Background: The increasing cost in terms of money and healthcare resources is driving healthcare providers to provide home-based telecare instead of institutionalized healthcare. Falling is one of the most common and dangerous accidents for elderly individuals and a significant factor affecting the living quality of the elderly. Many efforts have been put toward providing a robust method to detect falls accurately and in a timely manner. This study facilitated a reliable, safe, and real-time home-based healthcare environment, which we have termed the Home Healthcare Sentinel System (HONEY), to detect falls for elderly people in the home telecare environment. The basic idea of HONEY is a three-step detection scheme that consists of multimodality signal sources, including an accelerometer sensor, audio, images, and video clips via speech recognition and on-demand video techniques.
Materials and methods: The magnitude of acceleration, corresponding to a user's movements, triggers fall detection combining speech recognition and on-demand video. If a fall occurs, an alarm e-mail is delivered to medical staff or caregivers at once, containing the fall information, so that caregivers could make a primary diagnosis based on it. This article also describes the implementation of the prototype of HONEY.
Results: A comprehensive evaluation with 10 volunteers shows that HONEY has high accuracy of 94% for fall detection, 18% higher than the Advanced Magnitude Algorithm (AMA), which is a wearable sensor-based method, and the false-positive and false-negative rates are 3% and 10%, respectively, 19% and 16% lower than AMA, respectively. The average response time for a detected fall is 46.2 s, which is also short enough for first aid.
Conclusions: In summary, HONEY provides a highly reliable and convenient fall detection solution for the home-based environment.