Healthy: A Diary System Based on Activity Recognition Using Smartphone

An activity-diary system, named Healthy, is presented in this paper. Healthy can infer users diary of physical activities and energy expenditure based on METS (Metabolic Equivalents) values via recognizing general human activities. In this system, we design a two-layer classifier which costs less energy and memory with satisfactory accuracy. Our classifier divides the activities into two categories: periodic and nonperiodic. And a different sub-classifier is applied for each category. Meanwhile, We design a state listener to recognize more complicated activities. To further improve recognition accuracy, in the second layer sub-classifier, we put forward an adaptive framing algorithm based on the period length of periodical activities to determine the time during which features are extracted. By testing Healthy in real situation, we obtained an average recognition accuracy of 98.0%.