Robust Automated Human Activity Recognition and Its Application to Sleep Research

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

7 Citations (Scopus)

Abstract

Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Pervasive sensors, such as wearable devices, have an increasing market penetration and generate a tremendous amount of data. The myriad of available clinical and consumer-grade wearables generate a continuous time series of a person's daily physical exertion and rest. Applying HAR to the activity time series can provide new insights by enriching the feature set in health studies, and enhancing the personalisation and effectiveness of health, wellness, and fitness applications. The analyses of complex health behaviours such as sleep, traditionally require a time-consuming manual interpretation by experts. This manual work is necessary due to the erratic periodicity and persistent noisiness of human behaviour. In this paper, we present a robust automated human activity recognition algorithm, which we call RAHAR. We test our algorithm in the application area of sleep research by providing a novel framework for evaluating sleep quality and examining the correlation between the aforementioned and an individual's physical activity. Our results improve the state-of-The-Art procedure in sleep research by 15% for area under ROC and by 30% for F1 score on average. However, application of RAHAR is not limited to sleep analysis and can be used for understanding other health problems such as obesity, diabetes, and cardiac diseases.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages495-502
Number of pages8
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 2 Jul 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1615/12/16

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ASJC Scopus subject areas

  • Computer Science Applications
  • Software

Cite this

Sathyanarayana, A., Ofli, F., Fernandez-Luque, L., Srivastava, J., Elmagarmid, A., Arora, T., & Taheri, S. (2016). Robust Automated Human Activity Recognition and Its Application to Sleep Research. In C. Domeniconi, F. Gullo, F. Bonchi, F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, R. Baeza-Yates, R. Baeza-Yates, Z-H. Zhou, & X. Wu (Eds.), Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 (pp. 495-502). [7836708] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2016.0077