Citation

Tabbakha, Nour Eddin and Tan, Wooi Haw and Ooi, Chee Pun (2019) Elderly Action Recognition System with Location and Motion Data. In: 2019 7th International Conference on Information and Communication Technology, ICoICT 2019, 24-26 July 2019, Kuala Lumpur, Malaysia.

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Abstract

The elderly population is experiencing high growth in most countries. In Malaysia, a majority of the households are dualearner families, leaving their elderly parents at home. Without caretakers to look after them, aging parents may be susceptible to accidents. Therefore, an elderly action recognition system which can automatically identify their actions and whereabouts at home is needed. In this paper, the development and testing of a wearable device with motion detection and indoor positioning based on Random Forest Classifier is presented. The experiment was conducted in a home environment (MMU Digital Home Lab). The action recognition system utilizes a gyroscope and an accelerometer to detect different types of motion which are walking, sitting, standing, sitting-down, standing-up, falling, and sleeping. The action recognition system can measure these motions with a 97.6% accuracy. On the other hand, the indoor positioning uses Bluetooth Low Energy beacon and scanners to pinpoint five locations which are living room, bedroom, kitchen, office and toilet with a 97.25% accuracy. The results from the measured motions and indoor locations are pushed to an online Internet of Things (IoT) platform for action recognition, data logging and analytics.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Motion detectors, Assisted living, Indoor positioning, motion tracking, IoT, Machine learning.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	24 Aug 2021 13:40
Last Modified:	24 Aug 2021 13:40
URII:	http://shdl.mmu.edu.my/id/eprint/8716

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