Accelerometer-based human fall detection using fuzzy entropy

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Howedi, A., Lotfi, A. ORCID: 0000-0002-5139-6565 and Pourabdollah, A. ORCID: 0000-0001-7737-1393, 2020. Accelerometer-based human fall detection using fuzzy entropy. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): 2020 conference proceedings. Piscataway, NJ: IEEE. ISBN 9781728169323

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Official URL: https://doi.org/10.1109/fuzz48607.2020.9177577

Abstract

Falls are considered as one of the greatest risks and a fundamental problem in health-care for older adults living alone at home. The number of older adults living alone in their own homes is increasing worldwide due to the high expense of health care services. Therefore, it is important to develop an accurate system with the ability to detect human falls during daily activities. The focus of this study is to distinguish and detect human falls in Activities of Daily Living (ADL) based on data acquired from an accelerometer device. In this paper, a novel method based on Fuzzy Entropy measure is investigated to detect and distinguish human fall from other activities with a high degree of accuracy. The proposed method is tested and evaluated based on a publicly available URFD dataset. The experimental results show that Fuzzy Entropy achieved a sensitivity and specificity of 100% and 97.8%, respectively. Comparisons with other methods have also provided further support to the proposed method.

Item Type:

Chapter in book

Description:

Paper presented at the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, Scotland, 19-24 July 2020.

Creators:

Howedi, A., Lotfi, A. and Pourabdollah, A.

Publisher:

IEEE

Place of Publication:

Piscataway, NJ

Date:

2020