부산대학교 도서관

Gait analysis is typically associated with the pattern of the human walk. Determining it with computational means can be helpful in many ways-from identifying individual humans to detecting gait-related diseases. In comparison to the expensive approaches and devices, which are limited to laboratories, smart- phones with motion sensors are low-cost solutions through which we can analyze mobility and gait patterns. Thus, in this work, we present the usage of smartphone sensors for data acquisition followed by machine learning-based gender classification, which is a baseline for different gait-related tasks. In this regard, we collected data from 14 persons in different tracks, paces, and movement styles; after adequate normalization, iterative feature elimination, and Monte-Carlo experiment-based ML training, we found the Decision Tree is the most optimal algorithm with attaining 90.6 % balanced-accuracy.