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A Point of Release (PoR) prediction model is trained using motion features extracted from captured full body motion data and evaluated in a real-time VR application.
Source publication
In this paper, we present a novel real-time physical interaction system that allows users to throw a virtual ball without using an intermediary device such as a controller. A dataset of throwing motions was captured from six actors, from which ground truth Point of Release (PoR) frames were calculated. We trained a PoR prediction model using motion...
Citations
... Moreover, assistive devices are not versatile, and depend on the level of disorder [19,20]. On the other hand, recent studies on throwing motion estimation based on contactless sensors such as motion capture systems have been introduced [21][22][23]. This enables highly accurate estimation for the throwing motions of the entire body; however, not only does it require a large amount of data, it limits the estimation environment. ...
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities.
