TY - GEN
T1 - Strikes-Thrusts Activity Recognition Using Wrist Sensor Towards Pervasive Kendo Support System
AU - Takata, Masashi
AU - Nakamura, Yugo
AU - Torigoe, Yohei
AU - Fujimoto, Manato
AU - Arakawa, Yutaka
AU - Yasumoto, Keiichi
N1 - Funding Information:
ACKNOWLEDGMENTS This work is supported by JST PRESTO and ACT-I.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - In this paper, we focus on Kendo, which is a traditional sport in Japan, and propose a strikes-thrusts activity recognition method using a wrist sensor towards a pervasive Kendo support system. We collected the inertial sensor data set from 6 subjects. We attached 3 inertial sensor units (IMUs) on the subjects body, and 2 IMUs on the Shinai (bamboo sword used for Kendo). On the body, IMUs were placed on the Right Wrist, Waist and Right Ankle. On the Shinai, they were placed on the Tsuba and Saki-Gawa. We first classified strikes-thrusts activities consisting of 4 general types, Men, Tsuki, Do, and Kote, followed by further classification into 8 detailed types. We achieved 90.0% of F-measure in the case of 4-type classification and 82.6% of F-measure in the case of 8-type classification when learning and testing the same subjects data for only Right Wrist. Further, when adding data of sensors attached to the Waist and Right Ankle, we achieved 97.5% of F-measure for 4-type classification and 91.4% of F-measure for 8-type classification. As a result of leave-one-person-out cross-validation from 6 subjects to confirm generalized performance, in the case of 4-type classification, we achieved 77.5% of F-measure by using only 2 IMUs (Right Wrist and Shinai Tsuba).
AB - In this paper, we focus on Kendo, which is a traditional sport in Japan, and propose a strikes-thrusts activity recognition method using a wrist sensor towards a pervasive Kendo support system. We collected the inertial sensor data set from 6 subjects. We attached 3 inertial sensor units (IMUs) on the subjects body, and 2 IMUs on the Shinai (bamboo sword used for Kendo). On the body, IMUs were placed on the Right Wrist, Waist and Right Ankle. On the Shinai, they were placed on the Tsuba and Saki-Gawa. We first classified strikes-thrusts activities consisting of 4 general types, Men, Tsuki, Do, and Kote, followed by further classification into 8 detailed types. We achieved 90.0% of F-measure in the case of 4-type classification and 82.6% of F-measure in the case of 8-type classification when learning and testing the same subjects data for only Right Wrist. Further, when adding data of sensors attached to the Waist and Right Ankle, we achieved 97.5% of F-measure for 4-type classification and 91.4% of F-measure for 8-type classification. As a result of leave-one-person-out cross-validation from 6 subjects to confirm generalized performance, in the case of 4-type classification, we achieved 77.5% of F-measure by using only 2 IMUs (Right Wrist and Shinai Tsuba).
UR - http://www.scopus.com/inward/record.url?scp=85067935994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067935994&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2019.8730861
DO - 10.1109/PERCOMW.2019.8730861
M3 - Conference contribution
AN - SCOPUS:85067935994
T3 - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
SP - 243
EP - 248
BT - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
Y2 - 11 March 2019 through 15 March 2019
ER -