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This paper presents a method of integrating two contrasting sensor systems for studying human interaction with a mechanical system, using piano performance as the case study. Piano technique requires both precise small-scale motion of fingers on the key surfaces and planned large-scale movement of the hands and arms. Where studies of performance often focus on one of these scales in isolation, this paper investigates the relationship between them. Two sensor systems were installed on an acoustic grand piano: a monocular high-speed camera tracking the position of painted markers on the hands, and capacitive touch sensors attach to the key surfaces which measure the location of finger-key contacts. This paper highlights a method of fusing the data from these systems, including temporal and spatial alignment, segmentation into notes and automatic fingering annotation. Three case studies demonstrate the utility of the multi-sensor data: analysis of finger flexion or extension based on touch and camera marker location, timing analysis of finger-key contact preceding and following key presses, and characterization of individual finger movements in the transitions between successive key presses. Piano performance is the focus of this paper, but the sensor method could equally apply to other fine motor control scenarios, with applications to human-computer interaction.

Integrating optical finger motion tracking with surface touch events