Fall perception for elderly care: A fall detection algorithm in Smart Wristlet mHealth system

Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level.