Foot Gesture Recognition with Flexible High-Density Device Based on Convolutional Neural Network

Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.