Real-time fall risk assessment system based on acceleration data

According to recent statistics reported by the United Nations, the world's elderly population continues to grow at an unprecedented rate. The global population of elderly people is projected to reach nearly the 2.1 billion by 2050. With the global trend towards an increasingly ageing population, tele-health solutions are required to provide efficient healthcare services for the elderly. The elderly are usually faced with many problems resulting from the deterioration of health with increasing age. One of the major problems in the elderly is falls — balance and gait disorders. Falls have significant effects on both physiological and psychological condition of elderly people. They consequently lead to fracture, serious injuries, disability or eventually death. To reduce falls and their consequences, in this paper, we propose a novel fall risk assessment system that can dynamically perform gait analysis in order to detect the risk of falls in the elderly in real-time. Our system utilizes a gait analyzing service as a stream component. It exploits acceleration data derived from a mobile device to remotely monitor gait parameters in a timely fashion. The preliminary experimental results demonstrate that our fall risk assessment system can be used to detect the risk of falls in real world settings and it is accurate enough to differentiate between the walking pattern of the elderly with normal gait and that of the elderly with abnormal gait.