A Real-Time Acceleration-Based Frailty Detection System for Elderly Care

Over the past decade, almost all countries in the world have experiencing growth in both the size and the proportion of elderly people in the population. The number of world's aging population is expected to reach about 2.1 billion by 2050, which is more than double figure from 1 billion in 2020. This statistic indicates the fact that we are entering the era of “super-aged society”. One of the major problems in the elderly comes in the form of a decline in health and physical function, generally called Frailty. The elderly with frailty typically suffer several health issues including loss of muscle mass, poor gait and balance and cognitive impairment. They are higher risk of falls and quick progression to dependency. Early identification of frailty is required to effectively reduce and prevent frailty outcomes. To address the problem, we propose a frailty detection system that can remotely analyze gait characteristics and detect frailty among elderly individuals in real-time. Our system utilizes an Ultra-wideband tag attached to the body of the elderly as a data collection device. It applies data stream mining techniques that perform machine learning-based gait analysis and detect frailty based on the patterns of acceleration data. The experimental result indicates that our proposed frailty detection system is practical to be used in real-world settings with relatively high accuracy rate, about 81.8% for overall accuracy.