Sociology

Archaeology

Pedagogy

Population

Location tracking

Bluetooth

Wireless

Support vector machine

Machine learning

Activity recognition

Demography

Wearable computer

Random forest

Embedded system

Tracking system

Artificial intelligence

Kalman filter

Psychology

Geography

Tracking (education)

Bluetooth Low Energy

Context (archaeology)

Computer science

Real-time computing

Activity tracker

Telecommunications

Internet of Things

Bulletin of Electrical Engineering and Informatics

The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.

A wearable device for machine learning based elderly's activity tracking and indoor location system