Exploiting accelerometers to improve movement classification for prosthetics

Abstract

Abstract-Recent studies have explored the integration of additional input modalities to improve myoelectric control of prostheses. Arm dynamics in particular are an interesting option, as these can be measured easily by means of accelerometers. In this work, the benefit of accelerometer signals is demonstrated on a large scale movement classification task, consisting of 40 hand and wrist movements obtained from 20 subjects. The results demonstrate that the accelerometer modality is indeed highly informative and even outperforms surface electromyography in terms of classification accuracy. The highest accuracy, however, is obtained when both modalities are integrated in a multi-modal classifier.

Electromyogram-based hand gesture recognition robust to various arm postures

“…Our approach also used accelerometer signals, and we have verified that experimentally the recognition performance is highly improved more than 31.6% for eight hand gestures. In this study, EMG signals were measured on the wrists differently from previous studies, [27][28][29][30] and our results indicated that gravity, biomechanics, and muscle rotation due to arm posture have a significant effect on the signals measured on the wrist.…”

“…One is for recognizing complicate and numerous hand gestures such as sign languages and dexterous prosthetic hand control. [19][20][21]23,[27][28][29] The other is to prevent the performance degradation of the hand gesture recognition due to various arm postures, that is, even for the same hand gesture, the recognition accuracy decreases as arm postures change. 30 In this study, we aim to improve the robustness of hand gesture recognition to arm posture changes.…”

sEMG-Based Gesture Recognition with Convolution Neural Networks

“…The HIST needs to predefine the number of bins. The mDWT decomposes the signals in terms of a basis function (i.e., the wavelet) at different levels of resolution, resulting in a high-dimensional frequency-time representation [46]. The predefined number of bins and the parameters of the wavelet are listed in Table 1.…”

“…The increment of sliding window was set to 10 ms (20 samples). The selected signal features include: Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossings (ZC), Histogram (HIST) and marginal Discrete Wavelet Transform (mDWT) [14,43,46]. The HIST needs to predefine the number of bins.…”

Longitudinal tracking of physiological state with electromyographic signals.

“…More sophisticated approaches are needed to improve these prosthetic devices in order to reduce dependence on external control mechanisms. Some approaches include adding accelerometer data to the inputs [34], or even employing electroencephalogram (EEG) inputs [35].…”