X
Xiang Chen
Researcher at University of Science and Technology of China
Publications - 114
Citations - 3859
Xiang Chen is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Gesture recognition & Gesture. The author has an hindex of 24, co-authored 114 publications receiving 2753 citations. Previous affiliations of Xiang Chen include Central South University.
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Journal ArticleDOI
A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors
TL;DR: A framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors that facilitates intelligent and natural control in gesture-based interaction.
Proceedings ArticleDOI
Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors
TL;DR: A novel hand gesture recognition system that utilizes both multi-channel surface electromyogram (EMG) sensors and 3D accelerometer (ACC) sensors to realize user-friendly interaction between human and computers is described.
Journal ArticleDOI
A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices
TL;DR: An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition, which includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features.
Journal ArticleDOI
A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals
TL;DR: A framework for activity awareness using surface electromyography and accelerometer signals is proposed and a continuous daily activity monitoring and fall detection scheme was performed, demonstrating the excellent fall detection performance and the great feasibility of the proposed method in daily activities awareness.
Proceedings ArticleDOI
Hand Gesture Recognition Research Based on Surface EMG Sensors and 2D-accelerometers
TL;DR: Experimental results show that the combination of s EMG sensors and accelerometers achieved 5-10% improvement in the recognition accuracies for hand gestures when compared to that obtained using sEMG sensors solely.