Online human activity recognition using low-power wearable devices

Ganapati Bhat, Ranadeep Deb, Vatika Vardhan Chaurasia, Holly Shill, Umit Ogras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Human activity recognition (HAR) has attracted significant research interest due to its applications in health monitoring and patient rehabilitation. Recent research on HAR focuses on using smartphones due to their widespread use. However, this leads to inconvenient use, limited choice of sensors and inefficient use of resources, since smartphones are not designed for HAR. This paper presents the first HAR framework that can perform both online training and inference. The proposed framework starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer data. Using these features, we design a neural network classifier which is trained online using the policy gradient algorithm. Experiments on a low power IoT device (TI-CC2650 MCU) with nine users show 97.7% accuracy in identifying six activities and their transitions with less than 12.5 mW power consumption.

Original languageEnglish (US)
Title of host publication2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450359504
DOIs
StatePublished - Nov 5 2018
Event37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - San Diego, United States
Duration: Nov 5 2018Nov 8 2018

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152

Other

Other37th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018
CountryUnited States
CitySan Diego
Period11/5/1811/8/18

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Bhat, G., Deb, R., Chaurasia, V. V., Shill, H., & Ogras, U. (2018). Online human activity recognition using low-power wearable devices. In 2018 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2018 - Digest of Technical Papers [a72] (IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3240765.3240833