S Filter Based Sensor Fusion for Activity Recognition using Smartphone

Activity Recognition based on the sensors available on a smartphone is becoming a widely researched area. Smartphones are capable of collecting vital data from the sensors. These sensors include acceleration sensors, position sensors, vision sensors, audio sensors, temperature sensors and direction sensors. In this paper we propose a filter based sensor fusion system that uses smartphones accelerometer and gyroscope data to identify activities performed. The data collected from the accelerometer and gyroscope is labeled according to the activity that is performed. Stastical features such as Mean, Standard Deviation and Skewness are extracted from the data. Accelerometer and gyroscope data are combined using Complementary Filter and Kalman Filter, and the stastical features are extracted. The classification and prediction is performed by Support Vector Machine (SVM). The experiment results show that the data fused using the Kalman Filter has higher accuracy than raw data and Complementary Filter.