Indoor location and motion tracking system for elderly assisted living home


Tabbakha, Nour Eddin and Tan, Wooi Haw and Ooi, Chee Pun (2017) Indoor location and motion tracking system for elderly assisted living home. In: 2017 International Conference on Robotics, Automation and Sciences (ICORAS), 27-29 Nov. 2017, Melaka, Malaysia.

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This paper presents an indoor positioning system (IPS) and motion tracking system for the elderly, who are staying alone. The system is able to track the elderly location based on the room that they are currently situated and recognizes whether the elderly is moving or sitting. A smart wearable device featuring MPU6050 motion sensor, BLE beacon and Raspberry Pi Zero W is developed. The sensory data from MPU6050 will be analyzed to study the type of motion and publish to MQTT server. For location tracking, multiple Raspberry Pi 3 are used as BLE beacon scanners to scan for the beacon and publish its signal strength to an MQTT server. The datasets for the indoor location and motion type have also been developed in this research work. Weka, a machine learning software is used to evaluate and determine the most suitable classifier. The machine learning technique was implemented in Python programming language using scikit-learn, matplotlib, numpy and SciPy packages. The results are then pushed to an Internet of Things (IoT) platform for remote monitoring. The accuracy rate is 99% for the location tracking and 99.97% for motion recognition.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Machine learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 25 Apr 2021 14:07
Last Modified: 25 Apr 2021 14:07


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