Analysis and evaluation of smartphone-based human activity recognition using a neural network approach

It has been more important to measure daily physical activity for several purposes. There have been a number of methods of measuring physical activity, such as self-reporting, attaching wearable sensors, etc. Since a smartphone has become widespread rapidly, physical activity can be easily measured by accelerometers in the smartphone. Although there were a number of studies for activity recognition exploiting smartphone acceleration data, there was little discussion with the influence of each axis of accelerometers for activity recognition. In this paper, we investigate how each axis of smartphone acceleration data is affected on the performance of human activity recognition using a neural network based classifier. Assuming that the smartphone is kept in a pants pocket, the acceleration data of a subject are collected during standing, walking, and running for ten minutes. A multilayer perceptron was used as an activity classifier to recognize the three activities. Using averages as features, the classifier with the x-axis features provides the best accuracies. Using standard deviations as features, however, the accuracies are better than those using averages.