Multi-sensor Gait Analysis for Gender Recognition

Abeer Mostafa, Toka Barghash, Asmaa Assaf, Walid Gomaa

Abstract

Gender recognition has been adopted recently by researchers due to its benefits in many applications such as recommendation systems and health care. The rise of using smart phones in everyday life made it very easy to have sensors like accelerometer and gyroscope in phones and other wearable devices. Here, we propose a robust method for gender recognition based on data from Inertial Measurement Unit (IMU) sensors. We explore the use of wavelet transform to extract features from the accelerometer and gyroscope signals along side with proper classifiers. Furthermore, we introduce our own collected dataset (EJUST-GINR-1) which contains samples from smart watches and IMU sensors placed at eight different parts of the human body. We investigate which sensor placements on the body best distinguish between males and females during the activity of walking. The results prove that wavelet transform can be used as a reliable feature extractor for gender recognition with high accuracy and less computations than other methods. In addition, sensors placed on the legs and waist perform better in recognizing the gender during walking than other sensors.

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