniform mixture of Ecoflex and Nd2Fe14B, which has good skin-friendliness and high stretchability of over 100%. The voltage output of the magnetoelectric composite fiber remains stable over 5000 stretch-release cycles, reaching up to 969 μV. Based on this novel sensing material, a remote smart car control scheme for a human-machine interaction system was designed, enabling real-time gesture interaction.

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Abstract Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Engaging in Interactive Book Reading: General Warming Up Before Exercising for Children

Many parents are encouraging their children to exercise as a result of the COVID- 19 pandemic. Many others, on the other hand, do not warm up before participating in any sport. This is dangerous since it increases the risk of sprained muscles, cramping, and other problems. Warming up is crucial before exercising, yet many people are still unaware of the risks of not doing so. The objectives for this project is to create an interactive sports book. Unlike traditional books, this interactive book will use lift-the-flap, slide, or spin-the-wheel strategies to make learning more entertaining for children. To promote the socialization of young people through sports. As well as teaching children how to warm up with the assistance of adults. The method used is qualitative method. The results is It is clear from the in-depth interview and observation that many people still ignore the importance of warming up. Several children revealed in interviews that their teacher did not teach them how to warm up, and one of them learned about warming up via YouTube. Which means that the video that children see isn't always appropriate for their age group's warming up movement. According to the findings of the interviews, manipulating a scenario and performing fundamental movements is an effective technique to teach preschool children to warm up. The 7 Primal Movement Patterns is a great warm-up for children aged 3- 5 years. The seven basic movements are twist, push, pull, bend, squat, lunge, and gait. They represent the most basic breakdown of all human movements.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Body Movement Mirroring and Synchrony in Human–Robot Interaction

This review article provides an overview of papers that have studied body movement mirroring and synchrony within the field of human-robot interaction. The papers included in this review cover system studies, which focus on evaluating the technical aspects of mirroring and synchrony robotic systems, and user studies, which focus on measuring particular interaction outcomes or attitudes towards robots expressing mirroring and synchrony behaviors. We review the papers in terms of the employed robotic platforms and the focus on parts of the body, the techniques used to sense and react to human motion, the evaluation methods, the intended applications of the human-robot interaction systems and the scenarios utilized in user studies. Finally, challenges and possible future directions are considered and discussed.

Highly sensitive and structure stable polyvinyl alcohol hydrogel sensor with tailored free water fraction and multiple networks by reinforcement of conductive nanocellulose

The wearable composite hydrogel sensors with high stretchability have attracted much attention in recent years, while the traditional hydrogels have weak molecular (chain) interaction and contain a lot of free water, leading to poor mechanical properties, unstable environmental tolerance and sensing ability. Herein, a novel ice crystal extrusion-crosslinking strategy is used to obtain polyvinyl alcohol (PVA) hydrogel with conductive nanocellulose-poly (3,4-ethylenedioxythiophene) (CNC-PEDOT) as skeleton network, sodium alginate (SA) and Ca2+ as tough segment of multi-bonding network. This strategy synergistically enhanced the interaction of hydrogen bonds and calcium (Ca2+) ion chelation within the hydrogel, building highly sensitive and stable multiple tough-elastic networks. Therefore, the optimal hydrogel sensor (PVA/SA-CP45) shows good structural stability, robust mechanical performance, excellent compress (Sensitivity = 68.7), stretching sensitivity (Gauge factor = 4.16), ultra-wide application range (−105–60 °C), fast response/relaxation time and outstanding dynamic durability with 6000 stretching-releasing cycles. Especially, it can give good sensing performance for omnidirectional monitoring of human motion and weak signals. Moreover, it was also designed into multifunctional sensing systems for gait guidance of model training and real-time monitoring ammonia gas for food preservation and public environmental safety, demonstrating great potential in flexible sensors devices for health monitoring.

One-Dimensional Flexible Capacitive Sensor with Large Strain and High Stability for Human Motion Monitoring.

Flexible capacitive sensors have attracted the attention of researchers owing to their simple structure, ease of realization, and wearability. Currently, flexible capacitive sensors mainly have three-dimensional and two-dimensional structures, which are subject to several limitations in their applications. A low-cost, high-efficiency, and continuously processable process was used to wrap nylon DTY (PA) filaments on the surface of silver-coated nylon (SCN) core yarns and impregnate them with waterborne polyurethane (WPU) to obtain SCN/PA/WPU composite yarns, which were then utilized in the design of SCN/PA/WPU for the preparation of one-dimensionally structured flexible capacitive sensors. The morphology and mechanical properties of the SCN core yarn, SCN/PA wrapped yarn, and SCN/PA/WPU composite yarn were characterized. The strain-sensing performance of the sensor was analyzed, and the sensor was used to monitor human physiological activities. The sensor exhibited excellent strain capacitance sensing performance with a strain range of up to 140%. With a gauge factor of 0.66 at 10% tensile strain, it can detect strains as low as 1% and has good repeatability, withstanding more than 3200 tensile-unload cycles at 80% strain. The one-dimensional structure sensor can be used to monitor the large-scale movements of joints and muscles in various parts of the human body and the physiological signals of tiny human movements, such as breathing, coughing, and facial expressions, which have potential applications in the fields of sports monitoring and smart wearable.

New insights into icephobic material assessment: Introducing the human motion–inspired automated apparatus (HMA)

The impact of winter on exposed structures and transportation poses significant dangers and costs to various industries, particularly the transportation sector. Icephobic surfaces are currently being developed to reduce winter-related impacts. Creating such surfaces requires considering various factors, including reducing and preventing ice accumulation, significantly decreasing ice adhesion, and/or delaying water solidification. Although established methods such as centrifugal force and push-off tests exist for measuring ice adhesion, the results may not always correlate or offer the needed information for specific applications. To better assess icephobic properties, we have developed a novel device called the human motion–inspired automated apparatus (HMA) that mimics manual de-icing performed by humans in a scraping mode. The primary objective of the HMA is to emulate human removal of ice-covered surfaces, providing a more realistic evaluation of icephobic properties according to the ease of ice removal. This apparatus aims to revolutionize icephobic material assessment by offering improved accuracy, repeatability, and versatility in testing. We developed a unique procedure using low icing conditions, which are challenging to evaluate using conventional methods, and assessed four surfaces: aluminum as a reference, an epoxy-based hydrophilic coating, a hydrophobic silicone elastomer coating, and a hydrophobic epoxy–silicone coating. Our HMA characterizes surfaces according to several crucial parameters, including the normal force required to initiate ice scraping removal, the maximum force achieved, the angle of attack, and the equivalent force, all consistent with validation tests conducted by humans. Among the evaluated surfaces, the silicone coating required the lowest normal force, and the epoxy–silicone coating had the lowest maximum and equivalent forces. Our HMA results align well with validation tests conducted by humans. The HMA enables evaluating various critical icing conditions and promises a broad range of applications in research and development.

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials.

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.