My Tai-Chi coaches

Han, Ping-Hsuan; Chen, Yang-Sheng; Zhong, Yilun; Wang, Han-Lei; Hung, Yi‐Ping

doi:10.1145/3041164.3041194

Cited by 45 publications

(2 citation statements)

References 15 publications

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“…This perspective situates the trainees as an external observer of the feedforward, analogous to watching someone else perform the motion. In XR, this is typically a virtual coach using pre-recorded motion sequences such as to learn Tai-Chi [23], but can also be the virtual avatar of a remote trainer providing live motion guidance [26]. While distancing the trainee from the motion itself, the third-person perspective provides an exocentric view of the entire motion, thus promoting a more complete understanding of all facets of the motion-especially for motions involving multiple limbs [15].…”

Section: Viewing Perspectivementioning

confidence: 99%

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Yu,

Lee,

Sedlmair

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Figure 1: Schematic representations of motion feedforward. The leftmost column shows the complete motion sequence and its critical postures at four critical moments (Start, Point1, Point2, and End). The remaining columns show how the feedforward of this motion can be represented using different configurations of level of indirection (explicit, implicit and abstract) and interactive update strategy (discrete, continuous and autonomous). The visualizations in blue represent the feedforward; while the the black glyphs show the trainee's four moments during training from top to bottom (Start, reach Point 1, between Point 1 and Point 2, and between Point 2 and End); the green parts visualize the completed parts of a motion when using Implicit guidance (column 4 and 5). Discrete guidance (column 2, 4 and 6) presents cues for the next critical posture, which updates only when that posture is satisfied. Continuous guidance (column 3, 5 and 7) presents cues for the next frame in the motion sequence at all times. Autonomous guidance is not shown as it operates independently of the trainee's actions.

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Section: Viewing Perspectivementioning

confidence: 99%

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Yu,

Lee,

Sedlmair

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Digital fitness is garnering attention because it allows for exercise guidance remotely through automated systems, even when face-to-face interactions between instructors and learners are challenging. In particular, there has been an active pursuit of research [1][2][3][4][5][6][7] in which artificial intelligence analyzes exercise videos and supports self-training for exercise without communication with a sports instructor.…”

Section: Introductionmentioning

confidence: 99%

GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

Ju,

Kim,

Lee

2023

Applied Sciences

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Digital fitness has become a widely used tool for remote exercise guidance, leveraging artificial intelligence to analyze exercise videos and support self-training. This paper introduces a method for self-training in golf, a sport where automated posture analysis can significantly reduce the costs associated with professional coaching. Our system utilizes a pose refinement methodology and an explainable golf swing embedding for analyzing the swing motions of learners and professional golfers. By leveraging sequential coordinate information, we detect biased pose joints and refine the 2D and 3D human pose estimation results. Furthermore, we propose a swing embedding method that considers geometric information extracted from the swing pose. This approach enables not only the comparison of the similarity between two golf swing poses but also the visualization of different points, providing learners with specific and intuitive feedback on areas that require correction. Our experimental results demonstrate the effectiveness of our swing guide system in identifying specific body points that need adjustment to align more closely with a professional golfer’s swing. This research contributes to the digital fitness domain by enhancing the accuracy of posture analysis and providing a specialized and interpretable golf swing analysis system. Our proposed system offers a low-cost and time-efficient approach for users who wish to improve their golf swing, paving the way for broader applications of digital fitness technologies in self-training contexts.

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VRAS: A Virtual Rehearsal Assistant System for Live Performance

Ding

et al. 2018

Advances in Smart Vehicular Technology, Transportation, Communication and Applications

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My Tai-Chi coaches

Cited by 45 publications

References 15 publications

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

GolfMate: Enhanced Golf Swing Analysis Tool through Pose Refinement Network and Explainable Golf Swing Embedding for Self-Training

VRAS: A Virtual Rehearsal Assistant System for Live Performance

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