CNN based approach for activity recognition using a wrist-worn accelerometer

Madhuri Panwar, S. Ram Dyuthi, K. Chandra Prakash, Dwaipayan Biswas, Amit Acharyya, Koushik Maharatna, Arvind Gautam, Ganesh R. Naik

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

37 Citations (Scopus)

Abstract

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017
Place of PublicationSouth Korea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2438-2441
Number of pages4
ISBN (Electronic)9781509028092
ISBN (Print)9781509028092
DOIs
Publication statusPublished - 13 Sep 2017
Externally publishedYes
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17

Keywords

  • Accelerometer
  • Activity recognition
  • Convolutional Neural Network
  • Deep learning

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  • Cite this

    Panwar, M., Ram Dyuthi, S., Chandra Prakash, K., Biswas, D., Acharyya, A., Maharatna, K., Gautam, A., & Naik, G. R. (2017). CNN based approach for activity recognition using a wrist-worn accelerometer. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 (pp. 2438-2441). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037349