Research And Implementation Of The Key Technologies For Electronic Smart Clothing

The electronic smart clothing has used in sports, fire flight and rescue and medical health, these reveal it becoming an interesting topical research in recent years. However, the smart clothing research still at initial stage in domestic, it’s hard to meet variety of the user’s demands.This paper Analyses the key technologies of electronic smart clothing, and talks about the realization of its function. An intelligent environment monitoring clothing system platform which contents collect sensation, data processing, display and transmission was build on use several key technologies in smart clothing. This platform can collect signals of Formaldehyde, Dust, Light and Temperature by a sensor which install in smart clothing. These signals can transmit to mobile phones in WI-FI. In addition, this platform and several new technologies like Sensor, Microcomputer, Wireless communication used in smart clothing, which also turn to environmental monitoring.For the smart clothing has been topical application in health protects, This paper design a system of intelligent health protection This system centered on Microduino-core+ and affix with Heart rate sampling, Body temperature acquisition, Acceleration, GSM, GPS positioning modules. The users can measure their body temperature, heart rate, location at anytime or anywhere, if the users have some accidents just like fall down their sensors will automatic alarm and transmit to their guardian. Above all, this key technology can applied to the Wilderness rescue, military, fire and other fields.This paper analysis the pulse waveform signal and its original mechanism, and proposed the heart rate measurement algorithm for pulse waveform of cyclical fluctuations. This algorithm has advantage in measurement error, because it calculates the heart rate by getting waveform match time between adjacent two cycle on top values.This paper proposes limit value through experimental data analysis and support Vector machine(SVM) classification algorithm in distinguish the human daily life behavior and fall behavior. The limit value back up the Level 3 down recognition algorithm underline three-axis acceleration sensor. The experiments reveal it very accurate in sense.