arXiv subjects

Shucong Yin

Publications and source records attributed to Shucong Yin.

3 recordsLinked to original sources

arXiv2025

A Kinematic and Kinetic Dataset of Lower Limb Joints During Obstacle Crossing in Healthy Young Adults

Obstacle crossing is an essential component of human locomotion, particularly for individuals with lower limb amputations who face elevated risks of imbalance and falls. While prior studies have explored this task, they often lack a comprehensive examination of kinematic and kinetic changes throughout the entire gait cycle across varying obstacle heights. This study creates a novel dataset collected from ten healthy adults performing obstacle crossing at four different heights (7.5 cm, 15 cm, 22.5 cm, and 30 cm). Kinematic and kinetic data (angles and torques of hip, knee, and ankle) were recorded and analyzed. Results indicate that increased obstacle height leads to a longer swing phase and significant increases in both hip and knee joint angles (1.5* and 1.0*, respectively) and torques. In contrast, ankle joint angles and moments exhibited minimal variation across obstacle heights, indicating a relatively consistent movement strategy at the ankle. Furthermore, significant asymmetries were observed between the dominant and non-dominant foot: the dominant foot demonstrated larger hip and knee joint angles and more consistent ankle behavior, reflecting greater coordination. These findings offer valuable biomechanical insights for improving fall prevention strategies and informing the design of assistive devices such as prostheses and exoskeletons.

physics.med-ph↗

arXiv2024

Enhancing Prosthetic Safety and Environmental Adaptability: A Visual-Inertial Prosthesis Motion Estimation Approach on Uneven Terrains

Environment awareness is crucial for enhancing walking safety and stability of amputee wearing powered prosthesis when crossing uneven terrains such as stairs and obstacles. However, existing environmental perception systems for prosthesis only provide terrain types and corresponding parameters, which fails to prevent potential collisions when crossing uneven terrains and may lead to falls and other severe consequences. In this paper, a visual-inertial motion estimation approach is proposed for prosthesis to perceive its movement and the changes of spatial relationship between the prosthesis and uneven terrain when traversing them. To achieve this, we estimate the knee motion by utilizing a depth camera to perceive the environment and align feature points extracted from stairs and obstacles. Subsequently, an error-state Kalman filter is incorporated to fuse the inertial data into visual estimations to reduce the feature extraction error and obtain a more robust estimation. The motion of prosthetic joint and toe are derived using the prosthesis model parameters. Experiment conducted on our collected dataset and stair walking trials with a powered prosthesis shows that the proposed method can accurately tracking the motion of the human leg and prosthesis with an average root-mean-square error of toe trajectory less than 5 cm. The proposed method is expected to enable the environmental adaptive control for prosthesis, thereby enhancing amputee's safety and mobility in uneven terrains.

cs.RO↗

arXiv2023

A Learning Quasi-stiffness Control Framework of a Powered Trans-femoral Prosthesis for Adaptive Speed and Incline Walking

Impedance-based control represents a prevalent strategy in the powered trans femoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walking continues to be a significant challenge. This article proposes a task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. A Gaussian Process Regression model is introduced to predict the target features of the human joints angle and torque in a new task. Subsequently, a Kernel Movement Primitives is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the trans-femoral amputees.

cs.RO↗