Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Walking Rehabilitation Evaluation Based on Gait Analysis

With the development of medicine and the improvement of people’s living standards, the issue of rehabilitation is getting more and more attention. Gait rehabilitation provides a brand-new treatment method for patients with walking disfunction. It is currently recognized as an advanced rehabilitation medical method in the world. In recent years, the number of patients suffering from dyskinesias in the lower limbs in China has been increasing, and the society's demand for walking rehabilitation treatment is also increasing. The emergence of gait rehabilitation solves the problem of fewer therapists and more patients, reduces the intensity of the therapist’s work, and has the incomparable advantage that traditional rehabilitation methods lack. However, because there are no mature related products in China at present, and the prices of foreign products are very expensive, domestic medical institutions have not yet put them into practice. Accelerating the development of gait re-habilitation equipment is of great significance for improving China’s medical level, improving the quality of life of patients, and reducing social burden. Usually, high-precision optical sensors are installed on human limbs or using high-speed cameras to capture motion. However, due to the high cost of the equipment, the relative high price of image processing software when processing the collected motion data. In this paper, the acceleration sensors are installed on the human body and the data in a gait cycle can be obtained. After smoothing, using it as the input signal for gait feature extraction and classification. In order to classify normal gait and abnormal gait for evaluation and better walking rehabilitation.

EEMD and bidimensional RLS to suppress physiological interference for heterogeneous distribution in fNIRS study

Near-infrared spectroscopy(NIRS)can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical,which can be deployed for the cerebral function study.However,NIRS-based cerebral function detection accuracy can be signi¯cantly in°uenced by the physiological activities such as cardic cycle,respiration,spontaneous low-frequency oscillation and ultra-low frequency oscillation.The distribution difference of the capillary,artery and vein leads to the heterogeneity feature of the cerebral tissues.In the case that the heterogeneity is not serious,good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach.The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious.In this study,the cerebral function activity signal is extracted using recursive least square(RLS)method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition(EEMD)algorithm.The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal.Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology.The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.