Preprocessing techniques for daily healthcare device using SHIMMER™ wireless sensor platform

Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the ECG signals. To tackle this problem, in this paper, we present a human activity recognition and ECG based on SHIMMER
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Wireless sensor platform. We are developing ubiquitous system for constant monitoring of human health that uses a 3D accelerometer and an electrocardiogram communicating wirelessly to a personal computer. This system should provide an automatic alert whenever a person's condition indicates a risk of heart attack or other danger. We collected acceleration data and bodily movement records from our staffs and from volunteer in order to determine from the movement data what sorts of movements. Our system is able to identify the type of human movements from the acceleration data and use this in combination with ECG data to decide whether to issue an alert.