A standalone computing system to classify human foot movements using machine learning techniques for ankle-foot prosthesis control. Issue 12 (30th August 2022)

• Record Type: Journal Article
• Authors: Negi, Sachin; Sharma, Neeraj
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 12(2022)
• Page Start: 1370
• Page End: 1380
• Publication Date: 2022-08-30
• Subjects: Ankle-foot prosthesis -- Arduino Nano 33 BLE controller -- electromyography -- forcemyography -- TinyML; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.2012656
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: This paper presents different machine learning techniques to classify the following foot movements: (i) dorsiflexion, (ii) plantarflexion, (iii) inversion, (iv) eversion, (v) medial rotation, and (vi) lateral rotation. The purpose is to design a real-time standalone computing system to predict the foot movements in the sagittal plane, useful for ankle-foot prosthesis control. Electromyography (EMG) and forcemyography (FMG) signals were acquired from the leg's tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and peroneus longus muscles. First, Raspberry Pi was used to acquire EMG/FMG signals and to classify foot movements in real-time using different machine learning techniques. Later, an Arduino Nano 33 BLE controller was employed to implement the TinyML algorithm to classify these foot movements in the Arduino environment. The results showed that Raspberry Pi-based classification provided more than 99.5% accuracy for the EMG signals using LDA, LR, KNN, and SVC classifiers for offline prediction. However, for the classification of real-time signals, the performance of LDA is exceptionally well in predicting all classes. For Arduino Nano 33 BLE controller, the TinyML algorithm performed the classification task in real-time (8.5msec) without any misclassification. Further, the classification accuracy using EMG signal is much better than FMG based classification. Finally, the TinyML algorithm is applied on a transtibial amputee, and it is found that all three classes were classified correctly. Our finding suggests that a TinyML based Arduino Nano 33 BLE microcontroller is comparatively faster to predict and control, and it is smaller in size, thus advantageous for real-time prosthetic leg control applications.