Triaxial accelerometer-based real time fall event detection

Falls in older people and the injuries are a major problem for their welfare, confidence and happiness and represent a public health burden on health care cost. In this study, an automatic fall detection system consisting of a triaxial accelerometer and a smartphone is evaluated. The system classifies raw sensor data by using an online algorithm. Based on physical characteristics of activity, four time-domain features are abstracted, which are all independent of the sensor orientation with respect to the body. A decision tree is used as a classifier running on smartphone. Meanwhile, permitting control is adopted to save power by reducing data traffic. The accelerometer and Bluetooth unit are bounded as a wearable unit and placed on the subject´s waist/chest. A laboratory-based trial involving ten subjects during different time was undertaken; results indicate an overall accuracy of 92% and response time of less than 6 seconds, which demonstrates excellent effectiveness of this system.