Joint Angles Research Articles

Biomechanical determinants of running economy

ABSTRACT Running economy (RE) defined as the submaximal rate of oxygen consumption during running, has been correlated with better performance. Biomechanical factors such as spatiotemporal parameters and lower extremity mechanics play a significant role in influencing RE. However, the relationship between RE and biomechanical variables in middle-distance running shoes is still unclear. In this study, twenty-one healthy male runners (age: 32.5 ± 5.6 years, height: 1.70 ± 0.10 m, body mass: 69.9 ± 7.3 kg) performed running trials on the treadmill. Physiological and biomechanical variables, including joint angles, moments, angular velocities, powers, ground reaction forces, and spatiotemporal parameters were analyzed. Multiple linear regression and backward stepwise regression analyses were employed to identify key biomechanical determinants of RE. The final regression model identified that the hip positive power, knee extension angular velocity, ankle plantarflexion angular velocity and negative power, and metatarsophalangeal extension angular velocity explained 73% of RE variability. Therefore, focus on optimizing these biomechanical variables, possible with specific shoe properties, could lead to enhanced RE in middle-distance running shoes.

Dynamic Motion-Based Optimization of Support and Transmission Mechanisms for Legged Robots

In order to improve the dynamic performance of legged robots, this paper proposes a method for optimizing the parameters of the leg mechanism based on dynamic motion. The proposed method consists of two key parts as follows: support mechanism optimization and transmission mechanism optimization. For the support mechanism, a mechanism analysis index based on robot motion energy is introduced to evaluate the robot dynamic motion performance. Under the structure stiffness constraint, this index can quantitatively analyze the influence of the range of motion and structure mass on the robot motion performance, thereby guiding the design of parameters such as the range of motion, structure thickness, and U-flange position of the mechanism. For the transmission mechanism, this paper optimizes the linkage length and knee joint angle for transmission ratio. Considering the variable transmission ratio and robot motion characteristics, the parameters are optimized to reduce the torque and speed requirements of the leg joint. This method determines the optimal mechanism parameters for dynamic performance based on the specified motion energy requirements, and it also optimizes the linkage length. The results show that the peak torque of the knee joint motor is reduced by 18.5%, and the peak speed is reduced by 24.8%.

Skeleton-Aware Graph-Based Adversarial Networks for Human Pose Estimation from Sparse IMUs

Recently, sparse-inertial human pose estimation (SI-HPE) with only a few IMUs has shown great potential in various fields. The most advanced work in this area achieved fairish results using only six IMUs. However, there are still two major issues that remain to be addressed. First, existing methods typically treat SI-HPE as a temporal sequential learning problem and often ignore the important spatial prior of skeletal topology. Second, there are far more synthetic data in their training data than real data, and the data distribution of synthetic data and real data is quite different, which makes it difficult for the model to be applied to more diverse real data. To address these issues, we propose “Graph-based Adversarial Inertial Poser (GAIP),” which tracks body movements using sparse data from six IMUs. To make full use of the spatial prior, we design a multi-stage pose regressor with graph convolution to explicitly learn the skeletal topology. A joint position loss is also introduced to implicitly mine spatial information. To enhance the generalization ability, we propose supervising the pose regression with an adversarial loss from a discriminator, bringing the ability of adversarial networks to learn implicit constraints into full play. Additionally, we construct a real dataset that includes hip support movements and a synthetic dataset containing various motion categories to enrich the diversity of inertial data for SI-HPE. Extensive experiments demonstrate that GAIP produces results with more precise limb movement amplitudes and relative joint positions, accompanied by smaller joint angle and position errors compared to state-of-the-art counterparts. The datasets and codes are publicly available at https://cslinzhang.github.io/GAIP/ .

The effect of different midsole materials in safety shoes on perceived comfort, muscle activities, and biomechanical parameters during walking – a musculoskeletal modelling approach

In certain occupational environments, safety footwear is essential for mitigating the risk of foot, leg, or knee injuries. For employees, who have to stand or walk for extended periods in such footwear, ergonomic considerations and comfort play a key role in maintaining health and work capacity. Therefore, this study aimed to investigate the subjective perception, muscle activities, and biomechanical parameters of the lower extremities during walking in safety shoes equipped with different midsole materials characterized by varying deformation and energy return properties. A prospective, randomized, single-blind crossover study was conducted with 24 healthy males wearing safety shoes in their daily work environments. Two S2 safety shoes with a comparable structural design but differing midsole materials (polyurethane (PU) vs. expanded thermoplastic polyurethane (eTPU)) were evaluated during walking with regard to subjective comfort, gait parameters, muscle activities as well as joint angles, joint moments, and muscle forces of the lower extremities calculated using a musculoskeletal multibody simulation. Both safety shoes received favourable comfort ratings from the participants. However, a significant majority expressed a preference for recommending the eTPU shoe model to their colleagues (eTPU: 14; PU: 5; p = 0.039). The musculoskeletal multibody simulation revealed only small midsole-related differences in upper ankle joint angle (1.24° ± 0.11°; p < 0.01) but no variations were observed in joint moments. However, the analysis identified lower muscle forces in the gastrocnemius medialis muscle, while wearing the safety shoes with an eTPU midsole (-1.05 ± 0.04 N/kg; p < 0.001). In conclusion, the midsole material had a low impact on subjective comfort as well as lower extremity joint angles and moments. However, the eTPU midsole may provide support to the plantar flexors during stance.

Characteristics of trunk and pelvic kinematics during batting motion in baseball players with low back pain history.

The batting motion has been reported to have a possible association with the development of low back pain (LBP) in baseball players. This study aimed to identify the kinematics of the batting motion in baseball players with a history of LBP, focusing on rotational and extension movements of the pelvis and trunk. This study employed a cross-sectional design. Twenty-five experienced baseball players (age: 20.8±1.6 years; height: 173.4±6.3 cm; weight: 68.4±9.4 kg) were randomly recruited at the International University of Health and Welfare university, and all participants were classified into either the LBP (N.=13) or non-LBP (N.=12) group. The joint angles of the trunk and pelvis were measured during batting motion with a three-dimensional motion analysis system. The LBP group showed a significantly smaller trunk rotation and pelvis posterior tilt angle and earlier timing of trunk rotation to the batting side as compared to the non-LBP group (P<0.05). This study revealed that experienced players with no history of LBP showed a delay in the timing of trunk rotation to the batting side while increasing the trunk rotation angle to the non-batting side to efficiently transfer the rotational energy generated from the lower limbs to the trunk and the upper limbs. Conversely, players with a history of LBP might have repeated inefficient motion in the process of transferring rotational energy generated by the lower extremities, pelvis, and trunk to the upper extremities.

Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos

The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were recorded from four neurotypical participants. For all recordings, an expert physician provided manual scores as the ground truth. The OpenPose BODY25 model extracted body keypoint data, which were used to calculate joint angles for an automated scoring algorithm. The algorithm’s scores were compared to the ground truth and expert manual scoring. High accuracy was achieved for the global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth movements, with Pearson correlation coefficients (PCCs) &gt; 0.9 and a low root mean square error (RMSE). Although slightly less accurate for global external rotation, the algorithm still showed strong agreement. This study demonstrates the potential of using AI and smartphone videos for reliable, remote upper limb assessments.

Enhanced joint energy transfer potential by the biarticular gastrocnemii muscles during perturbed walking.

Our objective was to explore how the potential for energy transfer between the ankle and knee joint via the biarticular gastrocnemii muscles is modulated during unpredictable and adapted trip-like and drop-like gait perturbations. Using kinematic parameters of the ankle and knee joints, the energy transfer potential between the two joints was determined as the fraction of contact time when the ankle and knee joint angles are in-phase. Additionally, the electromyographic activity of the gastrocnemius medialis and lateralis were captured during the drop-like perturbations. The energy transfer potential increased 1.6-fold in the trip-like and 2.5-fold in the drop-like perturbations compared to unperturbed walking, indicating a relevant involvement of biarticular mechanisms in maintaining body stability. The activation of the gastrocnemii was high (50-60% of a maximum voluntary contraction) in the phases of ankle-to-knee and knee-to-ankle joint energy transfer, which suggests a relevant contribution of biarticular mechanisms to the management of the body's energy during the drop-like perturbations. Considering the similar ankle-to-knee joint energy transfer potential compared to unperturbed walking, the higher activation of the gastrocnemii muscles in the first 20% of the stance indicates a greater contribution of biarticular mechanisms to the absorption of body energy in the unpredictable perturbations.

Comparative Analysis of Human Activity Recognition for Karate Skills Using IMU Raw Data and Derived Body Joint Angles

Human Activity Recognition (HAR) is a machine learning (ML) application that plays a vital role in various fields such as medicine and sports. Recent advancements have made HAR a well-established field. HAR can be performed using imagery or sensory data; this study focuses on sensory data generated by the Inertial Measurement Units (IMUs). HAR typically requires data from at least one sensor. However, raw sensor data from two sensors—specifically raw sensors placed on two joint body joints- are needed to calculate a joint's three-axis angles. To our knowledge, no evidence in the literature compares the performance of HAR models trained on raw sensory data versus those trained on body joint angles in sports science. In this study, we aim to bridge this research gap by studying the difference in training on both data types in the karate sport as a case study. The current work includes two comprehensive four karate skills datasets: one consisting of raw sensory data and the other comprising calculated body joint angles derived from the raw data. The datasets were collected from professional karate players performing four distinct skills. We proposed a normalization algorithm to address the different numbers of readings per player performing the same karate skill. Next, several ML models were trained on the normalized versions of both datasets to determine which dataset contains more easily predictable patterns. The results indicate that the body joint angles dataset exhibited significantly higher accuracy than the raw sensory dataset. Moreover, the proposed normalization algorithm demonstrated promising results across all models and effectively mitigated the overfitting issue in both datasets.

Transfer learning-enhanced CNN-GRU-attention model for knee joint torque prediction

IntroductionAccurate prediction of joint torque is critical for preventing injury by providing precise insights into the forces acting on joints during activities. Traditional approaches, including inverse dynamics, EMG-driven neuromusculoskeletal (NMS) models, and standard machine learning methods, typically use surface EMG (sEMG) signals and kinematic data. However, these methods often struggle to reveal the complex, non-linear relationship between muscle activation and joint motion, particularly with complex or unfamiliar movements. The generalization of joint torque estimation models across different individuals faces a significant challenge, as feature transferability tends to decline in higher, task-specific layers, reducing model performance.MethodsIn this study, we proposed a CNN-GRU-Attention neural network model combining a neuromusculoskeletal (NMS) solver-informed (hybrid-CNN) augmented with transfer learning, designed to predict knee joint torque with higher accuracy. The neural network was trained using EMG signals, joint angles, and muscle forces as inputs to predict knee joint torque in different activities, and the predictive performance of the model was evaluated both within and between subjects. Additionally, we have developed a transfer learning method in the inter-subject model, which improved the accuracy of knee torque prediction by transferring the learning knowledge of previous participants to new participants.ResultsOur results showed that the hybrid-CNN model can predict knee joint torque within subjects with a significantly lower error (root mean square error ≤0.16 Nm/kg). A transfer learning technique was adopted in the inter-subject tests to significantly improve the generalizability with a lower error (root mean square error ≤0.14 Nm/kg).ConclusionThe transfer learning-enhanced CNN-GRU-Attention with the NMS model shows great potential in the prediction of knee joint torque.

Development of a flexible fabric-based variable shape actuator for enhanced multi-DoF haptic feedback

Conventional haptic feedback devices that rely on rigid components to achieve multiple degrees of freedom (DoF) often result in bulky, complex systems that hinder natural movement and increase user discomfort. This study addresses these limitations by substituting rigid materials with flexible, fabric-based materials to develop a variable shape actuator (VSA). The VSA is designed to provide multi-DoF haptic feedback by integrating contraction and expansion mechanisms within a lightweight, fabric structure. The actuator consists of a tactile feedback actuator (TFA) for delivering tactile sensations and a joint angle actuator (JAA) for kinesthetic feedback, allowing the system to dynamically alter its shape. Experimental results demonstrate that the VSA successfully produces multi-DoF haptic feedback on the palmar surface of the hand, enhancing the haptic experience in virtual environments. This advancement in haptic feedback technology paves the way for more diverse and immersive feedback systems, significantly narrowing the gap between virtual reality and physical reality.

Stability Study of Low Bond Stone Masonry Walls: A Case Study of the Royal City Platform Wall at the Shimao Site

ABSTRACT Masonry walls represent a significant architectural heritage that continues to be prevalent in various regions. Ancient masonry walls are typically constructed using mortar composed of soil and water and are characterised by low adhesion. Presently, research on the factors affecting the stability of low-bond stone masonry walls is still in the preliminary stage and lacks a unified understanding. This study investigates the factors influencing the stability of low-bond stone masonry walls, focusing on the Royal City platform wall at Shimao Site in China. A scaled-down model was constructed based on the actual conditions of the Royal City platform wall, and Schneebeli rods were loaded into the experimental model. This study examines the effects of height-to-width ratio, retaining wall inclination angle, masonry method, and mortar joint strength on wall stability. The results indicate that as the height-to-width ratio and inclination angle of the retaining wall increase, its stability decreases, and the angle between the failure surface and the horizontal direction increases. While the masonry method has a relatively minor influence on wall stability, variations in the mortar joint strength significantly impact the stability of the retaining wall. Based on the experimental results, which revealed two failure modes of overturning and sliding, a stability calculation method for low-bond stone masonry walls was derived using the limit equilibrium method. The proposed method was applied to analyse the Royal City platform wall. The findings provide valuable insights into the restoration and preservation of low-bond stone masonry walls.

Prediction of lower limb joint angles from surface electromyography using XGBoost

Prediction of lower limb joint angles from surface electromyography using XGBoost

Two cameras can be as good as four for markerless hand tracking during simple finger movements.

Two cameras can be as good as four for markerless hand tracking during simple finger movements.

Lower extremity joint kinematics in individuals with and without bilateral knee osteoarthritis during normal and narrow-base walking: A cross-sectional study.

Lower extremity joint kinematics in individuals with and without bilateral knee osteoarthritis during normal and narrow-base walking: A cross-sectional study.

An excitation trajectory for dynamic parameter identification of robot using Logistic function and Depth-first Search

Abstract The dynamic parameter identification is playing an important role in robot dynamic controller design. However, many excitation trajectories for parameter identification cannot cover enough motion states of the robot joints, which weakens the generalization of the dynamic model. In this paper, an excitation trajectory covering multi-axis motion states based on depth-first search is proposed and spliced from Logistic functions, in which the angular velocity and angular acceleration can be directly obtained from joint angle by the analytical formula. Taking the condition number of observation matrix as an optimization index, the excitation trajectory is optimized based on genetic algorithm. The strategy of iterative identification is adopted, and in each iteration the dynamic parameters are identified according to the last position and torque sequences and updated to the controller until the actual trajectory approaches the desired trajectory. Experimental results show that after two iterations, the tracking accuracy is greatly improved, and the effectiveness of the proposed approach is verified.

Postural control strategies in individuals with and without chronic ankle instability during the reach phase of the posteromedial direction of the star excursion balance test.

Postural control strategies in individuals with and without chronic ankle instability during the reach phase of the posteromedial direction of the star excursion balance test.

The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

The effects of different shoe stack heights and running speeds on full-body running coordination: An uncontrolled manifold analysis.

A novel revision technique in the treatment for postoperative deviation after radial polydactyly reconstruction

PurposePostoperative deviation (PD) is a complicated and troublesome complication after polydactyly reconstruction. We aim to investigate the clinical efficacy of a novel revision surgery, reconstruction of the balance of bone and soft tissue, treating PD after radial polydactyly reconstruction in comparison with a previous surgical plan.MethodsWe reconstructed the balance of bone and soft tissue to correct PD and the asymmetrical appearance of the thumb. At the follow-up, we used the digital circumferential ratio (reconstructive/healthy side), Vancouver Scar Scale (VSS) score, Japanese Society for Surgery of the Hand (JSSH), aesthetic scoring system (ASS) modified Tada score and range of motion (ROM) to evaluate the appearance and function outcomes.ResultsSeventeen PD patients received our new surgical plan (NG), and 31 patients received the traditional surgical plan (CG). There was no significant difference in gender, age, site of hand and follow-up period, as well as preoperative digital circumference ratio, deviation angle of metacarpophalangeal joints (MP) and interphalangeal joint (IP). At the final follow-up, we found that digital circumference ratio, JSSH and ASS were obviously higher in NG than in CG, implying that our novel surgical plan can provide good aesthetics and excellent postoperative function.ConclusionsThe reconstructive balance of bone and soft tissue can effectively improve the appearance and function of PD after polydactyly reconstruction. Mastering the indications for surgery and good microsurgical techniques are the keys to ensuring therapeutic effects.

Preoperative gait pattern as predictor of gait changes following selective dorsal rhizotomy.

Preoperative gait pattern as predictor of gait changes following selective dorsal rhizotomy.

Lower limb kinematic changes during stair navigation 3 and 5 months after anterior cruciate ligament reconstruction: A longitudinal analysis in real-world settings.

Lower limb kinematic changes during stair navigation 3 and 5 months after anterior cruciate ligament reconstruction: A longitudinal analysis in real-world settings.