Fusion of kinematic and physiological sensors for hand gesture recognition
學年 112
學期 1
出版（發表）日期 2024-01-29
作品名稱 Fusion of kinematic and physiological sensors for hand gesture recognition
作品名稱（其他語言）
著者 Aiguo Wang; Huancheng Liu; Chundi Zheng; Huihui Chen; Chih-Yung Chang
單位
出版者
著錄名稱、卷期、頁數 Multimedia Tools and Applications, vol. 83, pp. 68013-68040
摘要 The uncertainty of hand gestures, the variability of gestures across subjects, and the high cost of collecting a large amount of annotated data lead to a great challenge to the robust recognition of gestures, and thus it remains quite crucial to capture the informative features of hand movements and to mitigate inter-subject variations. To this end, we propose a gesture recognition model that uses two different types of sensors and optimizes the feature space towards enhanced accuracy and better generalization. Specifically, we use an accelerometer and a surface electromyography sensor to capture kinematic and physiological signals of hand movements. We use a sliding window to divide the streaming sensor data and then extract time-domain and frequency-domain features from each segment to return feature vectors. Afterwards, the feature space is optimized with a feature selector and a gesture recognizer is optimized. To handle the case where no labeled training data are available for a new user, we apply the transfer learning technique to reuse the cross-subject knowledge. Finally, extensive comparative experiments concerning different classification models, different sensors, and different types of features are conducted. Results show that the joint use of kinematic and physiological sensors generally outperforms the use of single sensor, indicating the synthetic effect of different sensors, and that the use of transfer learning helps improve the cross-subject recognition accuracy. In addition, we quantitatively investigate the impact of null gesture on a gesture recognizer and results indicate that null gesture would lower its accuracy, enlightening related studies to consider it.
關鍵字 Gesture recognition; Electromyography; Accelerometer; Feature extraction; Cross-subject
語言 en
ISSN 1573-7721
期刊性質 國內
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126327 )

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