This work is to develop an intelligent driver-assistance system which can perceive the physiological state of a driver to avoid fatigue driving. The proposed system includes a camera, a wireless ElectroCardioGram (ECG) sensor patch, and a computation platform. The camera in front of a driver is to catch a face image which is processed to obtain features of a mouth for identifying a yawn. The sensor patch records ECG signals which are computed to yield six Heart Rate Variability (HRV) parameters. Seven healthy subjects of 6 males and 1 female had individually driven a car, which was embedded with our system, for 3 hours at a well-known route, mostly in a freeway road. Based on the captured video and measured ECG signals, the correlations between the yawning frequency and six HRV parameters are investigated by using the regression method to discover that the ratio (LF/HF) of Low-Frequency (LF) spectrum power over High-Frequency (HF) spectrum power yields the relatively highest correlation. In order to effectively identity driver's fatigue, the variations of differential LF/HF are further characterized to attain two thresholds which are accompanied with yawning frequencies to build a fair detection mechanism. The practical road tests demonstrate that the proposed system is very feasible and easily adapted to different drivers.
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.
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