The growth of wearable electrophysiology is accelerating demand for gel‐free biopotential electrodes that are skin‐conformal, comfortable for extended use, and stable under typical human motion. Here we report σPOMaC, a self‐adhesive, conductive, skin‐compatible elastomer based on poly(octamethylene maleate (anhydride) citrate) (POMaC), a citrate‐derived polyester with mechanical characteristics that are easily tunable via changes to monomer ratios and curing. Although POMaC is readily processed into soft structures, achieving robust electronic conductivity that survives curing, drying, and handling remains challenging, hindering its use in bioelectronic interfaces. To address this, we co‐formulate a soft conductor, poly(3,4‐ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS), with a surfactant, 4‐dodecylbenzenesulfonic acid (DBSA), into the POMaC prepolymer to generate conductive σPOMaC composites. Because these additives affect processing parameters and material properties, such as curing time and viscoelasticity, we optimized composition and processing to jointly achieve high conductivity (50 S/cm; ∼ 0.02 Ω·cm), skin‐appropriate adhesion (0.013 ± 0.004 N/mm on PDMS), and elastomeric compliance suitable for biopotential recording. Using optimized σPOMaC, we fabricated a custom chest patch featuring conformal ECG electrodes and demonstrated clear, high‐fidelity on‐body ECG waveforms comparable in morphology and timing to simultaneous recordings made using commercial Ag/AgCl electrodes. Together, these results position σPOMaC as a material platform for gel‐free, self‐adhesive, skin‐interfaced bioelectronic electrodes, enabling simplified application and improved interface with the end‐user and reducing disposable hydrogel waste in longitudinal monitoring.

With the growing demand for thinner and more flexible electrochromic devices (ECDs), proper electrolyte selection is critical for the design and implementation of these systems. Compared to a traditional liquid electrolyte, polymer gel electrolytes have received growing application in ECDs due to their efficient ion transport, high stability, and, most importantly, zero risk of leakage during device integration. In this work, we capitalize on these features in the design, fabrication, and testing of a flexible and multifunctional polymer gel electrolyte using a polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) matrix enriched with ionic liquids and plasticizers that interface with adjacent electrochromic pixels. This polymer gel electrolyte remains stable at room temperature, enabling operation of ECD pixel elements over 2750 cycles and 2.5 days of continuous operation. Applications such as passive color filtering using the electrolyte are also explored, highlighting its potential to improve operation and expand colour range without modification of the electrochromic film.

Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and non-physiological noise. These low-quality segments can prevent reliable extraction of clinically relevant biomarkers, including heart rate, heart rate variability, pulse rate, oxygen saturation, and electrodermal activity, thereby limiting the robustness of downstream sleep stage classification and sleep apnea prediction. In this work, we present a multistage hybrid anomaly detection framework for signal quality assessment in a custom-designed multimodal wearable sleep monitoring platform. The proposed framework is shared across modalities: each sensor stream is segmented into windows, screened using signal-processing algorithms for obvious sensor failures, and then analyzed using an autoencoder trained on normal windows to detect subtler morphology-level deviations from normal physiological patterns. On chest ECG recordings collected over 10 overnight sessions, the hybrid fusion detector achieves an accuracy of 0.9204, AUROC of 0.9484, F1 score of 0.9108, precision of 0.9572, and recall of 0.8687, outperforming rule-based detection and autoencoder-based detection alone. Additional experiments on PPG, EDA, and ExG recordings show consistently high F1 scores of 0.9632, 0.9668, and 0.9779, respectively. These results demonstrate that hybrid anomaly detection can serve as an effective quality-control layer for multimodal wearable sleep monitoring and provide a foundation for future work on robust sleep stage and apnea prediction under real-world sensing conditions.

Wearable sleep monitoring systems provide a scalable and low-burden alternative to laboratory-based polysomnography, but overnight physiological recordings collected from wearable sensors are frequently corrupted by poor skin contact, sensor displacement, flatline behavior, saturation, abrupt autoscaling, outliers, and non-physiological noise. These low-quality segments can prevent reliable extraction of clinically relevant biomarkers, including heart rate, heart rate variability, pulse rate, oxygen saturation, and electrodermal activity, thereby limiting the robustness of downstream sleep stage classification and sleep apnea prediction. In this work, we present a multistage hybrid anomaly detection framework for signal quality assessment in a custom-designed multimodal wearable sleep monitoring platform. The proposed framework is shared across modalities: each sensor stream is segmented into windows, screened using signal processing algorithms for obvious sensor failures, and then analyzed using an autoencoder trained on normal windows to detect subtler morphology-level deviations from normal physiological patterns. On chest ECG recordings collected over 10 overnight sessions, the hybrid fusion detector achieves an accuracy of 0.9067, AUROC of 0.9068, F1 score of 0.8739, precision of 0.9470, and recall of 0.8202, outperforming rule-based detection and autoencoder-based detection alone. Additional experiments on PPG, EDA and ExG recordings show consistently high F1 scores of 0.9431, 0.9567 and 0.8728, respectively. These results demonstrate that hybrid anomaly detection can serve as an effective quality-control layer for multimodal wearable sleep monitoring and provide a foundation for future work on robust sleep stage and apnea prediction under real-world sensing conditions.