ArticleCureus2025
Design and Evaluation of a Conductive-Knit Sensor System for Measuring Forearm Pronation and Supination.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable sensors enable continuous monitoring of human motion in daily life, but assessing forearm pronation and supination usually requires specialized laboratory equipment. This study aimed to design and evaluate a knitted textile sensor system based on carbon nanotube (CNT) conductive yarns for unobtrusive measurement of rotational forearm movements. To overcome the hysteresis inherent in knitted strain sensors, we implemented a mechanism using two sensors positioned at 45° on the forearm, arranged so that only one sensor undergoes elongation during each motion. This configuration leverages the sensor's stable elongation response while minimizing the influence of unloading hysteresis, thereby enabling reliable estimation of rotational angles. Electrical characterization confirmed consistent strain resistance during elongation, and regression analysis showed that nonlinear (cubic) models provided superior accuracy, compared to linear models. Validation experiments demonstrated that the system achieved errors of about 2.5° root-mean-square in offline analysis and approximately 5° in real-time estimation, compared with a gyroscope. Importantly, the system did not require direct skin attachment and offered the advantages of breathability, flexibility, and comfort inherent to textile-based designs. These results highlight the feasibility of CNT-knitted sensors as a lightweight, cost-effective, and user-friendly platform for capturing complex upper limb movements, with promising applications in rehabilitation monitoring, sports performance assessment, and everyday healthcare technologies.
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