Gesture recognition intermediary robot for abnormality detection in human activities

Yahaya, S.W. ORCID: 0000-0002-0394-6112, Lotfi, A. ORCID: 0000-0002-5139-6565, Mahmud, M. ORCID: 0000-0002-2037-8348, Machado, P. ORCID: 0000-0003-1760-3871 and Kubota, N., 2019. Gesture recognition intermediary robot for abnormality detection in human activities. In: 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019), Xiamen, China, 6-9 December 2019. (Forthcoming)

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The world ageing population is increasing, giving rise to research targeted towards improving the quality of life and promoting the independent living of older adults. Detecting abnormalities in the daily activities of the older adults is relevant since abnormalities can be an early sign of health decline, prompting for the need for intervention. Current approaches to abnormality detection involve modelling the usual behavioural routine of the individual as a baseline and comparing subsequent behaviour to the baseline to detect abnormalities. This approach is prone to errors and not flexible since it does not take into account changes in human behavioural routine. Training is usually performed on pre-existing data making the abnormality detection model non-adaptive to new incoming data. An intermediary can be incorporated to enable model predictions to be communicated to humans for verification of the detected anomalies. This paper proposes a gesture recognition approach for facilitating interaction between humans and a robot intermediary. A model capable of recognising hand gestures corresponding to affirmations and denials is implemented. Preliminary evaluation shows that the proposed gesture recognition approach has the potential of being utilised in an assistive robot intermediary.

Item Type: Conference contribution
Creators: Yahaya, S.W., Lotfi, A., Mahmud, M., Machado, P. and Kubota, N.
Date: December 2019
Divisions: Schools > School of Science and Technology
Record created by: Jonathan Gallacher
Date Added: 18 Sep 2019 09:46
Last Modified: 12 Dec 2019 03:00
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