Developing conductive hydrogel‐incorporated strain sensors with high gauge factor (GF) and toughness for wearable applications is challenging. Herein, a facile strategy to fabricate strong, tough polyacrylamide‐co‐acrylic acid [P(AAm‐co‐AAc)] hydrogels via the synergy of fiber and metal‐ligand bonds is proposed. Through the secondary equilibrium approach, the pristine P(AAm‐co‐AAc) gel network is reconstructed with fiber and carboxyl–Zr4+ (Zirconium ion) coordination bonds intertwined over the entire gel matrix, generating a synergistic reinforcement in mechanical properties. The resultant hydrogels display a maximum tensile strength of 8.50 MPa and a fracture energy of 2.75 kJ m−2, which is 1–2 orders of magnitude greater than the original P(AAm‐co‐AAc) gels. It is also experimentally approved that by selecting different natural fibers, multivalent metal ions, and synthetic macromolecules containing carboxyl groups, the proposed approach is effective and can be generalized to fabricate strong, tough gels. Additionally, the electrical properties of obtained gel are evaluated by fabricating it into a stretchable strain sensor for object identification or human motion detection. The results reveal a high GF of 5.07 under a strain smaller 55%. These hydrogels are expected to enable numerous applications in soft robotics or wearable healthcare.
The utilization of Surface Electromyography (sEMG) is widespread for monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces like the larynx, because regular sEMG electrodes have a stiff structure. In this paper, we develop a stretchable, high-density sEMG electrode array via a layer-by-layer printing and lamination process. The electrode offers a series of excellent human-machine interface features in terms of conformal adhesion to the skin, high electron-to-ionic conductivity (and thus lower contact impedance), prolonged environmental adaptability to resist water evaporation, and epidermal biocompatibility. This makes the electrode more appropriate than commercial electrodes for long-term wearable, high-fidelity recording of sEMG at complicated skin interfaces. Systematic in vivo studies investigate its practical functionality in swallowing activities classification, which is accomplished with high accuracy by decoding sEMG signals from the chin with the integration of an ear-mounted wearable system through machine learning algorithms. The results demonstrate clinical feasibility of the system in swallowing recognition-driven, non-invasive, user-comfortable dysphagia rehabilitation.
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