Hyperdimensional biosignal processing: A case study for EMG based hand gesture recognition
The mathematical properties of high dimensional spaces seem remarkably suited for describing behaviors produces by brains. Brain inspired hyperdimensional computing (HDC) explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are high dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i.d. components. These features provide an opportunity for energy efficient computing applied to cyberbiological and cybernetic systems. We describe the use of HDC in a smart prosthetic application, namely hand gesture recognition from a stream of Electromyography (EMG) signals. Our algorithm encodes a stream of analog EMG signals that are simultaneously generated from four channels to a single hypervector. The proposed encoding effectively captures spatial and temporal relations across and within the channels to represent a gesture. This HDC encoder achieves a high level of classification accuracy (97.8% with only 1/3 the training data required by state of the art SVM on the same task. HDC exhibits fast and accurate learning explicitly allowing online and continuous learning. We further enhance the encoder to adaptively mitigate the effect of gesture timing uncertainties across different subjects endogenously; further, the encoder inherently maintains the same accuracy when there is up to 30% overlapping between two consecutive gestures in a classification window.
2016 IEEE International Conference on Rebooting Computing (ICRC)