关键词: 跌倒检测, 时序分析, 移动设备, 旋转分量, 姿态角

Abstract: With the popularity of smart phones, using mobile devices to detect fall event is becoming more and more meaningful. The on-body position of mobile devices, is one kind of important context information, which affects the recognition of fall detection activity. Therefore, the paper proposes a fall detection method based on mobile device wearing position. A feature extraction method based on fusion of rotation mode component and attitude angle is proposed firstly, which uses the data sensed by the accelerometer and the gyroscope to calculate the rotation radius, the magnitude of the angular velocity as well as the attitude angle and then extracts a set of features to classify the wearing position of the mobile device by Logistic Regression (LR) model. On the basis of the wearing position, a fall detection algorithm based on time series analysis can be adjusted adaptively. Results show that the method achieves an accuracy of 95.32% on average in wearing position classification. In different positions, the accuracy of the time series analysis of the fall detection algorithm are all above 92%. Compared with the traditional fall detection method, this method has better fall detection effect in different wearing positions.

Key words: fall detection, time series analysis, mobile device, rotation mode component, attitude angle