Smart-clothes System for Realtime Privacy Monitoring on Smart-phones

In this paper, we propose a method to infer the user`s behavior and situation through collected data from multi-sensor equipped with a smart clothing and it was implemented as a smart-phone App. This smart-clothes is able to monitor wearer users` health condition and activity levels through the gyro, temp and acceleration sensor. Sensed vital signs are transmitted to a bluetooth-enabled smart-phone in the smart-clothes. Thus, users are able to have real time information about their user condition, including activities level on the smart-application. User context reasoning and behavior determine is very difficult using multi-sensor depending on the measured value of the sensor varies from environmental noise. So, the reasoning and the digital filter algorithms to determine user behavior reducing noise and are required. In this paper, we used Multi-black Filter and SVM processing behavior for 3-axis value as a representative value of one.