Exploring the proprioceptive potential of joint receptors using a biomimetic robotic joint

TL;DR

This study demonstrates that biomimetic Type I joint receptors can provide accurate proprioceptive sensing, suggesting a greater role in joint position awareness than traditionally thought.

Contribution

The research introduces a robotic model mimicking Type I joint receptors, revealing their significant proprioceptive potential and challenging existing views on sensory contributions.

Findings

Type I-like joint receptors achieved less than 2° error in sensing joint movements.

Joint receptors may have a larger role in proprioception than previously believed.

Results could influence understanding of proprioceptive deficits in neuropathy.

Abstract

In neuroscience, joint receptors have traditionally been viewed as limit detectors, providing positional information only at extreme joint angles, while muscle spindles are considered the primary sensors of joint angle position. However, joint receptors are widely distributed throughout the joint capsule, and their full role in proprioception remains unclear. In this study, we specifically focused on mimicking Type I joint receptors, which respond to slow and sustained movements, and quantified their proprioceptive potential using a biomimetic joint developed with robotics technology. Results showed that Type I-like joint receptors alone enabled proprioceptive sensing with an average error of less than 2 degrees in both bending and twisting motions. These findings suggest that joint receptors may play a greater role in proprioception than previously recognized and that the relative contributions of muscle spindles and joint receptors are differentially weighted within neural networks during development and evolution. Furthermore, this work may prompt new discussions on the differential proprioceptive deficits observed between the elbows and knees in patients with hereditary sensory and autonomic neuropathy type III. Together, these findings highlight the potential of biomimetics-based robotic approaches for advancing interdisciplinary research bridging neuroscience, medicine, and robotics.