S&M2128 Research Paper of Special Issue
Published: February 20, 2020
Strike Activity Detection and Recognition Using Inertial Measurement Unit towards Kendo Skill Improvement Support System [PDF]
Yohei Torigoe, Yugo Nakamura, Manato Fujimoto, Yutaka Arakawa, and Keiichi Yasumoto
(Received September 11, 2019; Accepted January 9, 2020)
Keywords: activity recognition, wearable computing, inertial measurement unit, sports support, kendo
In the field of sports, there are increasing opportunities to use inertial measurement units (IMUs) to enhance the training process and improve the performance of athletes. We focus on kendo, a traditional martial art using shinai (bamboo swords) in Japan, and propose methods for detecting and recognizing strike activities using IMUs towards realizing a kendo skill improvement support system. We used a sensor data set of strike activities obtained from 14 participants (seven kendo-experienced and seven inexperienced persons). We attached four IMUs to the participants’ right wrist, waist, and shinai (tsuba and saki-gawa). First, to detect the strike activity, we calculated the dynamic time warping (DTW) distance between the training data and the time series data, and detected the strike activity sections. The proposed method detected strike activities with a high accuracy of 95.0%. Next, to recognize the strike activity, we recognized five types (Center-Men, Right-Men, Left-Men, Dō, and Kote). In the person-dependent (PD) case, we achieved an accuracy of 89.5% using data of the right wrist. In the person-independent (PI) case, we achieved an accuracy of 54.9% using IMUs attached to the three positions. These results clarified the points to be improved in the proposed method to realize the support system.
Corresponding author: Yohei Torigoe
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Yohei Torigoe, Yugo Nakamura, Manato Fujimoto, Yutaka Arakawa, and Keiichi Yasumoto, Strike Activity Detection and Recognition Using Inertial Measurement Unit towards Kendo Skill Improvement Support System, Sens. Mater., Vol. 32, No. 2, 2020, p. 651-673.