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This paper presents a new method of gesture recognition based on multiple sensors fusion technique. Three kinds of sensors, namely surface Electromyography (sEMG) sensor, 3-axis accelerometer (ACC) and camera, are used together to capture the dynamic hand gesture firstly. Then four types of features are extracted from the three kinds of sensory data to depict the static hand posture and dynamic gesture...
This study explored the feasibility of building robust surface electromyography (EMG)-based gesture interfaces starting from the definition of input command gestures. As a first step, an offline experimental scheme was carried out for extracting user-independent input command sets with high class separability, reliability and low individual variations from 23 classes of hand gestures. Then three types...
This paper investigates the roles of a three-axis accelerometer, surface electromyography sensors and a webcam for dynamic gesture recognition. A decision-level multiple sensor fusion method based on action elements is proposed to distinguish a set of 20 kinds of dynamic hand gestures. Experiments are designed and conducted to collect three kinds of sensor data stream simultaneously during gesture...
This paper investigates the feasibility of building muscle-computer interfaces starting from surface Electromyography (SEMG) -based neck and shoulder motion recognition. In order to reach the research goal, a real-time SEMG sensing, processing and classification system was developed firstly. Then two types of SEMG recognition experiments, namely user-specific and user-independent classification, were...
This study explores the test-retest repeatability of surface EMG measurements during hand gesture tasks across days and across subjects. Subjects took part in the data collection experiments on five separate days in a four-week time period. Surface EMG (sEMG) data was collected from the forearm. Intrasubject and intersubject test-retest repeatability of mean absolute values (MAV) and four-order AR...
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