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

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....

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Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering Jg. 30; S. 1
Hauptverfasser: Lu, Zongxing, Cai, Shaoxiong, Chen, Bingxing, Liu, Zhoujie, Guo, Lin, Yao, Ligang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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, 1558-0210
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Zusammenfassung: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.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2022.3205026