Wearable Real-Time Gesture Recognition Scheme Based on A-Mode Ultrasound

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 2623 - 2629
• Main Authors: Lu, Zongxing, Cai, Shaoxiong, Chen, Bingxing, Liu, Zhoujie, Guo, Lin, Yao, Ligang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: A-mode ultrasound; Accuracy; Algorithms; Completion time; Data processing; Feature extraction; Filtering; Gaussian process; Gesture recognition; human–machine interface (HMI); Machine learning; Man-machine interfaces; Muscles; Performance measurement; Real time; Real-time systems; Support vector machines; Transducers; Ultrasonic imaging; Ultrasound; Wearable technology
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3205026

A-mode ultrasound has the advantages of high resolution, easy calculation and low cost in predicting dexterous gestures. In order to accelerate the popularization of A-mode ultrasound gesture recognition technology, we designed a human-machine interface that can interact with the user in real-time. Data processing includes Gaussian filtering, feature extraction and PCA dimensionality reduction. The NB, LDA and SVM algorithms were selected to train machine learning models. The whole process was written in C++ to classify gestures in real-time. This paper conducts offline and real-time experiments based on HMI-A (Human-machine interface based on A-mode ultrasound), including ten subjects and ten common gestures. To demonstrate the effectiveness of HMI-A and avoid accidental interference, the offline experiment collected ten rounds of gestures for each subject for ten-fold cross-validation. The results show that the offline recognition accuracy is 96.92% ± 1.92%. The real-time experiment was evaluated by four online performance metrics: action selection time, action completion time, action completion rate and real-time recognition accuracy. The results show that the action completion rate is 96.0% ± 3.6%, and the real-time recognition accuracy is 83.8% ± 6.9%. This study verifies the great potential of wearable A-mode ultrasound technology, and provides a wider range of application scenarios for gesture recognition.