Human activity recognition with inertial sensors using a deep learning approach

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 . doi:10.1109/ICSENS.2016.7808590

TitleHuman activity recognition with inertial sensors using a deep learning approach
AuthorsZebin, T., Scully, P.J. and Ozanyan, K.B.
TypeConference 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.

KeywordsDeep Learning
Activity Recognition
Year2016
Conference2016 IEEE SENSORS
PublisherIEEE
Accepted author manuscript
Publication dates
Published09 Jan 2017
ISSN1930-0395
ISBN9781479982875
Digital Object Identifier (DOI)doi:10.1109/ICSENS.2016.7808590
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/abstract/document/7808590
Web address (URL)https://ieeexplore.ieee.org/abstract/document/7808590

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