Accurate sensing of finger joint angles is a key objective in wearable motion sensing systems for rehabilitation applications and requires high sensing reliability and accuracy. However, conventional textile-based strain sensors often exhibit low reliability, characterized by baseline drift and the double-peak phenomenon during repetitive deformation. These limitations are mainly attributed to the length-variation-based sensing mechanism, in which repeated deformation induces changes in the sensor’s length and shape, leading to signal instability. To address these limitations, this study adopts a contact area-variation-based sensing mechanism that minimizes noise arising from changes in the sensor’s length and shape during repetitive deformation. A glove-integrated embroidered strain sensor was developed, and three key structural design elements affecting sensing reliability were systematically investigated: slit presence, the number of effective conductive contact points, and embroidery stitch density. Nonparametric statistical analyses were applied, including Spearman’s rank correlation and the Mann–Whitney U test. The slit-structured sensor (S1) achieved 100% peak detection across all fingers, whereas the non-slit sensor (S0) showed inconsistent detection. Reproducibility improved from r = 0.818 to r = 0.972, signal magnitude increased up to fivefold (~ 15–80 mV), baseline drift was reduced to below 1%, and double-peak artifacts were suppressed. The system maintained integrity after 20,000 Martindale abrasion cycles. Among the investigated design elements, the slit structure was identified as the most dominant factor influencing sensing reliability. These results suggest that incorporating a slit structure provides a reliable sensing foundation and supports the feasibility of finger joint angle estimation in textile-based wearable systems. This framework will be extended to joint angle sensing in future work.
Choi et al. (2026) studied this question.