A5022453171- Muscle activation and electromyography studies
- Advanced Sensor and Energy Harvesting Materials
- Hand Gesture Recognition Systems
- EEG and Brain-Computer Interfaces
- Gait Recognition and Analysis
- Balance, Gait, and Falls Prevention
- Stroke Rehabilitation and Recovery
- Wireless Networks and Protocols
- Foot and Ankle Surgery
- Indoor and Outdoor Localization Technologies
- Motor Control and Adaptation
- Tendon Structure and Treatment
- Underwater Vehicles and Communication Systems
- Prosthetics and Rehabilitation Robotics
- Context-Aware Activity Recognition Systems
- Knee injuries and reconstruction techniques
Southern University of Science and Technology
2019-2025
Continuous motion prediction plays a significant role in realizing seamless control of robotic exoskeletons and orthoses. Explicitly modeling the relationship between coordinated muscle activations from surface electromyography (sEMG) human limb movements provides new path sEMG-based human–machine interface. Instead numeric features individual channels, we propose synergy-driven adaptive network-based fuzzy inference system (ANFIS) approach to predict continuous knee joint movements, which...
Current surface electromyography (sEMG) methods for locomotion mode prediction face limitations in anticipatory capability due to computation delays and constrained window lengths typically below 500ms-a practice historically tied stationarity requirements of handcrafted feature extraction. This study investigates whether end-to-end convolutional neural networks (CNNs) processing raw sEMG signals can overcome these constraints through extended (250ms 1500 ms). We systematically evaluate six...
While inertial measurement unit (IMU)-based motion capture (MoCap) systems have been gaining popularity for human movement analysis, they still suffer from long-term positioning errors due to accumulated drift and inefficient data transmission via Wi-Fi or Bluetooth. To address this problem, study introduces an integrated ultrawideband (UWB)-IMU system, named UI-MoCap, designed simultaneous 3D as well wireless IMU through UWB pulses. The UI-MoCap comprises mobile tags hardware-synchronized...
Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of intentions before actual movement. However, the estimation performance human joint trajectory remains a challenging problem due to inter- intra-subject variations. The former related physiological differences (such height weight) preferred walking patterns individuals,...
Physically-coupled bimanual tasks (activities where a force effect occurs between two human limbs) involve the coordination and cooperation of bilateral arms. Such uncertain contribution arms is often studied under static configuration, which not sufficient to typify all activities daily life (ADLs). This study aims investigate people's production control in dynamic tasks. Experiments were conducted with customized robotic system that characterized handles programmable fields them. Fourteen...
Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance recognition capabilities. However, many existing methods fall short fully exploit specific spatial topology temporal dependencies present HD-sEMG data. Additionally, these studies are...
Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed integrated feature extraction spatial, temporal, frequency dimensions from surface electromyography (sEMG) signals....
Keeping ligament strain at an appropriate range is beneficial for avoiding unexpected injuries and enhancing treatment efficacy. This study proposes a new trajectory determination method specifically the robot-assisted ankle rehabilitation. The input of this set constraints certain ligaments output detailed training trajectory. Simulations were conducted with two cases (one-ligament injury three-ligaments injury). While has not been experimentally tested, on condition accurate kinematics...
Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed integrated feature extraction spatial, temporal, frequency dimensions from surface electromyography (sEMG) signals....
Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance recognition capabilities. However, many existing methods fall short fully exploit specific spatial topology temporal dependencies present HD-sEMG data. Additionally, these studies are...