Human Motion Research Articles

Evaluating the impact of human movement-induced airflow on particle dispersion: A novel real-time validation using IoT technology

Human movement significantly influences local airflow and particle dispersion in indoor environments. Therefore, this study integrates the dynamics of human walking into the analysis of airborne infection risks in a positive pressure isolation ward designed for the protection of immunocompromised patients. Real-time data collection was performed using an anemometer and IoT-based PM sensors to measure airflow velocities and particulate matter (PM) concentrations within a full-scale experimental chamber. Computational Fluid Dynamics (CFD) was used for the numerical simulations, and a User-Defined Function (UDF) code simulated the translational movement of a manikin. The results demonstrated strong agreement with the experimental data, thereby validating the airflow turbulence and Lagrangian-based Discrete Phase Model (DPM) in predicting the airflow velocities and particle transport. The study revealed that human walking substantially enhanced particle dispersion distance by 10-fold compared to static conditions, primarily attributed to intensified air mixing induced by the body movement. Furthermore, the CFD analysis underscored that the direction of walking plays a crucial role in airborne transmission. Specifically, walking away from a patient did not elevate infection risk, whereas approaching a patient significantly increased particle deposition in the patient-occupied region. This study highlights the critical need to consider both movement patterns and directional flow in managing airborne infection risks, contributing to the development of more effective infection control strategies in healthcare settings.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

A robust myoelectric pattern recognition framework based on individual motor unit activities against electrode array shifts

Background and objectiveElectrode shift is always one of the critical factors to compromise the performance of myoelectric pattern recognition (MPR) based on surface electromyogram (SEMG). However, current studies focused on the global features of SEMG signals to mitigate this issue but it is just an oversimplified description of the human movements without incorporating microscopic neural drive information. The objective of this work is to develop a novel method for calibrating the electrode array shifts toward achieving robust MPR, leveraging individual motor unit (MU) activities obtained through advanced SEMG decomposition. MethodsAll of the MUs from decomposition of SEMG data recorded at the original electrode array position were first initialized to train a neural network for pattern recognition. A part of decomposed MUs could be tracked and paired with MUs obtained at the original position based on spatial distribution of their MUAP waveforms, so as to determine the shift vector (describing both the orientation and distance of the shift) implicated consistently by these multiple MU pairs. Given the known shift vector, the features of the after-shift decomposed MUs were corrected accordingly and then fed into the network to finalize the MPR task. The performance of the proposed method was evaluated with data recorded by a 16 × 8 electrode array placed over the finger extensor muscles of 8 subjects performing 10 finger movement patterns. ResultsThe proposed method achieved a shift detection accuracy of 100 % and a pattern recognition accuracy approximating to 100 %, significantly outperforming the conventional methods with lower shift detection accuracies and lower pattern recognition accuracies (p < 0.05). ConclusionsOur method demonstrated the feasibility of using decomposed MUAP waveforms’ spatial distributions to calibrate electrode shift. This study provides a new tool to enhance the robustness of myoelectric control systems via microscopic neural drive information at an individual MU level.

Flexible and robust polypyrrole/cross-linked collagen sponge with collagen aggregates as building blocks for piezoresistive sensing

The polypyrrole/cross-linked collagen sponge (PPy/CCS) was synthesized through in-situ polymerization of PPy nanoparticles onto the collagen skeleton composed of mesoscopic collagen aggregates (CAs) as building blocks, which was employed as a sensing material. The structure and morphology of PPy/CCS were characterized using scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal analysis (TA). The conductivity and mechanical properties of PPy/CCS can be regulated by adjusting the polymerization conditions. The PPy/CCS treated with glycerin aqueous solution exhibits exceptional recovery performance under 10,000 compression stress cycles, with a short response time of 420 ms and recovery time of 400 ms, along with high sensitivity of 1.13 kPa−1 within a control pressure range of 0–43 kPa. The assembled PPy/CCS-based sensors enable monitoring of human movement behavior and achieving multi-touch control, thereby demonstrating significant potential in flexible wearable devices and human–machine interaction.

Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.

Shape optimization of a non-uniform piezoelectric bending beam for human knee energy harvester

Abstract Scavenging energy from the human body to provide a sustainable source for electronic devices has gained significant attention. Recently, scientists have focused on harnessing biomechanical energy from human motion. This study was dedicated to developing and optimizing a non-uniform piezoelectric bending beam-based human knee energy harvester. The bimorph non-uniform piezoelectric bending beam consisted of a non-uniform carbon fiber substrate and piezoelectric macro fiber composites. Compared to the uniform piezoelectric bending beam, the non-uniform piezoelectric beam can optimize the shape to improve the average strain, thus improving the energy harvesting efficiency. In this study, eight shape functions, including ellipse, sin, tanh, exponential function, parabola, trigonometric line, and bell curves, were investigated and optimized. The bell curve bending beam was selected and fabricated due to its good performance. Then, a benchmark platform was developed to test the deflection curve and reaction force when the nonuniform bending beam was compressed. Finally, to validate the design, experimental testing on three subjects was conducted when they were equipped with the harvester and walked on a treadmill. Testing results indicated that the non-uniform bending beam-based energy harvester can improve the energy harvesting efficiency by 28.57% compared to the uniform beam-based energy harvester. The output power can reach 18.94 mW when walking at 7.0 km h−1.

Flexible Tactile Sensors with Gradient Conformal Dome Structures.

The trade-off between high sensitivity and wide detection range remains a challenge for flexible capacitive pressure sensors. Gradient structure can provide continuous deformation and lead to a wide sensing range. However, it simultaneously augments the distance between two electrodes, which diminishes the variation in the relative distance and results in a decreased sensitivity. Herein, a conformal design is introduced into the gradient structure to construct a flexible capacitive pressure sensor. The gradient conformal dome structure is fabricated by a simple reverse dome adsorption process. Taking advantage of the progressive deformation behavior of the gradient dielectric, and the significant improvement of relative distance variation between two electrodes from the conformal design, the sensor achieves a sensitivity of 0.214 kPa-1 in an ultrabroad linear range up to 200 kPa. It maintains high-pressure resolution under the preload of 10 and 100 kPa. Benefiting from the rapid response and excellent repeatability, the sensor can be used for physiological monitor and human motion detection, including arterial pulse, joint bending, and motion state. The gradient conformal design strategy may pave a promising avenue to develop pressure sensors with high sensitivity and wide linear range.

Retraction Note: Human motion image detection and tracking method based on gaussian mixture model combined with light sensor

Retraction Note: Human motion image detection and tracking method based on gaussian mixture model combined with light sensor

PNIPAAm-based temperature responsive ionic conductive hydrogels for flexible strain and temperature sensing

Conductive hydrogels have received much attention in the field of flexible wearable sensors due to their outstanding flexibility, conductivity, sensitivity and excellent compatibility. However, most conductive hydrogels mainly focus on strain sensors to detect human motion and lack other features such as temperature response. Herein, we prepared a strain and temperature dual responsive ionic conductive hydrogel (PPPNV) with an interpenetrating network structure by introducing a covalent crosslinked network of N-isopropylacrylamide (NIPAAm) and 1-vinyl-3-butylimidazolium bromide (VBIMBr) into the skeleton of the hydrogel composed of polyvinylalcohol (PVA) and polyvinylpyrrolidone (PVP). The PPPNV hydrogel exhibited excellent anti-freezing properties (−37.34 °C) and water retention with high stretchability (∼930 %) and excellent adhesion. As a wearable strain sensor, the PPPNV hydrogel has good responsiveness and stability to a wide range of deformations and exhibits high strain sensitivity (GF=2.6) as well as fast response time. It can detect large and subtle body movements with good signal stability. As wearable temperature sensors, PPPNV hydrogels can detect human physiological signals and respond to temperature changes, and the volumetric phase transition temperature (VPTT) can be easily controlled by adjusting the molar ratio of NIPAAm to VBIMBr. In addition, a bilayer temperature-sensitive hydrogel was prepared with the temperature responsive hydrogel by two-step synthesis, which shows great promising applications in temperature actuators.

Monomer Trapping Synthesis Toward Dynamic Nanoconfinement Self-healing Eutectogels for Strain Sensing.

The rapid advancement in attractive platforms such as biomedicine and human-machine interaction has generated urgent demands for intelligent materials with high strength, flexibility, and self-healing capabilities. However, existing self-healing ability materials are challenged by a trade-off between high strength, low elastic modulus, and healing ability due to the inherent low strength of noncovalent bonding. Here, drawing inspiration from human fibroblasts, a monomer trapping synthesis strategy is presented based on the dissociation and reconfiguration in amphiphilic ionic restrictors (7000-times volume monomer trapping) to develop a eutectogel. Benefiting from the nanoconfinement and dynamic interfacial interactions, the molecular chain backbone of the formed confined domains is mechanically reinforced while preserving soft movement capabilities. The resulting eutectogels demonstrate superior mechanical properties (1799% and 2753% higher tensile strength and toughness than pure polymerized deep eutectic solvent), excellent self-healing efficiency (>90%), low tangential modulus (0.367MPa during the working stage), and the ability to sensitively monitor human activities. This strategy is poised to offer a new perspective for developing high strength, low modulus, and self-healing wearable electronics tailored to human body motion.

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch).

Wide-response-range and high-sensitivity piezoresistive sensors with triple periodic minimal surface (TPMS) structures for wearable human-computer interaction systems

High-performance pressure sensors are garnering interest in human-computer interaction technology, wearable devices, and bionic electronic skin development. However, highly sensitive sensors frequently have a limited response range. In this work, we developed composites with outstanding conductive network structures through the synergistic effect of transition metal carbides (MXene) and multi-walled carbon nanotubes (MWCNTs). Additionally, pressure sensors with various TPMS structures were prepared using innovative parametric design and Fused Deposition Molding (FDM) printing. Due to the stable synergistic conductive network and distinctive curved surface structure, the sensors exhibit exceptional sensing performance. This includes high sensitivity ranging from 4.67 MPa−1 to 7.03 MPa−1 (within the range of 0–0.1 MPa), a broad operating range (maximum 10 MPa), rapid response and recovery times (326 ms/193.4 ms), and long-term fatigue resistance (over 10,000 s cycles). By integrating mechanical properties, sensing properties, and finite element simulations, we analyzed the mechanism underlying the impact of various TPMS pore structures on the sensitivity and response range of the pressure sensor. In addition, the sensors were arrayed as 4 × 4 modules to successfully recognize a wide range of foot movements from different volunteers. These findings illuminate potential applications in human motion detection, healthcare rehabilitation, and artificial intelligence.

Flexible and antibacterial conductive hydrogels based on silk fibroin/polyaniline/AgNPs for motion sensing and wound healing promotion under electrical stimulation.

Flexible conductive hydrogel-based electronic skin (E-skin) for simultaneous biotherapeutics and sensing applications is one of the current research directions. In this study, conductive and homogeneous silk fibroin/polyaniline/AgNP complexes (SPAg complexes) were prepared with the assistance of silk fibroin, which greatly optimized the compatibility of PANI with the hydrogel matrix. Then, SPAg was introduced into the covalently crosslinked polymer network to prepare poly(acrylamide-co-sulfobetaine methacrylate) - SPAg hydrogels (labeled as PSPAg hydrogels). The PSPAg hydrogels exhibit good biocompatibility, excellent mechanical properties, superb adhesive performance, and fantastic sensing capabilities. Being connected to a smartphone via a Bluetooth system, the SPAg hydrogel-based E-skin was employed to accurately monitor human movements including vigorous joint movements and subtle facial micro-expressions. Finally, benefiting from the synergistic effect of antimicrobial and exogenous electrical stimulation, through promoting angiogenesis and accelerating collagen production in diabetic wounds, PSPAg E-skin successfully facilitates rapid diabetic wound healing. Therefore, the multifunctional PSPAg hydrogel-based E-skin shows great promise for applications in wearable devices and bioelectronics.

Neural signatures of motor imagery for a supernumerary thumb in VR: an EEG analysis

Human movement augmentation is a rising field of research. A promising control strategy for augmented effectors involves utilizing electroencephalography through motor imagery (MI) functions. However, performing MI of a supernumerary effector is challenging, to which MI training is one potential solution. In this study, we investigate the validity of a virtual reality (VR) environment as a medium for eliciting MI neural activations for a supernumerary thumb. Specifically, we assess whether it is possible to induce a distinct neural signature for MI of a supernumerary thumb in VR. Twenty participants underwent a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements. Spectral power and event related desynchronization (ERD) analyses at the group level showed that the MI signature associated with the supernumerary thumb was indeed distinct, significantly different from both the baseline and the MI signature associated with the natural thumb, while single-trial classification showed that it is distinguishable with a 78% and 69% classification accuracy, respectively. Furthermore, spectral power and ERD analyses at the group level showed that the MI signatures associated with directional movement of the supernumerary thumb, flexion and extension, were also significantly different, and single-trial classification demonstrated that these movements could be distinguished with 60% accuracy. Fine-tuning the models further increased the respective classification accuracies, indicating the potential presence of personalized features across subjects.

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Visual experience and sensitivity to motion sequences, from human movement patterns to animated shapes

Visual experience and sensitivity to motion sequences, from human movement patterns to animated shapes

Humanoid Robot Teleoperation Through 3-D Pose Estimation

The knowledge of how the human motions is performed helps to understand how the human body works. This paper presents a method to estimate the human limbs angles through a kinematic model depicted by Roll-Pitch-Yaw rotationmatrix and the mimic of those angles on a humanoid robot. The advantage of this model is the detailed representation of each joint movement in the coordinate axes (x, y, z). The angles estimation is made with the information provided by algorithms of artificial vision and artificial intelligence. In order to reduce the latency between the human motion capture and robot motions, a fuzzy logic controller is implemented in order to control each robot joint. The final robot limbs angles are compared with the human angles in order to obtain the final error between those measurements. This method shows a similar result on the arms posture regarding previous works.

Graph network-based human movement prediction for socially-aware robot navigation in shared workspaces

Methods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments.

A Study on the Mechanisms and Performance of a Polyvinyl Alcohol-Based Nanogenerator Based on the Triboelectric Effect.

Polyvinyl alcohol (PVA), a versatile polymer, is extensively used across many industries, such as chemicals, food, healthcare, textiles, and packaging. However, research on applying PVA to triboelectric nanogenerators (TENGs) remains limited. Consequently, we chose PVA as the primary material to explore its contact electrification mechanisms at the molecular level, alongside materials like Polyethylene (PE), Polyvinylidene fluoride (PVDF), and Polytetrafluoroethylene (PTFE). Our findings show that PVA has the highest band gap, with the smallest band gap occurring between the HOMO of PVA and the LUMO of PTFE. During molecular contact, electron transfer primarily occurs in the outermost layers of the molecules, influenced by the functional groups of the polymers. The presence of fluorine atoms enhances the electron transfer between PVA and PTFE to maximum levels. Experimental validation confirmed that PVA and PTFE contact yields the highest triboelectric performance: VOC of 128 V, ISC of 2.83 µA, QSC of 82 nC, and an output power of 384 µW. Moreover, P-TENG, made of PVA and PTFE, was successfully applied in self-powered smart devices and monitored human respiration and bodily movements effectively. These findings offer valuable insights into using PVA in triboelectric nanogenerator technologies.

DAMO: A Deep Solver for Arbitrary Marker Configuration in Optical Motion Capture

Marker-based optical motion capture (mocap) systems are increasingly utilized for acquiring 3D human motion, offering advantages in capturing the subtle nuances of human movement, style consistency, and ease of obtaining desired motion. Motion data acquisition via mocap typically requires laborious marker labeling and motion reconstruction, recent deep-learning solutions have aimed to automate the process. However, such solutions generally presuppose a fixed marker configuration to reduce learning complexity, thereby limiting flexibility. To overcome the limitation, we introduce DAMO, an end-to-end deep solver, proficiently inferring arbitrary marker configurations and optimizing pose reconstruction. DAMO outperforms state-of-the-art like SOMA and MoCap-Solver in scenarios with significant noise and unknown marker configurations. We expect that DAMO will meet various practical demands such as facilitating dynamic marker configuration adjustments during capture sessions, processing marker clouds irrespective of whether they employ mixed or entirely unknown marker configurations, and allowing custom marker configurations to suit distinct capture scenarios. DAMO code and pretrained models are available at https://github.com/CritBear/damo