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Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition
Open AccessArticle

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan
Author to whom correspondence should be addressed.
Sensors 2020, 20(8), 2424;
Received: 31 January 2020 / Revised: 20 April 2020 / Accepted: 22 April 2020 / Published: 24 April 2020
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network. View Full-Text
Keywords: gait; recognition; wearable sensor; age estimation; gender; smartphone gait; recognition; wearable sensor; age estimation; gender; smartphone
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Ahad, M.A.R.; Ngo, T.T.; Antar, A.D.; Ahmed, M.; Hossain, T.; Muramatsu, D.; Makihara, Y.; Inoue, S.; Yagi, Y. Wearable Sensor-Based Gait Analysis for Age and Gender Estimation. Sensors 2020, 20, 2424.

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