Balancing Precision and Battery Drain in Activity Recognition on Mobile Phone
Many achievements have been announced with real time running capability for activity recognition (AR) using mobile accelerometer. However, they also have weak points including low accuracies especially in multiple-subject activity recognition and lacking of evidences about power consumption. In this paper, we contribute a novel method for extracting features on time domain and frequency domain. These different features were then respectively applied to Support Vector Machine (SVM) classifier and Dynamic Time Warping (DTW) method in order to find out the most effective combinations. Our own data and SCUTT-NAA dataset were used in our experiment. Accuracy rates of 95% and 97% in multiple-subject AR were achieved by respectively using SVM and DTW from time domain features (TF). These approaches were then implemented on a mobile phone to measure the power consumptions. SVM using time feature method was found as the most effective method for balancing accuracy and energy consumption.