KEYNOTE SPEAKER

Fall and Balance Monitoring System: Prevention and Detection for Older Persons

Older persons tend to develop geriatric syndromes over time such as frailty, urinary incontinence that led to the tendency of falling. This study presents a balance monitoring solution that detects an older person balance instability and accidental fall occurrence. While fall detection devices nowadays have been utilized by many, the overall wellbeing of older persons still needs to be practically supervised by caretakers nearby. However, it has been shown that fall detection devices can only provide help to older persons after accident occur; not being able to detect abnormalities beforehand that it cannot by itself be used as a fall prevention intervention. Thus, a fuzzy logic-based fall prevention algorithm is developed to be integrated with fall detection devices, where two important variables to detect loss of balance are selected as the inputs for the system: Limit of Stability (LOS) and Degree of Sway (DOS). The system will notify the user if there are movement abnormalities to avoid fall or prolong balance instability. This study demonstrates the simulation of a fall prevention algorithm to improve the accuracy of detecting falls while monitoring balance instability. The results include simulations of the user’s movement with an indicator displaying fall risk level alongside with other health-related data. Enabling environments for older persons can be generated and ease the community’s challenge to prepare for the ageing society while improving their wellbeing in golden years.