Fall detection system for healthcare quality improvement in residential care facilities

Falls and fall-induced injuries among elderly people have become an important public health concern in an aging society. More than 50% of those living in residential care facilities fall at least once a year, and about half of them fall more than once a year. Because fall-induced injuries result in health decline and increasing medical care cost, fall management plays an important role in the residential care facilities. In this study, we propose a fall-detecting system based on wearable sensor and real-time fall detection algorithm. We use a head-mounted tri-axial accelerometer to capture the movement data of human body and develop a fall detection method to distinguish between falls and daily activities. A ZigBee-based alarm system is also proposed. It provides location information of the user in the case of emergency. When a fall happens, the caregivers can know where the accident is and then give immediate care for the residents directly to reduce severe injury, which could improve the healthcare quality in residential care facilities. The experimental results have demonstrated the proposed scheme has high reliability and sensitivity for fall detection. It fulfills the requirements of fall detection.