This paper presents a unique real-time motion recognition system for Electromyographic (EMG) signal acquisition and classification. It is the first approach which can classify hand poses from multi-channel EMG signals gathered from randomly placed arm sensors as accurately as current placed-sensor EMG acquisition approaches. It combines time-domain feature extraction, Linear Discriminant Analysis (LDA) feature projection and Multilayer Perceptron (MLP) classification to allow nine distinct poses to be correctly identified more than 95% of the time. This is comparable to state-of-the-art placed-sensor EMG acquisition systems. Processing times of 11.70 ms also make this a viable candidate approach for real-time EMG acquisition and processing in practical prosthesis applications.
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings|
|Number of pages||5|
|Publication status||Published - 13 Sep 2018|
|Event||IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Calgary, Canada|
Duration: 15 Jan 2018 → 20 Jan 2018
Conference number: 2018
|Name||IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings|
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing|
|Period||15/01/2018 → 20/01/2018|
- Electromyographic (EMG), time-domain features, pattern recognition, linear discriminant analysis (LDA), multilayer perceptron (MLP).