James W. L. Pau

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Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a(More)
This paper presents the development of a neuromuscular interface for an exoskeleton to assist the elbow joint. The interface uses electromyographic (EMG) signals obtained from the biceps and triceps to predict elbow flexion and extension movements. These movements occur in the sagittal plane and the effects of forearm weight have been incorporated. The(More)
PURPOSE This case study describes how an individual with spastic quadriplegic cerebral palsy was trained over a period of four weeks to use a commercial electroencephalography (EEG)-based brain-computer interface (BCI). METHOD The participant spent three sessions exploring the system, and seven sessions playing a game focused on EEG feedback training of(More)
PURPOSE Using a commercial electroencephalography (EEG)-based brain-computer interface (BCI), the training and testing protocol for six individuals with spastic quadriplegic cerebral palsy (GMFCS and MACS IV and V) was evaluated. METHOD A customised, gamified training paradigm was employed. Over three weeks, the participants spent two sessions exploring(More)
This paper presents a novel approach that involves first identifying and verifying the available superficial muscles that can be recorded by surface electromyography (EMG) signals, and then developing a musculoskeletal model based on these findings, which have specifically independent DOFs for movement. Such independently controlled multiple DOF EMG-driven(More)
The increasing popularity of using biosignal interfacing with assistive devices and users who are physically disabled sees research being split into disparate areas of electromyography (EMG) signal filtering, feature extraction and interpretation, and specific areas of control. This paper presents the development of a neuromuscular interface (NI), which has(More)
Neural-musculoskeletal models play a significant role in the interactions between human and robotic devices. Surface Electromyography (sEMG) can effectively measure the electric signal from human muscle and provide useful information for improving the accuracy of human-machine interfaces. This paper summarizes three main sEMG-based research methods at(More)
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