Human activity recognition with inertial sensors using a deep learning approach

Zebin, Tahmina, Scully, Patricia J. and Ozanyan, Krikor B. (2017) Human activity recognition with inertial sensors using a deep learning approach. In: IEEE Sensors, SENSORS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., USA. ISBN 9781479982875

[img]
Preview
PDF (manuscript_reviewed) - Submitted Version
Available under License ["licenses_description_other" not defined].

Download (267kB) | Preview

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.

Item Type: Book Section
Uncontrolled Keywords: convolution,convolutional neural networks (cnn),feature extraction,human activity recognition (har),signal processing,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2208
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 28 May 2019 13:30
Last Modified: 17 Jun 2020 23:32
URI: https://ueaeprints.uea.ac.uk/id/eprint/71147
DOI: 10.1109/ICSENS.2016.7808590

Actions (login required)

View Item View Item