Pattern recognition of time-series signals for movement and gesture analysis plays an important role in many fields as diverse as healthcare, astronomy, industry and entertainment. As a new technique in recent years, Deep Learning (DL) has made
tremendous progress in computer vision and Natural Language Processing (NLP), but largely unexplored on its performance for
movement and gesture recognition from noisy multi-channel sensor signals. To tackle this problem, this study was undertaken to classify diverse movements and gestures using four developed DL
models: a 1-D Convolutional neural network (1-D CNN), a Recurrent neural network model with Long Short Term Memory
(LSTM), a basic hybrid model containing one convolutional layer and one recurrent layer (C-RNN), and an advanced hybrid model containing three convolutional layers and three recurrent layers
(3+3 C-RNN). The models will be applied on three different databases (DB) where the performances of models were compared.
DB1 is the HCL dataset which includes 6 human daily activities of 30 subjects based on accelerometer and gyroscope signals. DB2 and DB3 are both based on the surface electromyography (sEMG) signal for 17 diverse movements. The evaluation and discussion for the improvements and limitations of the models were made
according to the result.
History
School
Mechanical, Electrical and Manufacturing Engineering
Published in
International Conference on Artificial Intelligence and Pattern Recognition
Citation
XIE, B., LI, B. and HARLAND, A.R., 2018. Movement and gesture recognition using deep learning and wearable-sensor technology. Presented at the International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2018), Beijing, China, 18-20th August, pp.26-31.