Effect of Twisted-Yarn Architecture on Pressure and Proximity Sensing Characteristics of Textile Capacitive Sensors for Robotic Skin

Ishtia Zahir; Eslam Saleh; Maryam Rezayati; Güunter Grabher; Gaffar Hossain

cs.ROAug 14, 2026

Effect of Twisted-Yarn Architecture on Pressure and Proximity Sensing Characteristics of Textile Capacitive Sensors for Robotic Skin

Authors: Ishtia Zahir, Eslam Saleh, Maryam Rezayati, Güunter Grabher, Gaffar Hossain

Organizations: V-Trion GmbH, Millennium Park 15, 6890 Lustenau, Austria · Institute of Mechatronic Systems, ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland · Grabher Group GmbH, Augarten Strasse 27, 6890 Lustenau, Austria

Abstract

Textile-integrated capacitive sensors offer flexible and conformable tactile sensing for wearable electronics and human-robot interaction; however, the influence of yarn-level architecture on capacitive transduction characteristics remains insufficiently quantified. This work presents a textile capacitive sensing platform based on silver-coated yarns coated with polydimethylsiloxane and assembled into one-, two-, and four-layer twisted configurations. The influence of effective electrode overlap area and inter-fiber separation on the capacitive response is systematically investigated, enabling architecture-dependent tuning of pressure and proximity sensing characteristics. Pressure was calculated using the localized single-fiber contact area, corresponding to stresses of 0.4-3.9 MPa. Increasing the layer number improved mechanical strength and sensing performance: elongation at break increased from 37.5% to 62.5% and 85.0%, while the maximum load increased from 23.3 to 42.7 and 89.7 N. Sensitivity increased with layer number and frequency, reaching 0.1331 MPa $^{-1}$ for the four-layer sensor at 100 kHz. The four-layer configuration also exhibited low hysteresis, minimal thermal drift from 25 to 90 $^\circ$ C, and stable operation over 15,000 cycles. Proximity detection ranges of 60, 50, and 40 mm were obtained for the one-, two-, and four-layer sensors, respectively, revealing an architecture-dependent sensitivity-range trade-off. A 4 $\times$ 4 textile sensing array enabled spatial contact mapping, while robotic-arm integration demonstrated real-time touch and proximity detection with an end-to-end robotic system latency (from detection to robot reaction) of 403 ms. The results establish yarn architecture as a tunable design parameter governing the measurement characteristics of textile-integrated capacitive sensing systems.

Figures & tables

Fig. 1: Textile capacitive sensor characterization. (a) One-, two-, and four-layer twisted yarns. (b) Corresponding $A_{\mathrm{eff}}$ values. (c–e) Junction schematics and textile grids showing field distribution, larger junction area, and reduced $d_{\mathrm{eff}}$ .

Fig. 2: Pressure, frequency, and mechanical characterization of the textile capacitive sensor. (a–d) Stepwise pressure response. (e–h) Setup, response time, and 1–5 Hz frequency behavior. (i–l) Tensile properties and frequency-dependent sensitivity.

Fig. 3: Repeatability and durability of the textile capacitive sensor. (a) Cyclic loading schematic. (b–d) Repeatability over 200 s. (e) Durability test setup. (f–h) Long-term response over 15,000 cycles.

Fig. 4: Hysteresis and cyclic pressure response of the textile capacitive sensor. (a) Capacitive fiber sensing junction and LCR-meter measurement configuration. (b) Hysteresis across ply configurations. (c) Frequency response across ply configuration for dynamic loading and unloading.

Fig. 5: Environmental stability and proximity sensing. (a-c) Capacitance measurements at different temperatures within 25–90 ${}^{\circ}\mathrm{C}$ . (d-e) Fringe field perturbation schematic during proximity sensing. (f) Maximum proximity detection distance for different material types (paper, hand, metal, and plastic). (g) $\Delta C/C_{0}$ as a function of hand distance (0–6 cm) for all configurations.

Fig. 6: Overview and validation of the $4\times 4$ pressure-sensing matrix. (a) System architecture. (b) 2D/3D pressure maps for different touch positions. (c) Real-time finger-press pressure localization.

Fig. 7: Experimental validation of the textile sensing platform: (a) Autonomous evasion triggered by proximity detection; (b) Interactive touch localization for intuitive robot control; (c) Intelligent classification distinguishing between metallic gears and human finger proximity.

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