2021 Volume 29 Pages 70-80
In wheelchair basketball (WB), players are constantly trying to improve their wheelchair maneuvering techniques since these are the most basic and important actions in all situations. However, assessing maneuvering quality is difficult due to the lack of quantitative metrics. In this paper, we propose two classification methods for maneuvering actions and turns by focusing on the specific wheelchair movement. For this purpose, inertial sensors are fixed to the left and right wheels of the wheelchair. In maneuver classification, the occurrence of maneuvers is detected using the angular velocity. Major maneuver activities in WB are classified into 2 types: PUSH and PULL. First, our method segments candidates of maneuver periods by the local maximum/minimum of the angular velocity since the rotation of the wheel generated by maneuvering that leads to sharp changes in the angular velocity. We then classify maneuvering actions based on thresholds. As for the turn classification, we first detect turns by calculating the amount of wheelchair rotation from the angular velocities of both wheels. We then classify the detected turns into PIVOT and TURN by using thresholds based on the typical movement of both wheels during each turn. To evaluate the performance of the proposed maneuver classification method, we collected real data from 6 players. From the result, we confirmed our method achieves an average recall and precision of 91.9% and 84.6% for maneuver classification, respectively. The results also show that our turn classification achieves an average recall and precision of 99.7% and 99.7%, respectively. Furthermore, we confirmed the effectiveness of the classification results for the assessment of maneuver quality.