Effectiveness of Surface Electromyography in Pattern Classification for Upper Limb Amputees

Abstract

This study was undertaken to explore 18 time domain (TD) and time-frequency domain (TFD) feature configurations to determine the most discriminative feature sets for classification. Features were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements and used to perform a series of classification trials with the random forest classifier. Movement datasets for 11 intact subjects and 9 amputees from the NinaPro online database repository were used. The aim was to identify any optimum configurations that combined features from both domains and whether there was consistency across subject type for any standout features. This work built on our previous research to incorporate the TFD, using a Discrete Wavelet Transform with a Daubechies wavelet. Findings report configurations containing the same features combined from both domains perform best across subject type (TD: root mean square (RMS), waveform length, and slope sign changes; TFD: RMS, standard deviation, and energy). These mixed-domain configurations can yield optimal performance (intact subjects: 90.98%; amputee subjects: 75.16%), but with only limited improvement on single-domain configurations. This suggests there is limited scope in attempting to build a single absolute feature configuration and more focus should be put on enhancing the classification methodology for adaptivity and robustness under actual operating conditions. CCS Concepts • Computing methodologies➝Machine learning • Computing methodologies➝Feature selection • Human-centered computing~Interaction techniques.

Gesture Recognition from Bio-signals Using Hybrid Deep Neural Networks

“…The input of the model is time-domain features generated by DeepLearnToolbox. The feature extraction strategy of my work is based on Côté-Allard et al [10] and [19]. The former compared the performance of raw signal input with state-of-the-art feature input on several deep learning models.…”

“…The former compared the performance of raw signal input with state-of-the-art feature input on several deep learning models. On the aspect of sEMG feature extracting, [19] has done much work on the Ninapro in both the time domain and frequency domain.…”

Biosignal-based transferable attention Bi-ConvGRU deep network for hand-gesture recognition towards online upper-limb prosthesis control

“…In this category, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are widely used state-of-the-art. A 3-layer LSTM model to predict 17 hand movements using 12 sEMG signal channels was reported [11]. Recently, hybrid CNN + RNN deep learning networks have shown advantages in gesture recognition [12].…”

“…We conducted a range of experiments on both time and frequency domain features in this and our previous work [11]. Frequency-domain features were studied, e.g.…”

Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal

“…In [18], the authors used 11 intact subjects of DB2 along with 9 amputated subjects of the Ninapro dataset. They used the same analysis window configuration as that of the [8] and used time-frequency domain features by using the discrete wavelet transform of the data.…”