Search Results (179)

The human lower extremity plays a vital role in locomotion, posture, and weight-bearing through coordinated motion at the hip, knee, and ankle joints. These joints facilitate essential functions including flexion, extension, and internal and external rotation. To address mobility impairments through personalized therapy, this study presents the design, dynamic modeling, and control of a four-degree-of-freedom (4-DOF) lower limb exoskeleton robot. The system actuates hip flexion–extension and internal–external rotation, knee flexion–extension, and ankle dorsiflexion–plantarflexion. Anatomically aligned joint axes were incorporated to enhance biomechanical compatibility and reduce user discomfort. A detailed CAD model ensures ergonomic fit, modular adjustability, and the integration of actuators and sensors. The exoskeleton robot dynamic model, derived using Lagrangian mechanics, incorporates subject-specific anthropometric parameters to accurately reflect human biomechanics. A conventional sliding mode controller (SMC) was implemented to ensure robust trajectory tracking under model uncertainties. To overcome limitations of conventional SMC, an adaptive sliding mode controller with boundary layer-based chattering suppression was developed. Simulations in MATLAB/Simulink 2025 R2025a demonstrate that the adaptive controller achieves smoother torque profiles, minimizes high-frequency oscillations, and improves tracking accuracy. This work establishes a comprehensive framework for anatomically congruent exoskeleton design and robust control, supporting the future integration of physiological intent detection and clinical validation for neurorehabilitation applications. Full article

Recent studies have identified the hip joint as a central component of the human kinetic chain, playing a pivotal role in optimizing force transmission during movement. Enhancing its functional capacity represents an effective strategy for enhancing overall physical well-being and preventing injuries. This study investigates the effects of an eight-week hip joint functional training program on the health-related physical fitness, hip joint function, and factors associated with injury risk in university students from a track and field elective class. A total of 56 participants were randomly assigned to an experimental group (n = 28) or a control group (n = 28). The experimental group incorporated hip joint functional training, which comprising dynamic stretching and activation exercises, into their standard physical education (PE) class activities, while the control group continued with the regular physical education curriculum. Pre-intervention and post-intervention assessments included hip joint range of motion (ROM), functional movement screening (FMS), a 50 m sprint, standing long jump, sit-and-reach test, and spinal health evaluations. Results indicated that the experimental group demonstrated significant improvements in multi-directional hip range of motion (ROM), with examples including flexion increasing by 10° and external rotation by 9°. These improvements were accompanied by significant gains in functional movement screen (FMS) scores, with significant improvements in the Hurdle Step, whose median score increased to 3.0, Active Straight Leg Raise, and Rotary Stability components (all p < 0.05) compared to the control group. Furthermore, the training significantly reduced spinal asymmetry (axial trunk rotation reduced from 3.86° to 3.43°) and enhanced performance in the 50 m sprint (−0.26 s) and standing long jump (+0.08 m) (all p < 0.05). These objective improvements in functional movement patterns, postural alignment, and physical performance are associated with key biomechanical factors known to influence injury risk, such as the demonstrated gains in joint mobility and movement efficiency. Therefore, incorporating hip joint functional training into college physical education programs may effectively enhance students’ fundamental movement quality, improve joint stability, and promote postural health, thereby mitigating key biomechanical risk factors. This approach offers a practical strategy for educators to improve student physical health in general PE settings. Full article

As the demand for marine resource development escalates, underwater robots have gained prominence as a technological alternative to human participation in deep-sea exploration, resource assessments, and other intricate tasks, underscoring their academic and engineering importance. Traditional underwater robots, however, typically exhibit limited resilience to environmental disturbances and are readily obstructed or interfered with by aquatic vegetation, sediments, and other physical impediments. This paper examines the biological locomotion mechanisms of black ghostfish, which utilize undulatory fins and flapping wings, and presents a coupled undulatory-flapping propulsion strategy to facilitate rapid movement and precise posture adjustment in underwater robots. A multimodal undulatory-flapping bio-inspired underwater robotic platform is proposed, with a systematic explanation of its structure and motion principles. Additionally, kinematic and dynamic models for coordinated propulsion with multiple actuators are developed, and the robot’s performance under various driving modes is evaluated using computational fluid dynamics simulations. The simulation outcomes confirm the viability of the developed dynamic model. A prototype was constructed, and a PID-based control algorithm was developed to assess the robot’s performance in linear movement, turning, and other behaviors in both undulatory fin and flapping modes. Experimental findings indicate that the robot, functioning in undulatory fin propulsion mode at a frequency of 2.5 Hz, attains a velocity of 0.35 m/s, while maintaining attitude angle fluctuation errors within ±5°. In the flapping propulsion mode, precise posture modifications can be executed. These results validate the feasibility of the proposed multimodal bio-inspired underwater robot design and provide a new approach for the development of high-performance, autonomous bio-inspired underwater robots. Full article

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm² to 25.96 cm², the estimated results were close to the actual area trends (R² = 0.9425), and the area error was within 5.16 cm² (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage. Full article

Existing research has preliminarily achieved stable walking in humanoid robots; however, natural human-like leg motion and adaptive capabilities in dynamic environments remain unattained. This paper proposes a bionic central pattern generator (CPG) gait generation method based on Kimura neurons. The method maps the CPG output to the spatial motion patterns of the robot’s center of mass (CoM) and foot trajectory, modulated by 22 undetermined parameters. To address the vague physical interpretation of CPG parameters, the strong neuronal coupling, and the difficulty of decoupling, this research systematically optimized the CPG parameters by defining an objective function that integrates dynamic balance performance with step constraints, thereby enhancing the naturalness and coordination of gait generation. To further enhance the walking stability of the robot under varying road curvatures, a vestibular reflex mechanism was designed based on the Tegotae theory, enabling real-time posture adjustment during slope walking. To validate the proposed approach, a virtual simulation platform and a physical humanoid robot system were constructed to comparatively evaluate motion performance on flat terrain and slopes with different gradients. The results show that the energy consumption characteristics of robot-coordinated gait are highly consistent with the energy-saving mechanism of human natural motion. In addition, the established reflection mechanism significantly improves the motion stability of the robot in slope transition, and its excellent stability margin and environmental adaptability are verified by simulation and experiment. Full article

Accurate human keypoint localization in complex environments demands robust sensing and advanced modeling. In this article, we construct a ST-ConvLSTM network for 3D human keypoint estimation via millimeter-wave radar point clouds. The ST-ConvLSTM network processes multi-channel radar image inputs, generated from multi-frame fused point clouds through parallel pathways. These pathways are engineered to extract rich spatiotemporal features from the sequential radar data. The extracted features are then fused and fed into fully connected layers for direct regression of 3D human keypoint coordinates. In order to achieve better network performance, a mmWave radar 3D human keypoint dataset (MRHKD) is built with a hybrid human motion annotation system (HMAS), in which a binocular camera is used to measure the human keypoint coordinates and a 60 GHz 4T4R radar is used to generate radar point clouds. Experimental results demonstrate that the proposed ST-ConvLSTM, leveraging its unique ability to model temporal dependencies and spatial patterns in radar imagery, achieves MAEs of 0.1075 m, 0.0633 m, and 0.1180 m in the horizontal, vertical, and depth directions. This significant improvement underscores the model’s enhanced posture recognition accuracy and keypoint localization capability in challenging conditions. Full article

Background: This study investigated the biomechanical and physiological demands of the hiking position in Laser sailing, a posture requiring sailors to extend their upper bodies outside the boat to counter wind-induced heeling. This study utilized a mixed-methods approach. Methods: Twenty-two experienced Laser sailors participated in both on-land and offshore assessments. The study combined subjective discomfort ratings, biomechanical measurements, digital human modeling, and muscle activation analysis to evaluate the effects of hiking during and after exertion. Results: A two-way ANOVA showed significant effects by body region and time. The quadriceps, abdominals, and lower back reported the highest discomfort. Key postural angles were identified, including knee and hip flexion, trunk inclination, and ankle dorsiflexion. Muscle activation analysis revealed the highest engagement in the rectus abdominis (46.1% MVC), brachialis (~45%), and psoas major (~41%), with notable bilateral asymmetries. The trunk region had the highest overall activation (28.7% MVC), followed by the upper limbs (~18.7%), while the lower limbs were minimally engaged during static hiking. Conclusions: On-water conditions resulted in greater variability in joint angles, likely reflecting wind fluctuations and wave-induced boat motion. Findings highlight the quadriceps, abdominals, and lower back as primary contributors to sustained hiking, while also emphasizing the importance of targeted endurance training and ergonomic equipment design. These insights can guide training, recovery, and ergonomic strategies to optimize performance and reduce injury risk in Laser sailors. Full article

Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the ‘information silo’ problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human–machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments. Full article

Utilizing tactile sensors embedded in intelligent mats is an attractive non-intrusive approach for human motion analysis. Interpreting tactile pressure 2D maps for accurate posture estimation poses significant challenges, such as dealing with data sparsity, noise interference, and the complexity of mapping pressure signals. Our approach introduces a novel dual-diffusion signal enhancement (DDSE) architecture that leverages tactile pressure measurements from an intelligent pressure mat for precise prediction of 3D body joint positions, using a diffusion model to enhance pressure data quality and a convolutional-transformer neural network architecture for accurate pose estimation. Additionally, we collected the pressure-to-posture inference technology (PPIT) dataset that relates pressure signals organized as a 2D array to Motion Capture data, and our proposed method has been rigorously evaluated on it, demonstrating superior accuracy in comparison to state-of-the-art methods. Full article

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

Gait evaluation approaches using small, lightweight inertial sensors have recently been developed, offering improvements in terms of both portability and usability. However, accelerometer outputs include both the acceleration that is generated by human motion and gravitational acceleration, which changes along with the posture of the body part to which the sensor is attached. This study presents a gait analysis method that uses the gravitational, centrifugal, tangential, and translational accelerations obtained from sensors attached to the lower legs. In this method, each sensor pose is sequentially estimated using sensor fusion to combine data obtained from a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The estimated sensor pose is then used to calculate the gravitational acceleration that is included in each axis of the sensor coordinate system. The centrifugal and tangential accelerations are determined from the gyroscope output. The translational acceleration is then obtained by subtracting the centrifugal, tangential, and gravitational accelerations from the accelerometer output. As a result, the acceleration components contained in the outputs of the accelerometers attached to the lower legs are provided. As only the acceleration components caused by walking motion are captured, thus reflecting their characteristics, it is expected that the developed method can be used for gait evaluation. Full article

Resilient artificial intelligence (Resilient AI) is relevant in many areas where technology needs to adapt quickly to changing and unexpected conditions, such as in the medical, environmental, security, and agrifood sectors. In the case study involving the therapeutic rehabilitation of patients with motor problems, the Resilient AI system is crucial to ensure that systems can effectively respond to changes, maintain high performance, cope with uncertainties and complex variables, and enable the dynamic monitoring and adaptation of therapy in real time. The proposed system integrates advanced technologies, such as computer vision and deep learning models, focusing on non-invasive solutions for monitoring and adapting rehabilitation therapies. The system combines the Microsoft Kinect v3 sensor with MoveNet Thunder – SinglePose, a state-of-the-art deep-learning model for human pose estimation. Kinect’s 3D skeletal tracking and MoveNet’s high-precision 2D keypoint detection together improve the accuracy and reliability of postural analysis. The main objective is to develop an intelligent system that captures and analyzes a patient’s movements in real time using Motion Capture techniques and artificial intelligence (AI) models to improve the effectiveness of therapies. Computer vision tracks human movement, identifying crucial biomechanical parameters and improving the quality of rehabilitation. Full article

Monitoring posture and movement accurately and efficiently is essential for both physical therapy and athletic training evaluation and interventions. Motion Tape (MT), a self-adhesive wearable skin-strain sensor made of piezoresistive graphene nanosheets (GNS), has demonstrated promise in capturing low back posture and movements. However, to address some of its limitations, this work explores alternative materials by replacing GNS with multi-walled carbon nanotubes (MWCNT). This study aimed to characterize the electromechanical properties of MWCNT-based MT. Cyclic load tests for different peak tensile strains ranging from 1% to 10% were performed on MWCNT-MT made with an aqueous ink of 2% MWCNT. Additional tests to examine load rate sensitivity and fatigue were also conducted. After characterizing the properties of MWCNT-MT, a human subject study with 10 participants was designed to test its ability to capture different postures and movements. Sets of six sensors were made from each material (GNS and MWCNT) and applied in pairs at three levels along each side of the lumbar spine. To record movement of the lower back, all participants performed forward flexion, left and right bending, and left and right rotation movements. The results showed that MWCNT-MT exceeded GNS-MT with respect to consistency of signal stability even when strain limits were surpassed. In addition, both types of MT could assess lower back movements. Full article

Human pose estimation is an important research direction in the field of computer vision, aiming to detect and locate key points of the human body from images or videos and infer human posture. It plays a significant role in many applications, such as action recognition, motion analysis, virtual reality, and human–computer interaction. As a popular research topic, it is often studied by beginners in deep learning. However, the task of human pose estimation is rather complex, and the mainstream datasets are huge. Even on high-end single-GPU devices, training models requires a considerable amount of time. To help beginners learn efficiently on devices with limited performance, this paper introduces the method of mixed-precision training into the model and combines it with early stopping to reduce the training time. The experimental results show that after introducing mixed-precision training, the training speed of the model was significantly improved and there was no significant decrease in model accuracy. Full article

Passive back-support exoskeletons commonly employ elastic components to assist users during dynamic tasks. However, these designs are ineffective in providing sustained assistance for prolonged static bending postures, such as those required in surgery, assembly, and farming, where users experience continuous lumbar flexion. To address this limitation, a novel passive back-support exoskeleton inspired by the human spine is proposed in this work. The exoskeleton integrates a five-bar linkage mechanism with vertebrae-mimicking units, allowing for both dynamic flexion–extension movements and rigid support at various flexion angles. During the experiments, subjects are instructed to perform a 30-min forward-bending assembly task under two conditions: with and without wearing the exoskeleton. Compared to the free condition, the electromyography results indicate a 10.1% reduction in integrated EMG (IEMG) and a 9.78% decrease in root mean square (RMS) values of the erector spinae with the exoskeleton. Meanwhile, the metabolic rate is decreased by 11.1%, highlighting the effectiveness of the exoskeleton in mitigating muscle fatigue during prolonged static work. This work provides a promising solution for reducing musculoskeletal strain in occupations requiring sustained forward bending, making it a valuable advancement in passive exoskeleton technology. Full article