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

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