Self-adaptive fall-detection apparatus embedded in glasses

Fall injury is already a major problem in elderly health care. This work develops a self-adaptive fall-detection apparatus which is embedded in glasses for users easily to put on. The proposed system adopts a 9-axis sensing module of a triaxial magnetometer, accelerometer and gyroscope. First, the magnetometer is to filter out some normal events like head rotating, based on variations of rotation angles which are modeled by the Gaussian mixture model. Second, the sensed signals from a triaxial accelerometer are computed to obtain differential acceleration values at three directions, which are integrated and then compared with a threshold. Here, the threshold is determined by the Gaussian mixture model and optimized thresholding technique. Our system can update an adequate threshold on the fly. Third, when a fall occurs, its direction is identified using an accelerometer and a gyroscope. The experimental results reveal that the proposed system achieves accuracy rate of 92.1%, a specificity of 98.7%, and a sensitivity of 81.7%. As compared to the conventional fall-detection systems, the proposed system not only shows fairly good performance but also provides convenient, comfortable and non-intrusive wearing. Therefore, the system proposed herein can be widely spread in various head-mounted devices for health care applications.