Wearable Sensor-Based Gait Analysis for Age and Gender Estimation
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 an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/8/2424 |
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| author | Md Atiqur Rahman Ahad Thanh Trung Ngo Anindya Das Antar Masud Ahmed Tahera Hossain Daigo Muramatsu Yasushi Makihara Sozo Inoue Yasushi Yagi |
| author_facet | Md Atiqur Rahman Ahad Thanh Trung Ngo Anindya Das Antar Masud Ahmed Tahera Hossain Daigo Muramatsu Yasushi Makihara Sozo Inoue Yasushi Yagi |
| author_sort | Md Atiqur Rahman Ahad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:15:34Z |
| format | Article |
| id | doaj.art-839cd3a02d02403e8ad47917d91fd538 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:15:34Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-839cd3a02d02403e8ad47917d91fd5382023-11-19T22:38:00ZengMDPI AGSensors1424-82202020-04-01208242410.3390/s20082424Wearable Sensor-Based Gait Analysis for Age and Gender EstimationMd Atiqur Rahman Ahad0Thanh Trung Ngo1Anindya Das Antar2Masud Ahmed3Tahera Hossain4Daigo Muramatsu5Yasushi Makihara6Sozo Inoue7Yasushi Yagi8Department of Media Intelligent, Osaka University, Ibaraki 567-0047, JapanDepartment of Media Intelligent, Osaka University, Ibaraki 567-0047, JapanElectrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USADepartment of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, BangladeshGraduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, JapanDepartment of Media Intelligent, Osaka University, Ibaraki 567-0047, JapanDepartment of Media Intelligent, Osaka University, Ibaraki 567-0047, JapanGraduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, JapanDepartment of Media Intelligent, Osaka University, Ibaraki 567-0047, JapanWearable 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.https://www.mdpi.com/1424-8220/20/8/2424gaitrecognitionwearable sensorage estimationgendersmartphone |
| spellingShingle | Md Atiqur Rahman Ahad Thanh Trung Ngo Anindya Das Antar Masud Ahmed Tahera Hossain Daigo Muramatsu Yasushi Makihara Sozo Inoue Yasushi Yagi Wearable Sensor-Based Gait Analysis for Age and Gender Estimation Sensors gait recognition wearable sensor age estimation gender smartphone |
| title | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation |
| title_full | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation |
| title_fullStr | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation |
| title_full_unstemmed | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation |
| title_short | Wearable Sensor-Based Gait Analysis for Age and Gender Estimation |
| title_sort | wearable sensor based gait analysis for age and gender estimation |
| topic | gait recognition wearable sensor age estimation gender smartphone |
| url | https://www.mdpi.com/1424-8220/20/8/2424 |
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