Novel tent pooling based human activity recognition approach

Nowadays, mobile devices have been widely used worldwide and they have variable sensors. By using these sensors, many applications for instance navigation, healthcare, and human activity recognition (HAR) have been developed. The primary objective of this paper is to present a multi lightweight HAR method with high prediction performance. To implement this objective, novel pooling methodology is presented and it is called as tent pooling since pooling is processed using the variable size of blocks. 2 × 2, 3 × 3, 4 × 4, 5 × 5, 6 × 6, 7 × 7 and 8 × 8 sized non-overlapping blocks are used for pooling and maximum, minimum and average pooling methods. These pooling methods have been utilized as a feature generation model. The generated features are forwarded to the ReliefF feature selection method. ReliefF is a weight-based feature selector and uses Manhattan distance based fitness function. By using relief, the weights of the features are calculated and the negative weighted features are eliminated. This operation is named positive weighted feature selection. The selected positive weighted features are classified using a cubic support vector machine (SVM). To test the proposed method, a publicly and freely published HAR dataset is used. There are 6 activities in the used dataset. Also, the results of the proposed method were compared to other state of art methods. The best accuracy rate of the proposed tent pooling based HAR method was calculated as 99.81% using the average pooling. This results clearly prove that the success of the proposed tent pooling based method. The proposed method is also simple and effective. It has the highest success rates among the selected state-art-of HAR methods.