Running is one of the most popular sports. However, not many runners have received formal training on running. Associate Professor Shinichi Yamagiwa of the University of Tsukuba and his colleagues have developed a system for improving running skills based on big data analysis.
Dr. Yamagiwa, Associate Professor Yoshinobu Kawahara of Osaka University and Mizuno have jointly developed a technology that instructs the ideal running motions based on “big data of motions” collected by monitoring motions during running via sensors and videos.
The research team analyzed the running motion data of about 2,000 runners by using an artificial intelligence technique and expressed the results in numerical skill values. They discovered that the movements of the elbows, knees and ankles differed between high-rank marathon runners and beginners. Based on the findings, a technology called “skill grouping” was developed to assisting runners in improving their skills by displaying the effects of the movements in easy-to-understand scores.
Skill grouping can also be used for time-sequential healthcare and motor capacity control, such as during conditioning and rehabilitation. As it converts movements into objective values, it will enable information devices that have been difficult to generalize to be developed and is, thus, expected to lead to development of healthcare tools in the era of the Internet of Things, such as mobile-phone applications.
Another possible application of skill grouping is to assist transmission of traditional performance arts and design skills. Skill grouping is expected to realize a new system of artificial intelligence supporting “transmission of traditional skills” for globally urgent issues.
Citation: Shinichi Yamagiwa, Yoshinobu Kawahara, Noriyuki Tabuchi, Yoshinobu Watanabe, Takeshi Naruo, Skill Grouping Method: Mining and Clustering Skill Differences from Body Movement BigData, Proceeding of International conference on BigData 2015, IEEE (October 29-November 1, 2015 •Santa Clara, CA, USA)
