ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Mechanical Compatibility in Stitch Configuration and Sensor Adhesion for High-Fidelity Pulse Wave Monitoring.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Gradient Electrode-Electrolyte Interface Enables Ultrastable Piezoionic Sensor for Artificial Intelligence.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Mechanical Compatibility in Stitch Configuration and Sensor Adhesion for High-Fidelity Pulse Wave Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Wearable electronics can achieve high-fidelity monitoring of pulse waveforms on the body surface enabling early diagnosis of cardiovascular diseases (CVDs). Textile-based wearable devices offer advantages in terms of high permeability and comfort. However, knitted strain sensors struggle to capture small-range deformation signals due to stress dissipation during friction and slip of yarns within the textiles. They are optimized for mechanical adaptability and adhesive capability. In this work, the stitch configurations of knitted structure are adjusted to optimize the energy dissipation ratio during deformation and waveform fitting performance. These electric-mechanical results enabled the selection of the most suitable knitted structure for the clinical diagnosis. On the other hand, the sensor's adhesion is optimized with respect to electrical-force-strain coupling and energy transfer efficiency at the interface between skin and sensor. The balance between the storage modulus and loss modulus are adjusted via the crosslinking degree of the polyacrylamide (PAAm) hydrogel network. As a result, the optimized knitted sensor enables stable collection of pulse waveforms from the radial and dorsalis pedis arteries. In human patient evaluations, the knitting-based strain sensor can distinguish patients with different potential CVD risks through extracted characteristic indicators.
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