This paper describes how to recognize certain types of human physical activities using acceleration data generated by a user's cell phone. We propose a recognition system in which a new digital low-pass filter is designed in order to isolate the component of gravity acceleration from that of body acceleration in the raw data. The system was trained and tested in an experiment with multiple human subjects in real-world conditions. Several classifiers were tested using various statistical features. High-frequency and low-frequency components of the data were taken into account. We selected five classifiers each offering good performance for recognizing our set of activities and investigated how to combine them into an optimal set of classifiers. We found that using the average of probabilities as the fusion method could reach an overall accuracy rate of 91.15%.
A recent study REF proposes a smartphone-based recognition system, in which the application of a low-pass filter and a combination of Multilayer Perceptron, LogitBoost and Support Vector Machine (SVM) classifiers reached an overall accuracy of 91.15% when the smartphone was held in the hand of the user.
A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
FNC/MobiSPC
Computer Science Engineering