A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

Abstract

This paper proposes a novel dimension reduction technique for physical activity recognition from a waistmounted accelerometer. Firstly, the wavelet transform is used to extract features from the acceleration signals. Then the proposed technique is used to reduce the dimension of the wavelet features. Finally, the Multi-Layer Perceptron Neural Network (MLPNN) is used to recognize the physical activities from the reduced features. In our experiments, 5 volunteers who were healthy with the ages between 21 to 25 year old were asked to mount a tri-axial accelerometer at the right side of their waists. Next, the volunteers were asked to perform 5 daily-life physical activities: 1) walking 2) standing up from a chair 3) sitting down on a chair 4) lying down on a bed and 5) getting up from a bed; and 5 falling events: 1) forward fall 2) backward fall 3) falling to the right side 4) falling to the left side and 5) falling when standing up. In order to evaluate the performance of the proposed algorithm, the precision of the recognition and the total number of the used operators were used. The performance of the recognition with different setting of mother wavelets, vanishing moments, the rate of the dimension reduction, and the number of nodes of the hidden layer were evaluated. From the experiments, the proposed dimension reduction not only reduce the total number of the used operators but also increase the precision of the recognition.