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

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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
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Depositing User: LivePure Connector
Date Deposited: 28 May 2019 13:30
Last Modified: 30 Sep 2021 17:31
DOI: 10.1109/ICSENS.2016.7808590

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