Human activity classification by ECG and accelerometers aided by fuzzy logic

This paper proposes a classification system for human activity using a multi-sensor system with a built-in electrocardiograph and 3D accelerometers. The multi-sensor system unconstraintly measures biological information, and provides these data to personal computer by wireless communication. We classify human activity by the biological information. The sensor detects the electrocardiogram and triaxial acceleration data of subject. The subject has several activities such as “Walking”, “Walking Stairs”, “Rest” and “Strength training”. The proposed system classifies these activities by a decision tree. Branch conditions of the decision tree are defined by fuzzy membership functions. These fuzzy membership functions are constructed by exercise intensity, distinction frequency and postures. We compared our proposed method with a method using only acceleration data to show the effectiveness of the multi-sensor system. As the results, the proposed method obtained high classification accuracy.