Browsing by Author "Liu, Daniel H."

Musically-Evoked Kinesthetic Imagery (MEKI) encompasses the internal sensation of dynamic self-movement in response to musical stimuli. While analyzing these sensations offers insights into human cognition and motor response to music, traditional methods for tracking movement are biased towards overt movement, being fundamentally unequipped to measure imagined or physically impossible movements such as floating. Existing self-report methods also lack the dimensional complexity to categorize a broad range of motion features. To address this methodological gap, we are piloting the MEKI avatar: a 3D procedurally-animated humanoid avatar that is web-deployable and designed as a portable self-reporting mechanism that can be used in future MEKI studies. The tool utilizes a system of sliders that isolate specific kinematic parameters including amplitude of movement, speed, and smoothness of motion, while also prioritizing an accessible UI to prevent participant interface fatigue. By translating subjectively experienced MEKI into standardized, quantitative data objects, our avatar offers researchers a high-dimension, flexible new method to visualize and analyze imagined movement.