Human Motion Research Articles

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Fabrication and Performance of a Silver Nanowire Silk Conductive Fabric.

In this work, silk was selected as the substrate, and formic acid was utilized to create a rough texture on the silk. The conductive fabrics made from AgNWs and silk were created by applying multiple layers of silver nanowire dispersion onto the textured silk fabrics (SFs). The silk was immersed in a dispersion containing polydopamine (PDA), sericin (SE), tannic acid (TA), and silver nanowire under specific temperature conditions. After being cured at 120 °C, the three silver nanowire/silk fabrics (AgNWs/SFs), PDA/AgNWs/SF, SE/AgNWs/SF, and TA/AgNWs/SF, exhibited square resistances of 7.37, 540, and 200 Ω/sq, respectively. The method used to prepare the AgNW conductive SF is straightforward, resulting in fabrics that possess excellent thermal stability and resistance to washing. These fabrics also exhibit a range of useful properties, including conductivity, electrothermal capabilities, electrochemical functionality, human body sensing, hydrophobicity, and antimicrobial properties. These characteristics make them highly promising for various applications, such as human body motion detection, electronic textiles, electrothermal textiles, and antimicrobial applications.

Wearable Activity Recognition for Advancing Elderly Care with Modified Transformer Model

Technological devices such as smartphones can be utilized in tracking various human activities or movements through built-in accelerometers and gyroscopes. Data obtained from these inertial sensors can be utilized in various applications to assist people, including healthcare, human-computer interaction, and sports. As a result, further developments in the effective classification of time series data through machine and deep learning is highly valued and actively pursued. In this study, the transformer model, a deep learning architecture designed for sequential data such as natural language processing (NLP), has been utilized for analysis of time-series motion readings from wearable accelerometers. The transformer model in this study has been refined by incorporating a Long Short-Term Memory (LSTM) recurrent neural network (RNN) architecture. By leveraging the HAR70+ dataset with a wide range of activities, the modified transformer model in this study obtained a best accuracy of 95.85%, demonstrating that it can match the performance of state-of-the-art wearable activity recognition methods using Deep Neural Networks (DNN) and LSTM. Hence, the findings presented in this study suggest the future relevance of improved transformer or deep learning models to enhance the quality of life for seniors.

Study on Physical Fitness Index BMI and VO<sub>2</sub> Max of Physical Education Students

Physical activity is human body movement of any type, while physical education programs use physical activity to teach children how to improve and sustain an active lifestyle. One of the most crucial factors to think about when evaluating a subject's cardiorespiratory efficiency is their Physical Fitness Index (PFI). The risks involved in implementing BMI for adults also affect kids and teenagers. The term "maximal oxygen consumption capacity" (VO<sub>2</sub>max) refers to the body's capacity to consume oxygen in the skeletal muscle mitochondria at a maximum rate during vigorous full-body exercise. To investigate correlation body mass index, physical fitness index and maximal oxygen capacity (VO<sub>2</sub>max) among physical education students. The investigator was selected 36 BPED students (Male: 20 & Female: 16) from University of Kalyani, Kalyani, Nadia. The age range of subject from 19 to 25 years. To conduct this study measured body mass index though Quetelet index, physical fitness index through Harvard step test and maximal oxygen capacity (VO<sub>2</sub>max) through Queens College Step test were considered. Conclusion of this present study were no significant relationship between physical fitness index and body mass index of male, female students and students as a whole. Significantly relationship between VO<sub>2</sub> max and body mass index for female students but in case of male and student as a whole found no relationship. Positive relationship between physical fitness index and VO<sub>2</sub> max for male students and student as a whole but in case of female found no relationship.

A skin-inspired anisotropic multidimensional sensor based on low hysteresis organohydrogel with linear sensitivity and excellent robustness for directional perception

Human skin has complex sensors to perceive three-dimensional stimuli from the environment. Skin-inspired flexible sensors with multidimensional and directional perception are desirable for applications in intelligent robots and human–machine interaction. Herein, wearable multidimensional sensors have been fabricated by macroscopically assembling 3D-printed microstructured anisotropic organohydrogel sensory units through robust interfacial adhesion. The organohydrogel is crosslinked using microgels and starch microparticles through non-covalent interactions, and composited with short carbon fibers as conductive fillers. The physical network exhibits low hysteresis against 5000 cyclic loadings, which demonstrates excellent robustness in both mechanical properties and sensory performance. The organohydrogel precursor is printed into a cone-shaped sensor with a pressure sensitivity 50 times higher than that of its bulk counterpart. A sea cucumber epidermis-like microstructured sensor with abundant highly sensitive cone array is assembled with 3D-printed anisotropic gels with highly aligned carbon fibers inside, generating a multidimensional sensor capable of perceiving stimuli along X-, Y-, and Z-axis. The assembled sensor can directionally distinguish external forces and identify human motions. This study demonstrates a promising strategy to fabricate well-structured organohydrogel sensors for applications in electronic skin, biomimetic flexible electronics, and artificial intelligence.

Highly Stretchable Supercapacitor‐Type Multifunctional Self‐Powered Porous Electronic Skins

AbstractThe development of highly stretchable self‐powered electronic skins with a broad detection range is urgently needed but remains a great challenge. Herein, unprecedented highly stretchable, broad‐range‐response, supercapacitor‐type, one‐body, and self‐powered electronic skins are developed by assembling porous polyurethane/polypyrrole electrode, polyacrylic acid/polyacrylamide ionic gel electrolyte, and porous polyurethane/MXene electrode. The folded porous structure of the electrodes significantly enhances the stretchability, while the modulus‐gradient multilayer structure of the device effectively broadens the pressure detection range. The electronic skins combine self‐powered dynamic/static pressure sensing, ultrabroad detection range (20 Pa–3.5 MPa), ultrahigh stretchability up to 387%, excellent compression stability (5000 cycles under 195 kPa), and good stretching stability (500 cycles under 200% tensile strain). They have the capability of monitoring human body motions, physiological signals, and high pressures from motorcycle tires, as well as tactile sensing of robotic grippers. In addition, they can be applied for highly stretchable thermal management and electromagnetic shielding devices. This work provides a new strategy for the development of highly stretchable, broad‐range‐response, and multifunctional self‐powered wearable electronics and sensors.

Diffusion‐based Human Motion Style Transfer with Semantic Guidance

Abstract3D Human motion style transfer is a fundamental problem in computer graphic and animation processing. Existing AdaIN‐based methods necessitate datasets with balanced style distribution and content/style labels to train the clustered latent space. However, we may encounter a single unseen style example in practical scenarios, but not in sufficient quantity to constitute a style cluster for AdaIN‐based methods. Therefore, in this paper, we propose a novel two‐stage framework for few‐shot style transfer learning based on the diffusion model. Specifically, in the first stage, we pre‐train a diffusion‐based text‐to‐motion model as a generative prior so that it can cope with various content motion inputs. In the second stage, based on the single style example, we fine‐tune the pre‐trained diffusion model in a few‐shot manner to make it capable of style transfer. The key idea is regarding the reverse process of diffusion as a motion‐style translation process since the motion styles can be viewed as special motion variations. During the fine‐tuning for style transfer, a simple yet effective semantic‐guided style transfer loss coordinated with style example reconstruction loss is introduced to supervise the style transfer in CLIP semantic space. The qualitative and quantitative evaluations demonstrate that our method can achieve state‐of‐the‐art performance and has practical applications. The source code is available at https://github.com/hlcdyy/diffusion-based-motion-style-transfer.

Preparation and application of organic hydrogels incorporating polyacrylamide/sodium alginate/DMSO for enhanced anti-drying, anti-freezing, and self-healing properties

The double-network hydrogel comprises two asymmetrically structured cross-linked networks, demonstrating exceptional mechanical properties and possessing significant potential for application in the field of flexible sensors. However, conventional hydrogels primarily disperse in water as a medium and continuously lose moisture in natural environments, rendering them incapable of retaining hydration over extended periods and unsuitable for utilization under low-temperature conditions. In this study, we devised an organic hydrogel based on polyacrylamide (PAM), sodium alginate (SA), and dimethyl sulfoxide (DMSO). Through hydrogen bonding interactions among DMSO molecules, polyacrylamide, and sodium alginate, this organic hydrogel not only exhibits anti-drying and anti-freezing properties but also demonstrates self-healing characteristics. Moreover, the organic hydrogel showcases remarkable mechanical properties and conductivity capabilities that render it suitable for constructing flexible strain sensors to monitor human movements such as finger bending, elbow flexion, knee joint bending, and ankle extension. Additionally, the sensor exhibits excellent stability and durability.

Heat and mass transfer analysis on peristaltic transport of PTT fluid with wall properties

The current study is intended to examine the effect of heat and mass transfer on the peristaltic propulsion of the Pan-Thien-Tanner fluid in a cylindrical tube with wall properties. The complexity of the system is reduced by long wavelength and low Reynolds number strategy. The physical behavior of the peristaltic motion of the PTT fluid is explained and elucidated graphically for several values of various parameters connected with this flow problem. It has been revealed that raising the Source/Sink parameter values and the Weissenberg number results in a higher velocity profile. The outcomes embrace the key to understanding the human organs' physiological fluid motion, such as the chime moment in the small intestine and esophagus.

Wood Cell Wall Nanoengineering toward Anisotropic, Strong, and Flexible Cellulosic Hydrogel Sensors.

Achieving highly ionic conductive hydrogels from natural wood remains challenging owing to their insufficient surface area and low number of active sites on the cell wall. This study proposes a viable strategy to design a strong and anisotropic wood-based hydrogel through cell wall nanoengineering. By manipulating the microstructure of the wood cell wall, a flexible cellulosic hydrogel is achieved through Schiff base bonding via the polyacrylamide and cellulose molecular chains. This results in excellent flexibility and mechanical properties of the wood hydrogel with tensile strengths of 22.3 and 6.1 MPa in the longitudinal and transverse directions, respectively. Moreover, confining aqueous salt electrolytes within the porous structure gives anisotropic ionic conductivities (19.5 and 6.02 S/m in the longitudinal and transverse directions, respectively). The wood-based hydrogel sensor has a favorable sensitivity and a stable working performance at a low temperature of -25 °C in monitoring human motions, thereby demonstrating great potential applications in wearable sensor devices.

Sustainable and high performance MXene hydrogel with interlocked structure for machine learning-facilitated human-interactive sensing

Wearable epidermal electronics fabricated from MXene-based conductive hydrogels have attracted considerable interest because of their promising prospects in real-time health monitoring and human–computer interaction sensing. However, the development of a sustainable hydrogel with exceptional mechanical toughness, high conductivity, and self-adhesion is crucial to reliably capture signals through polymer network designs that establish robust interactions between MXene nanosheets and network substrates while minimizing electronic waste generated by discarded sensors. This study presents the fabrication of a sustainable conductive MXene-based dual-network hydrogel through skillful assembly of MXene nanosheets, β-cyclodextrin with azobenzene-modified polyacrylic acid chains, and poly (vinyl alcohol) polymer networks. The hydrogel demonstrates exceptional mechanical properties (strength at breaking ∼ 625 kPa, elongation at break ∼ 630 %) attributed to the presence of numerous hydrogen bonds and host–guest interactions, along with superior electrical conductivity (23.65 S/cm) and self-adhesion. The hydrogel demonstrates exceptional sensitivity across a wide range of strains, enabling the detection of various human movements and achieving successful applications in handwriting recognition and sign language translation. Under acidic condition and UV irradiation, the disassembly of AZO and β-CD as well as the untangling of PVA chains entanglement facilitate the release and recycling of MXene. This study presents a technological platform that holds promise for developing conductive hydrogels with enhanced sensing capabilities, catering to the requirements of stretchable electronic skin and intelligent robotic systems.

A highly stretchable and self-adhesive cellulose complex hydrogels based on PDA@Fe3+ mediated redox reaction for strain sensor

As the application of conductive hydrogels in the field of wearable smart devices is gradually deepening, a variety of hydrogel sensors with high mechanical properties, strong adhesion, fast self-healing, and excellent conductivity are emerging. However, it is still a great challenge to manufacture hydrogel sensors combining multiple properties. Herein, we leveraged the dynamic redox reaction occurring between polydopamine (PDA) and Fe3+ to induce ammonium persulfate (APS) to generate free radicals, thereby initiating the copolymerization of hydroxyethyl methacrylate (HEMA) and acrylic acid (AA) monomers. Then, polypyrrole-encapsulated cellulose nanofibers (PPy@CNF) and carboxymethylcellulose (CMC) were incorporated as conductive reinforced nanofillers and interpenetrating network skeleton. The obtained hydrogel cross-linked through reversible metal-ligand bonds, π-π stacking and abundant hydrogen bonding demonstrated great mechanical properties (strength 240.4 kPa, strain 1175 %) and self-healing ability (88.96 %). Particularly, the gel displayed ultrahigh durability and skin adhesive ability (75 kPa after 10 cycles), surpassing previous skin adhesion hydrogels. Furthermore, through the synergistic conductive effect of PPy@CNF and Fe3+, the prepared hydrogel sensor possessed high sensitivity (GF = 1.89) with a wide sensing range (~1000 %), which could realize the human body's daily motion detection, and had a promising application in flexible wearable electronics.

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review.

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

Cation-π Interactions Based Conductive Hydrogels with Slide-Ring Structure Toward Super Long-Time in-air/Underwater Linear Sensing and Communication.

Conductive hydrogels (CHs) are attracted more attention in the flexible wearable sensors field, however, how to stably apply CHs underwater is still a big challenge. In order to achieve the usage of CHs in aquatic environments, the integrated properties such as water retention ability, resistance to swelling, toughness, adhesiveness, linear GF sensing, and long-term usage are necessary to consider, but rarely reported in the previous reports. This paper proposes CHs prepared using cationic and aromatic monomers along with polyrotaxanes-based crosslinkers. Due to the intermolecular cation-π interactions and topological slide-ring-based polyrotaxanes, the CHs exhibit good mechanical performance, adhesive nature, and anti-swelling properties. The presence of slide-ring-based topological architecture effectively mitigates stress concentration. Additionally, the encapsulation of PA allows CHs to maintain functionality even after 240 days of direct placement at room temperature. Notably, the designed CHs exhibit linear sensitivity in detecting land/underwater human motions, and serve as Morse code signal transmitters for information transmission. Thus, the designed CHs may have broad applications in the underwater wearable sensors field.

CP decomposition-based algorithms for completion problem of motion capture data

Motion capture (MoCap) technology is an essential tool for recording and analyzing movements of objects or humans. However, MoCap systems frequently encounter the challenge of missing data, stemming from mismatched markers, occlusion, or equipment limitations. Recovery of these missing data is imperative to maintain the reliability and integrity of MoCap recordings. This paper introduces a novel application of the tensor framework for MoCap data completion. We propose three completion algorithms based on the canonical polyadic (CP) decomposition of tensors. The first algorithm utilizes CP decomposition to capture the low-rank structure of the tensor. However, relying only on low-rank assumptions may be insufficient to deal with complex motion data. Thus, we propose two modified CP decompositions that incorporate additional information, SmoothCP and SparseCP decompositions. SmoothCP integrates piecewise smoothness prior, while SparseCP incorporates sparsity prior, each aiming to improve the accuracy and robustness of MoCap data recovery. To compare and evaluate the merit of the proposed algorithms over other tensor completion methods in terms of several evaluation metrics, we conduct numerical experiments with different MoCap sequences from the CMU motion capture dataset.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Emergence of social phases in human movement.

Recent empirical studies have found different thermodynamic phases for collective motion in animals. However, such a thermodynamic description of human movement remains unclear. Existing studies of traffic and pedestrian flows have primarily focused on relatively high-speed mobility data, revealing only a fluidlike phase. This focus is partly because the parameter space of low-speed movement, which is governed predominantly by pairwise social interaction, remains largely uncharted. Here, we used ultrawideband radio frequency identification (UWB-RFID) technology to collect high-resolution spatiotemporal data on movements in four different classroom and playground settings. We observed two unique social phases in children's movements: a gaslike phase of free movement and a liquid-vapor coexistence phase characterized by the formation of small social groups. We also developed a simple statistical physics model that can reproduce different empirically observed phases. The proposed UWB-RFID technology can also be used to study the dynamics of active matter systems, including animal behavior, coordinating robotic swarms, and monitoring human interactions within complex systems, potentially benefiting future research in social physics.

Identifying Phases in Low-Speed Human Movement

Identifying Phases in Low-Speed Human Movement

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

Molecular and Cellular Mechanisms of Motor Circuit Development.

Motor circuits represent the main output of the central nervous system and produce dynamic behaviors ranging from relatively simple rhythmic activities like swimming in fish and breathing in mammals to highly sophisticated dexterous movements in humans. Despite decades of research, the development and function of motor circuits remain poorly understood. Breakthroughs in the field recently provided new tools and tractable model systems that set the stage to discover the molecular mechanisms and circuit logic underlying motor control. Here, we describe recent advances from both vertebrate (mouse, frog) and invertebrate (nematode, fruit fly) systems on cellular and molecular mechanisms that enable motor circuits to develop and function and highlight conserved and divergent mechanisms necessary for motor circuit development.