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Updated: Aug 20, 2026

A Standardized Method for Measurement of Elbow Kinesthesia
Published on: October 10, 2020
Textile-Integrated Magnetoinductive Waveguides for Low-Complexity, Single-Channel Quantitative Estimation of
Objective:
Wearable monitoring of isometric elbow torque is of great interest for rehabilitation monitoring and muscular disease management, yet remains limited by obtrusiveness, drift, and complexity. We present a wearable sensor that measures transient circumferential deformation of the mid-upper arm as a low-complexity bio-signal for joint torque monitoring.
Methods:
The proposed sensor operates on the basis of magnetoinductive waveguide principles. Participants (n=20) completed three sets of three contractions with sensor replacement between sets. A two-stage analysis extracted contraction-level calibration slopes and characterized variability using a three-level nested mixed effects model. Systematic bias was assessed and leave-one-out cross validation quantified prediction error at the subject, set, and contraction levels.
Results:
The sensor data had a strong, linear relationship with elbow torque (mean $R^{2}=0.925\pm 0.063$) across the test population. Within-contraction modeling error was $2.5\;Nm$ (5.9%); leave-one-out calibration grouped by set, subject, and population increased error to $5.84\;Nm$ (14.0%), $7.70\;Nm$ (18.4%), and $9.76\;Nm$ (23.4%), respectively, with an opposite trend in calibration burden. No systematic bias was detected across set and contraction order, nor with respect to anthropometric data, supporting the sensor's generalizability.
Conclusion:
The herein described magnetoinductive waveguide sensor provides a strong, linear predictor of isometric elbow torque with calibration variability emerging primarily due to inter-subject differences that warrant further study. Extension to complex contractions remains critical for future work.
Significance:
The single-channel, low-complexity, textile-integrated modality enabled by transient circumferential deformation sensing creates a promising path towards monitoring of at-home rehabilitation and muscular disease management.

