Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors

Abstract

Activity classification in smartphones helps us to monitor and analyze the physical activities of the user in daily life and has potential applications in healthcare systems. This paper proposes a descriptor-based approach for activity classification using built-in sensors of smartphones. Accelerometer and gyroscope sensor signals are acquired to identify the activities performed by the user. In addition, time and frequency domain signals are derived using the collected signals. In the proposed approach, two descriptors, namely, histogram of gradient and centroid signature-based Fourier descriptor, are employed to extract feature sets from these signals. Feature and score level fusion are explored for information fusion. For classification, we have studied the performance of multiclass support vector machine and $k$ -nearest neighbor classifiers. The proposed approach is evaluated on two publicly available data sets, namely, UCI HAR data set and physical activity sensor data. Our experimental results show that the feature level fusion provides better performance than the score level fusion. In addition, our approach provides considerable improvement in classifying different activities as compared with the existing works. The average activity classification accuracy achieved using the proposed method is 97.12% as against the existing work, which provided 96.33% on UCI HAR data set. On the second data set, the proposed approach attained 96.83% classification accuracy, whereas the existing work achieved 90.2%.