James Reynolds

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.

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.

This study investigates how conductive yarn type, weave structure, and conductive yarn insertion direction influence the electrical resistance of woven e-textile interconnects, both prior to laundering and across repeated wash cycles. Using four types of commercially available conductive yarn, woven samples were fabricated in plain, twill, and satin structures with the conductive threads inserted along either the warp or weft direction. Electrical resistance was measured before laundering and sequentially after each of five standardized wash cycles. A full factorial three-way analysis of variance (ANOVA) was used to evaluate pre-wash effects, and a linear mixed-effects model was applied to characterize resistance changes across laundering cycles. Before washing, all structural and material parameters, as well as their interactions, significantly affected resistance (p < 0.05). After laundering, yarn type emerged as the dominant factor (p < 0.05), while structural parameters and insertion direction no longer exhibited significant effects. Multifilament copper-based yarns maintained low resistance across wash cycles, whereas the silver-plated yarn consistently exhibited the highest values. These findings clarify the relative contributions of material and structural design parameters and provide guidance for fabricating woven e-textile interconnects with improved electrical reliability during repeated laundering.

Most commercially available electronic textile (e-textile) products and research prototypes rely on conventional printed circuit boards (PCBs) for integrating electronic functionality. However, these rigid PCBs offer limited flexibility, are prone to mechanical failure when subjected to stretching or repeated deformation and add significant bulk to the textile form. Woven textile circuit boards (WTCBs) offer a promising alternative by incorporating electronic components directly into the fabric structure. While prior studies have investigated circuit integration in textiles, a comprehensive review of WTCBs is still lacking. This review aims to address that gap. It begins with an overview of woven e-textile fundamentals, including loom types and mechanisms, weave structures, and the role of weaving parameters in determining textile performance. It then examines the methods reported for developing WTCBs and reviews the interconnect materials and joining techniques used in their fabrication. The review concludes with key insights and highlights areas where further investigation is needed. Major findings include the identification of four main approaches for creating WTCBs: woven conductive tracks with external electronics, integration of flexible circuit boards into woven structures, self-contained woven circuit boards, and post-weaving surface integration. Recent studies demonstrate growing interest in both conventional and novel joining techniques; however, further investigation is still needed to enhance their durability and reliability under real-world environmental and mechanical conditions. These findings lay the foundation for designing functional, scalable WTCBs that deliver electronic capability while maintaining the comfort, flexibility, and durability expected from traditional textiles.

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC–carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.

Unobtrusive acquisition of physiological and behavioral data from freely moving animals is important to many applications including animal research, veterinary science, animal husbandry, pet monitoring, etc. This paper reports a miniaturized, injectable, and multimodal implant for real-time measurements of heart rate, breathing rate, movement, and subcutaneous temperature with future extensions to blood pressure and oxygen saturation. To estimate these vital signs, the presented device incorporates sensors of various modalities: photoplethysmography, electrocardiography, accelerometry, magnetometry, and thermometry. A rechargeable battery drives the system containing a system-on-chip with Bluetooth low energy capability and multiple sensor front-end circuits. The implant electronics are isolated from the animal’s extracellular fluid by a dual-layer encapsulation of biomedical epoxy and poly(methyl methacrylate) that fits into a 6-gauge surgical needle to allow for subcutaneous injection. Electrically conductive epoxy is used to create electrodes on the surface of the encapsulation for biopotential measurements. With a 3-meter wireless range from a custom receiver, this implant can continuously transmit data from all the sensors for 20 hours, which can support 2-3 months of duty-cycled and intermittent recording between battery recharges. The system was tested in vivo where the acquired heart rate and breathing rate estimations showed an error of less than 2 beats per minute compared to the gold standard. Longer-term evaluation of tissue reaction showed an acceptable level of immune responses with minimal effect on the sensing performance. This novel system has the potential to provide new insights with greater depth in veterinary research and practice, and animal welfare management.

Smart farming is the targeted use of phenotyping for the rapid, continuous, and accurate assessment of plant health in the field. Bioimpedance monitoring can play a role in smart farming as a phenotyping method, which is now accessible thanks to recent efforts to commoditize and miniaturize electronics. Here, we demonstrate that bioimpedance measurements reflect the physiological changes in live plant tissue with induced alterations in their environmental conditions. When plants were exposed to $-$ 1.0 MPa polyethylene glycol, to simulate drought conditions, the extracellular resistance was observed to increase prior to the intercellular resistance, where the low frequency bioimpedance measurements increased by 25% within one hour. Similar patterns were observed when drought stress was applied to the plants by water withholding, with a bioimpedance increase within a matter of a few hours. The bioimpedance measurements were also compared with leaf relative water content, imaging, and field transpirable soil water, which reinforced these findings. These preliminary results suggest that bioimpedance can function as a phenotyping tool for continuous and real time monitoring of plant stress to allow the development of strategies to prevent damage from environmental stresses such as drought.