Zebin, T., Scully, P.J. and Ozanyan, K.B. 2016. Human activity recognition with inertial sensors using a deep learning approach. 2016 IEEE SENSORS. Orlando, FL, USA 30 Oct - 03 Nov 2016 IEEE . https://doi.org/10.1109/ICSENS.2016.7808590
| Title | Human activity recognition with inertial sensors using a deep learning approach |
|---|---|
| Authors | Zebin, T., Scully, P.J. and Ozanyan, K.B. |
| Type | Conference paper |
| Abstract | Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks. |
| Keywords | Deep Learning |
| Activity Recognition | |
| Year | 2016 |
| Conference | 2016 IEEE SENSORS |
| Publisher | IEEE |
| Accepted author manuscript | |
| Publication dates | |
| Published | 09 Jan 2017 |
| ISSN | 1930-0395 |
| ISBN | 9781479982875 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ICSENS.2016.7808590 |
| Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/abstract/document/7808590 |
| Web address (URL) | https://ieeexplore.ieee.org/abstract/document/7808590 |