Mobile health monitoring system based on activity recognition using accelerometer

We propose a new method to recognize a user’s activities of daily living with accelerometers and RFID sensor. Two wireless accelerometers are used for classification of five human body states using decision tree, and detection of RFID-tagged objects with hand movements provides additional instrumental activity information. Besides, we apply our activity recognition module to the health monitoring system. We derive linear regressions for each activity by finding the correlations between the attached accelerometers and the expended calories calculated from gas exchange analyzer under different activities. Finally, we can predict the expended calories more efficiently with only accelerometer sensor depend on the recognized activity. We implement our proposed health monitoring module on smart phones for better practical use.