Gender Classification Using Smartphone Sensors and Machine Learning Approaches

Abstract

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.