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Using Machine Learning Techniques to Optimize Fall Detection Algorithms in Smart Wristband

Zheng, Ge and Zhang, Hongtao and Zhou, Keming and Hu, Huosheng (2019) Using Machine Learning Techniques to Optimize Fall Detection Algorithms in Smart Wristband. In: 2019 25th International Conference on Automation and Computing (ICAC), 2019-09-05 - 2019-09-07, Lancaster, UK.

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Abstract

The consumer electronics market is already saturated with wearable devices that intend to be used to detect falls and request help from carers or family members. However, these products have a high rate of false alarms which affect their reliable performance. To provide the high accuracy and high precision of fall detection for the elderly, this paper presents a machine learning approach to improve the fall detection accuracy and reduce the false alarms. Three machine learning algorithms are deployed in this research, namely the K-Means, Perceptron Neural Network (PNN), and Convolutional Neural Network (CNN) algorithms. A development board with a 9-axis inertial sensor unit is used as a prototype of wristband to collect data and identify falls from seven daily activities. These data is then used to train and test machine learning algorithms. Experimental results show that the CNN algorithm achieves the highest accuracy comparing with K-mean, PNN and the algorithm used in the existing wristbands.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2019 25th International Conference on Automation and Computing (ICAC)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 06 Jul 2021 09:32
Last Modified: 06 Jul 2021 10:15
URI: http://repository.essex.ac.uk/id/eprint/27634

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