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In view of problems of low positioning accuracy of underground personnel positioning system and incomplete functions of underground personnel vital signs monitoring system, a wearable device with functions of personnel positioning, falling or lying posture monitoring and heart rate monitoring was studied. The device adopts combination of RSSI ranging and step calculation to improve positioning accuracy of underground personnel. Three-axis acceleration sensor is used to detect abnormal movement of person, and heart rate sensor is used to obtain heart rate data. The test results show that the device can achieve accurate positioning of underground personnel, can accurately determine falling or lying posture of underground personnel, and alarm abnormal state.

It is of great importance for the detection of epilepsy in clinical applications. Based on the limitations of the common used approximate entropy (ApEn) in the epilepsy detection, this paper analyzes epileptic EEG signals with the sample entropy (SampEn) approach, a new method for signal analysis with much higher precision than that of the ApEn. Data analysis results show that the values from both ApEn and SampEn decrease significantly when the epilepsy is burst. Furthermore, the SampEn is more sensitive to EEG changes caused by the epilepsy, about 15%-20% higher than the results of the ApEn.