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Movement and gesture recognition using deep learning and wearable-sensor technology

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conference contribution
posted on 06.09.2018 by Baao Xie, Baihua Li, Andy Harland
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.

Publisher

© ACM

Version

AM (Accepted Manuscript)

Publication date

2018

Notes

© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in AIPR 2018 Proceedings of the 2018 International Conference on Artificial Intelligence and Pattern Recognition, http://dx.doi.org/10.1145/3268866.3268890.

ISBN

9781450365246

Language

en

Location

Beijing

Exports