Ascending and Descending Walking Recognition using Smartphone

In the recent decades, activity recognition for human behavior monitoring has been one of the major interesting research subjects. This paper investigates fractal dimension (FD) as an effective feature for human activity recognition. The target activities are descending and ascending stairs, which are performed by six male participants. To testify the validity of feature, the classification methods are k-nearest neighbors (kNN) and support vector machine (SVM). The best result achieved in this results is using SVM classifier and obtaining 95.30% for walking downstairs and 88.56% for walking upstairs with window of 4s and equalized data. Another aim of this paper is to examine the impact of database on the classification results. The finding in the paper provides evidence that the classification accuracy is affected insignificantly by the ratio of descending and ascending data.