MuscleGolfAR: Embodied versus Detached Visualization for Motions and Inferred Muscle Activations in Augmented Reality

Precise kinematic and dynamic representations are equally critical for fine-grained motor skills such as golf, yet the latter remains underexplored. This work introduces MuscleGolfAR, an augmented reality (AR) training system integrating swing motions and muscle activities. To support cost-effective inference of muscle activations, we construct a multimodal dataset encompassing posture, electromyography (EMG), and plantar pressure. The system displays inferred EMG through embodied (first-person perspective) and detached (third-person perspective) visualizations. User studies are subsequently conducted to evaluate the impact of these strategies on training effectiveness. Results reveal that embodied visualizations enhance ownership over augmented feedback, while detached visualizations facilitate a holistic comprehension of the whole body. Furthermore, individuals' preferences for these strategies correlate with their practice habits and skill proficiencies, offering new insights for the design of AR-based motor skill training systems.