TY - GEN
T1 - A Diving Glove with Inertial Sensors for Underwater Gesture Recognition
AU - Tang, Qi
AU - Mai, Jingeng
AU - Wang, Tiantong
AU - Wang, Qining
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - Underwater gesture recognition has emerged as a popular research area for achieving efficient and secure underwater human-human interaction and human-robot collaboration. Previous research primarily relied on visual methods, which face challenges related to low visibility and motion blur in underwater environments. In this paper, we introduce a diving glove embedded with inertial sensors for underwater gesture recognition. The Nearest Centroid (NC), Random Forest (RF) and Support Vector Machine (SVM) classifiers are used in our diving glove. To demonstrate the underwater gesture recognition performance, we conducted a two-stage underwater experiment with ten underwater gestures. For the same user, The average recognition accuracies of the NC, RF and SVM classifiers are 97.5 % ± 5.4 %, 98.6 % ± 0.6 % and 99.2 % ± 0.3 %, respectively. For new users, the average recognition accuracies of the NC, RF and SVM classifiers are 86.4 % ± 2.18 %, 88.6 % ± 7.4 % and 96.5 % ± 0.31 %, respectively. The study suggests that the diving glove with inertial sensors is a feasible solution for underwater gesture recognition.
AB - Underwater gesture recognition has emerged as a popular research area for achieving efficient and secure underwater human-human interaction and human-robot collaboration. Previous research primarily relied on visual methods, which face challenges related to low visibility and motion blur in underwater environments. In this paper, we introduce a diving glove embedded with inertial sensors for underwater gesture recognition. The Nearest Centroid (NC), Random Forest (RF) and Support Vector Machine (SVM) classifiers are used in our diving glove. To demonstrate the underwater gesture recognition performance, we conducted a two-stage underwater experiment with ten underwater gestures. For the same user, The average recognition accuracies of the NC, RF and SVM classifiers are 97.5 % ± 5.4 %, 98.6 % ± 0.6 % and 99.2 % ± 0.3 %, respectively. For new users, the average recognition accuracies of the NC, RF and SVM classifiers are 86.4 % ± 2.18 %, 88.6 % ± 7.4 % and 96.5 % ± 0.31 %, respectively. The study suggests that the diving glove with inertial sensors is a feasible solution for underwater gesture recognition.
KW - diving glove
KW - inertial sensors
KW - machine learning
KW - underwater gesture recognition
UR - https://www.scopus.com/pages/publications/85176016518
U2 - 10.1007/978-981-99-6486-4_20
DO - 10.1007/978-981-99-6486-4_20
M3 - 会议稿件
AN - SCOPUS:85176016518
SN - 9789819964857
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 230
EP - 242
BT - Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
A2 - Yang, Huayong
A2 - Zou, Jun
A2 - Yang, Geng
A2 - Ouyang, Xiaoping
A2 - Liu, Honghai
A2 - Yin, Zhouping
A2 - Liu, Lianqing
A2 - Wang, Zhiyong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023
Y2 - 5 July 2023 through 7 July 2023
ER -