Deep Learning-Based Violin Bowing Action Recognition.

We propose a violin bowing action recognition system that can accurately recognize distinct bowing actions in classical violin performance. This system can recognize bowing actions by analyzing signals from a depth camera and from inertial sensors that are worn by a violinist. The contribution of this study is threefold: (1) a dataset comprising violin bowing actions was constructed from data captured by a depth camera and multiple inertial sensors; (2) data augmentation was achieved for depth-frame data through rotation in three-dimensional world coordinates and for inertial sensing data through yaw, pitch, and roll angle transformations; and, (3) bowing action classifiers were trained using different modalities, to compensate for the strengths and weaknesses of each modality, based on deep learning methods with a decision-level fusion process.

Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features.

In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players' behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers.