Multi-sensor Gait Analysis for Gender Recognition Abeer Mostafa 1 a , Toka Ossama Barghash 1 , Asmaa Al-Sayed Assaf 1 and Walid Gomaa 1,2 b 1 Cyber-Physical Systems Lab, Egypt Japan University of Science and Technology, Alexandria, Egypt 2 Faculty of Engineering, Alexandria University, Alexandria, Egypt Keywords: Gender Recognition, IMU, Wavelet Transform, Supervised Learning. 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. 1 INTRODUCTION Gender recognition has been studied widely in the last decade. Various types of data have been used to recognise the gender of a person such as images, voice signals or inertial measurements based on the motion of the person (Lu et al., 2014), (Garofalo et al., 2019) and (Zhang et al., 2017). There are many useful ap- plications that depend on gender recognition such as speech recognition (Yuchimiuk, 2007), recommenda- tion systems (Shepstone et al., 2013), and most im- portantly health care applications (Rosli et al., 2017). However, there is a huge lack of datasets and accuracy in the methods that are developed for gender recogni- tion and the analysis of the data itself. Inertial Mea- surement Units (IMUs) are known to be embedded in many wearable devices which lead to useful applica- tions. It will be convenient to recognise gender based on their readings (accelerometer, gyroscope, etc). Datasets collected from IMU sensors are not al- ways publicly available and most publicly available datasets don’t focus on diversity of sensor placements on the human body to get the accelerometer and gyro- scope signals. For these reasons, we introduce a new dataset (EJUST-GINR-1) which is collected from col- a https://orcid.org/0000-0002-8971-4311 b https://orcid.org/0000-0002-8518-8908 lege students to record accelerometer and gyroscope signals from their walking activity. We record signals from smart watches and IMU sensors placed at eight different parts of the human body. We study which part of the human body effectively and uniquely iden- tifies the gender. We run experiments on each sensor individually and also on combinations of sensors to see their effect on the classification accuracy, and in general we analyse the reliability of each body part in uniquely determining the gender of the person from the inertial movements of the corresponding body part during walking. We run experiments on a different dataset and analyse the cultural effect that can be im- portant in changing the nature of the data. Further- more, we propose a reliable approach to do feature extraction followed by classification to recognise the gender based on IMU readings. There are many approaches to extract relevant fea- tures used for classification. Recently the most promi- nent approach is using deep neural networks. How- ever, these methods perform well when there is a huge amount of data. This size of data is not always avail- able when the recognition is based on data coming from sensors because the process of collecting the data and annotating it takes much time and effort. Moreover, the process may require the participation of many people and the availability of the sensors may be limited. Accordingly, we propose the use of a fea- Mostafa, A., Barghash, T., Assaf, A. and Gomaa, W. Multi-sensor Gait Analysis for Gender Recognition. DOI: 10.5220/0009792006290636 In Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2020), pages 629-636 ISBN: 978-989-758-442-8 Copyright c  2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 629