Identifying patterns of proprioception and target matching acuity in healthy humans

TL;DR

The goal is to characterize normative kinesthetic perception and real-world performance as a multimodal sensory "fingerprint" that can serve as a reference point for identifying deficit in persons affected by stroke, and then as a jumping point for later neuroscientific interrogation.

Abstract

Traditional approaches to measurement in upper-limb therapy have gaps that electronic sensing and recording can help fill. We highlight shortcomings in current kinematic recording devices, and we introduce a wrist sensing device that performs multimodal sensing during single-axis rotation. Our goal is to characterize normative kinesthetic perception and real-world performance as a multimodal sensory "fingerprint" that can serve as a reference point for identifying deficit in persons affected by stroke, and then as a jumping point for later neuroscientific interrogation. We present an experiment involving psychophysical measurements of passive stimuli discrimination, matching adjustment acuity, and ADL performance in 11 neurologically-intact persons. We found that passive velocity sense and active position sense of healthy controls, measured by velocity discrimination and position matching respectively, correlated in rank with each other, but other score comparisons of acuity or task performance had no statistically significant correlations. We also found that participants differed in acuity between passive and active velocity sense, which supports current understanding about muscle spindle activation being modulated by conscious motor command. The potential for our null correlation results to reveal dissociable aspects of deficit is discussed, as well as implications for future neuroscientific study with more kinematic measures and larger datasets.

Figures (12)

• Figure 1: Birds-eye view of the WRIST testbed, with the motor-driven wrist interface at top and the signal conditioning and amplification electronics in the white box at the bottom.
• Figure 2: Side view of the robotic actuator of the WRIST testbed, with annotated callouts of key components.
• Figure 3: The WRIST Testbed utilizing different interface attachments to change and fix the direction of rotation for (a) flexion/extension and (b) abduction/adduction.
• Figure 4: A graphical summary of all notable components of the human-device interaction system during a study using the WRIST testbed. Green arrows generally indicate electronic signal flows. Dashed arrows highlight visual connection that can be toggled.
• Figure 5: A scoped output of all signals processed by the Simulink software, which includes position, velocity, torque, grip force, and current.