OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender

Thanh Trung Ngo, Md Atiqur Rahman Ahad, Anindya Das Antar, Masud Ahmed, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi, Sozo Inoue, Tahera Hossain, Yuichi Hattori,
The Competition at The 12th IAPR International Conference On Biometrics
(Not Available)
(Not Available)
6 pages
2019-01-01
Greece
http://www.am.sanken.osaka-u.ac.jp/GAG2019/
Recently, wearable computing resources, such as smartphones, are developing fast due to the advancements of technology and their great supports to human life. People are using smartphone for communication, work, entertainment, business, traveling, and browsing information. However, the health-care application is very limited due to many challenges. We would like to break the limitation and boost up the research to support human health. One of the important steps for a health-care system is to understand age and gender of the user through gait, who is wearing the sensor. Gait is chosen because it is the most dominant daily activity, which is considered to contain not only identity but also physical, medical conditions. To this end, we organize a challenging competition on gender and age prediction using wearable sensors. The evaluation is mainly based on the published OU-ISIR inertial dataset which is currently the world largest inertial gait dataset∗.

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