Gated Recurrent Neural Networks for EMG-Based Hand Gesture Classification. A Comparative Study

Electromyographic activities (EMG) generated during contraction of upper limb muscles can be mapped to distinct hand gestures and movements, posing them as a promising modality for prosthetic and cybernetic applications. This paper presents a comparative analysis between different recurrent neural network (RNN) configurations for EMG-based hand gesture classification. In particular, RNNs with recurrent units of long short-term memory (LSTM) and gated recurrent unit (GRU) are evaluated. Furthermore, the effects of an attention mechanism and varying learning rates are evaluated. Results show a classifier 1) with a bidirectional recurrent layer composed of LSTM units, 2) that applies the attention mechanism, and 3) trained with step-wise learning rate outperforms all other tested RNN classifiers.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[3]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[4]  Manfredo Atzori,et al.  Building the Ninapro database: A resource for the biorobotics community , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Dana Kulic,et al.  Discriminative functional analysis of human movements , 2013, Pattern Recognit. Lett..

[8]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[9]  Dana Kulic,et al.  Affective Movement Recognition Based on Generative and Discriminative Stochastic Dynamic Models , 2014, IEEE Transactions on Human-Machine Systems.

[10]  Dana Kulic,et al.  Hand gesture recognition based on surface electromyography , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[12]  Daniel P. W. Ellis,et al.  Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems , 2015, ArXiv.

[13]  Ilja Kuzborskij,et al.  Characterization of a Benchmark Database for Myoelectric Movement Classification , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.