Visualization discriminant technology for human body physiological status based on multi-sensor is applicable to current wearable body area network devices. Three wearable human physiological signal sensors are utilized. The feature vector is extracted from the pre-processed pulse signal that acquired from pulse sensor

and the physiological status of the human body is classified into"normal status"and"event status"by the method of support vector machine. The classification accuracy rate is more than 90 % for 16 experimenters. Respiratory wave sensor and body temperature sensor are also used as auxiliary methods. Results of visualization of comprehensive assessment of the physiological status of the human body is obtained according to the binary coding for data fusion of the three classification results.