Abstract
The identification of people’s gender and events in our everyday applications by means of gait knowledge is becoming important. Security, safety, entertainment, and billing are examples of such applications. Many technologies could also be used to monitor people’s gender and activities. Existing solutions and applications are subject to the privacy and the implementation costs and the accuracy they have achieved. For instance, CCTV or Kinect sensor technology for people is a violation of privacy, since most people don’t want to make their photos or videos during their daily work. A new addition to the gait analysis field is the inertial sensor-based gait dataset. Therefore, in this paper, we have classified people’s gender from an inertial sensor-based gait dataset, collected from Osaka University. Four machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Bagging, and Boosting have been applied to identify people’s gender. Further, we have extracted 104 useful features from the raw data. After feature selection, the experimental outcome exhibits the accuracy of gender identification via the Bagging stands at around 87.858%, while it is about 86.09% via SVM. This will in turn form the basis to support human wellbeing by using gait knowledge.
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Pathan, R.K., Uddin, M.A., Nahar, N., Ara, F., Hossain, M.S., Andersson, K. (2021). Gender Classification from Inertial Sensor-Based Gait Dataset. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_51
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DOI: https://doi.org/10.1007/978-3-030-68154-8_51
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