Important dates (GMT):

Test dataset release: 15 Dec, 2018 03 Jan, 2019

Registration close: 15 Dec, 2018 03 Jan, 2019

Prediction results and technical reports submission deadline: 1 Feb, 2019 5 Feb, 2019

Results announce: 2 Feb, 2019 6 Feb, 2019


Dr. Trung Thanh Ngo, Osaka University

Prof. Md Atiqur Rahman Ahad, Osaka University

Assoc. Prof. Daigo Muramatsu, Osaka University

Assoc. Prof. Yasushi Makihara, Osaka University

Prof. Yasushi Yagi, Osaka University

Assoc. Prof. Sozo Inoue, Kyushu Institute of Technology

Tahera Hossain, Kyushu Institute of Technology

Anindya Das Antar, Dept. of EEE, University of Dhaka

Masud Ahmed, Dept. of EEE, University of Dhaka

Yuichi Hattori, Kyushu Institute of Technology


Recently, wearable computing resource, such as smartphone, is developing so quickly. People are using smartphone for communication, entertainment, business, travelling, browsing information. Although it has a huge benefit if we can use such wearable computing resource to support people life, the healthcare application is very limited. We would like to break the limitation and boost up the research to support human health. One of the important step for a healthcare system is to understand age and gender of the user who is wearing the sensor through gait. Gait is chosen since it is the most dominant daily activity, which is considered to contain not only identity but also physical, medical conditions.


The competition registration can be done by Google Form or email. If you would like to register by email, please send to with subject line as OU-ISIR-GAG, with the following information: (1) team name; (2) names, affiliations of all members; and (3) name, affiliation, email, phone number and mailing address of contact person. A reference number will be assigned to each team by our confirmation email.


The training datasets are publicly available, while test dataset will be uploaded. All these datasets were captured by the same sensors (IMUZ).

Training datasets:

Participants can use both OU-ISIR inertial datasets:

(a) The OU-ISIR Gait Database, Inertial Sensor Dataset:
(b) The OU-ISIR Gait Database, Similar Action Inertial Dataset: Sensor setup

Test dataset:

A half of the dataset is similar to that of (a) and (b). The other half was captured in the wild on an almost flat ground; sensors were fixed in a backpack while their orientations will be unavailable. File format is the same as that of (a). The labels of this test dataset will not be released because we do not have release permission from most participants.

Download test dataset (expired)


We will evaluate the results separately for age and gender. The prediction results will be evaluated by mean absolute error for age and number of mistake for gender.

Prediction results submission

Participant can submit multiple solutions for age or gender. Submission of a technical paper (pdf, 2~4 pages) is required for each algorithm (age or gender) to explain the algorithm. Participant may decide not to disclose the method but need to briefly summarize the algorithm. A technical paper file should be named as reference number_X_Y, where X=AGE or GENDER and Y=1,2,.. the order of your algorithm. For examples, if you were given a reference number of GAG2019112701 and you want to submit two age solutions and one gender solution, you will submit 3 papers: GAG2019112701_AGE_1.pdf, GAG2019112701_AGE_2.pdf, and GAG2019112701_GENDER_1.pdf

Participant has to submit prediction result (age or gender) for all available data files of the test dataset in a prediction form. Number of prediction columns in the prediction form can be modified accordingly, depending on the number of solutions. The prediction form file should be named as reference number.csv

Prediction form and technical papers must be submitted by sending single or multiple emails to with the assigned reference number on the subject line.

Presentation and Winner Selection

Participants are encouraged to present their work at ICB 2019 conference orally onsite or online. Date and time will be announced.

Winner will be decided mainly by the prediction result, then partialy by the method details (novelty of algorithm, computation cost, … ), and presentation from presenters. A winner for age and a winner for gender will be selected.

Prediction Results Summary
We have 18 registered teams and 10 of them submitted the results. The results are summarized as below.

ID Rank for Age Rank for Gender Team
GAG2019121202 1st 1st Tim Van hamme (imec-DistriNet, KU Leuven), Giuseppe Garofalo (imec-DistriNet, KU Leuven), Davy Preuveneers (imec-DistriNet, KU Leuven), Enrique Argones Rúa (imec-COSIC, KU Leuven)
GAG2019120701 3rd 2nd Takuya Yaguchi (So-net Media Networks Corp.), Yipeng Shen (University of Southampton), FangFei Liu (Taiping Financial Technology Service Co., Ltd.)
GAG2019121501 2nd 3rd Sudeep Sarkar (Department of Computer Science, University of South Florida, USA), Ravichandran Subramanian (USF Alumnus)