DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices.

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments.

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.

Flexible and Stretchable Organic Electrochemical Transistors for Physiological Sensing Devices.

Flexible and stretchable bioelectronics provides a biocompatible interface between electronics and biological systems and has received tremendous attention for in situ monitoring of various biological systems. Considerable progress in organic electronics has made organic semiconductors, as well as other organic electronic materials, ideal candidates for developing wearable, implantable, and biocompatible electronic circuits due to their potential mechanical compliance and biocompatibility. Organic electrochemical transistors (OECTs), as an emerging class of organic electronic building blocks, exhibit significant advantages in biological sensing due to the ionic nature at the basis of the switching behavior, low driving voltage (<1 V), and high transconductance (in millisiemens range). During the past few years, significant progress in constructing flexible/stretchable OECTs (FSOECTs) for both biochemical and bioelectrical sensors has been reported. In this regard, to summarize major research accomplishments in this emerging field, this review first discusses structure and critical features of FSOECTs, including working principles, materials, and architectural engineering. Next, a wide spectrum of relevant physiological sensing applications, where FSOECTs are the key components, are summarized. Last, major challenges and opportunities for further advancing FSOECT physiological sensors are discussed.

Dual functionality luminescence thermometry with Gd2O2S:Eu3+,Nd3+ and its multiple applications in biosensing and in situ temperature measurements.

Luminescence thermometry using sharp line emission of lanthanide ions has become an active area of research as it offers the advantages of remote temperature sensing with high sensitivity and superior spatial resolution. The most widely applied method relies on the temperature dependence of the luminescence intensity ratio of emission lines from two thermally coupled levels. However, the usable temperature range for this type of Boltzmann thermometer is limited. In addition, the weak and narrow line absorption of the parity forbidden 4f-4f transitions of lanthanides forms a serious drawback. To solve both problems, we here report a new dual functionality luminescence thermometer: Gd2O2S co-doped with Eu3+ and Nd3+. This material combines Boltzmann and energy transfer thermometry to extend the temperature range and uses the strong and broad charge transfer absorption band of Eu3+ for sensitization. In the T-range of 300-500 K efficient energy transfer from Eu3+ to Nd3+ allows for charge transfer-sensitized luminescence thermometry using near infrared emission from the thermally coupled 4F3/2 and 4F5/2 levels of Nd3+. Above 500 K a high temperature sensitivity is obtained using the strong temperature dependence of the luminescence intensity ratio of red Eu3+ to near infrared Nd3+ emission. The dual-functionality provides a single thermometer combining strong absorption and high relative sensitivity (0.6 - 1.4%) over a wide temperature range (300 to 650 K). Finally, it is proposed that this dual-function luminescent thermometer has promising potential for multifunctional applications in biosensors and in situ temperature measurements of chemical reaction process.

Multifunctional hybrid skin patch for wearable smart healthcare applications.

Recent advances in wearable patches have included various sensors to monitor either physiological signs, such as the heart rate and respiration rate, or metabolites. Nevertheless, most of these have focused only on a single physiological measurement at a time, which significantly inhibits the calibration of various biological signals and diagnostic facilities. In this study, a novel multifunctional hybrid skin patch was developed for the electrochemical analysis of sweat glucose levels and simultaneous monitoring of electrocardiograms (ECGs). Furthermore, pH and temperature sensors were co-integrated onto the same patch for the calibration of the glucose biosensor to prevent inevitable inhibition and weakening of enzyme activity due to changes in the sweat pH and temperature levels. The fabricated electrochemical glucose biosensor exhibited excellent linearity (R2 = 0.9986) and sensitivity (29.10 µA mM-1 cm-2), covering the normal range of human sweat. The potentiometric pH sensor displayed a good response with an excellent sensitivity of -77.81 mV/pH and high linearity (R2 = 0.991), indicating that it can distinguish variations in the typical pH range for human sweat. Furthermore, the P, QRS complex, and T peaks in the measured ECG waveforms could be clearly distinguished, indicating the reliability of the fabricated flexible dry electrodes for continuous monitoring. The fabricated skin patch overcomes the inconvenience of the mandatory attachment of multiple patches on the human body by fully integrating all the electrochemical and electrophysiological sensors on a single patch, thus facilitating advanced glycemic control and continuous ECG monitoring for smart management of chronic diseases and healthcare applications.

Radar-based remote physiological sensing: Progress, challenges, and opportunities.

Modern microwave Doppler radar-based physiological sensing is playing an important role in healthcare applications and during the last decade, there has been a significant advancement in this non-contact respiration sensing technology. The advantages of contactless, unobtrusive respiration monitoring have drawn interest in various medical applications such as sleep apnea, sudden infant death syndromes (SIDS), remote respiratory monitoring of burn victims, and COVID patients. This paper provides a perspective on recent advances in biomedical and healthcare applications of Doppler radar that can detect the tiny movement of the chest surfaces to extract heartbeat and respiration and its associated different vital signs parameters (tidal volume, heart rate variability (HRV), and so on) of the human subject. Additionally, it also highlights the challenges, and opportunities of this remote physiological sensing technology and several future research directions will be laid out to deploy this sensor technology in our day-to-day life.

Cell-mediated deposition of porous silica on bacterial biofilms.

Living hybrid materials that respond dynamically to their surrounding environment have important applications in bioreactors. Silica based sol-gels represent appealing matrix materials as they form a mesoporous biocompatible glass lattice that allows for nutrient diffusion while firmly encapsulating living cells. Despite progress in sol-gel cellular encapsulation technologies, current techniques typically form bulk materials and are unable to generate regular silica membranes over complex geometries for large-scale applications. We have developed a novel biomimetic encapsulation technique whereby endogenous extracellular matrix molecules facilitate formation of a cell surface specific biomineral layer. In this study, monoculture Pseudomonas aeruginosa and Nitrosomonas europaea biofilms are exposed to silica precursors under different acid conditions. Scanning electron microscopy (SEM) imaging and electron dispersive X-ray (EDX) elemental analysis revealed the presence of a thin silica layer covering the biofilm surface. Cell survival was confirmed 30 min, 30 days, and 90 days after encapsulation using confocal imaging with a membrane integrity assay and physiological flux measurements of oxygen, glucose, and NH 4⁺. No statistical difference in viability, oxygen flux, or substrate flux was observed after encapsulation in silica glass. Shear induced biofilm detachment was assessed using a particle counter. Encapsulation significantly reduced detachment rate of the biofilms for over 30 days. The results of this study indicate that the thin regular silica membrane permits the diffusion of nutrients and cellular products, supporting continued cellular viability after biomineralization. This technique offers a means of controllably encapsulating biofilms over large surfaces and complex geometries. The generic deposition mechanism employed to form the silica matrix can be translated to a wide range of biological material and represents a platform encapsulation technology.

Direct Construction of Catechol Lignin for Engineering Long-Acting Conductive, Adhesive, and UV-Blocking Hydrogel Bioelectronics.

Long-active conductivity, adhesiveness, and environmental stability are essential in the applications of hydrogel electronics. Integrating different functional materials into one system suffers from compatibility and cost problems. Inspired by the unique o-methoxyl structure in polyphenol lignin and its binding role in plants, catechol lignin (DAL) is constructed by one-step demethylation, which endows the lignin with a mussel-like bioadhesion, good reducibility, as well as a high ultraviolet absorption. The DAL is then applied to reduced graphene oxide, and the products-the oxidized DAL and the reduced graphene oxide mixture (DAL/rGO) is added into a sodium alginate/polyacrylamide (SA/PAM) double network hydrogel. Based on the Schiff base reaction between the quinone of the oxidized DAL and the amino of the skin, the DAL/rGO incorporated hydrogels could stably adhere to the skin, and sensitively respond to physiological signals. In addition, the DAL could provide the hydrogels with long-active sunscreen property when applied to real skin. These DAL based hydrogels have potential for on-skin sensing and outdoor sport equipment.

3D Interfacing between Soft Electronic Tools and Complex Biological Tissues.

Recent developments in soft functional materials have created opportunities for building bioelectronic devices with tissue-like mechanical properties. Their integration with the human body could enable advanced sensing and stimulation for medical diagnosis and therapies. However, most of the available soft electronics are constructed as planar sheets, which are difficult to interface with the target organs and tissues that have complex 3D structures. Here, the recent approaches are highlighted to building 3D interfaces between soft electronic tools and complex biological organs and tissues. Examples involve mesh devices for conformal contact, imaging-guided fabrication of organ-specific electronics, miniaturized probes for neurointerfaces, instrumented scaffold for tissue engineering, and many other soft 3D systems. They represent diverse routes for reconciling the interfacial mismatches between electronic tools and biological tissues. The remaining challenges include device scaling to approach the complexity of target organs, biological data acquisition and processing, 3D manufacturing techniques, etc., providing a range of opportunities for scientific research and technological innovation.

Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques.

Pain management is a crucial part in Sickle Cell Disease treatment. Accurate pain assessment is the first stage in pain management. However, pain is a subjective response and hard to assess via objective approaches. In this paper, we proposed a system to map objective physiological measures to subjective self-reported pain scores using machine learning techniques. Using Multinomial Logistic Regression and data from 40 patients, we were able to predict patients' pain scores on an 11-point rating scale with an average accuracy of 0.578 at the intra-individual level, and an accuracy of 0.429 at the inter-individual level. With a condensed 4-point rating scale, the accuracy at the inter-individual level was further improved to 0.681. Overall, we presented a preliminary machine learning model that can predict pain scores in SCD patients with promising results. To our knowledge, such a system has not been proposed earlier within the SCD or pain domains by exploiting machine learning concepts within the clinical framework.

Temperature-Dependent Detectivity of Near-Infrared Organic Bulk Heterojunction Photodiodes.

Bulk heterojunction photodiodes are fabricated using a new donor-acceptor polymer with a near-infrared absorption edge at 1.2 µm, achieving a detectivity up to 1012 Jones at a wavelength of 1 µm and an excellent linear dynamic range of 86 dB. The photodiode detectivity is maximized by operating at zero bias to suppress dark current, while a thin 175 nm active layer is used to facilitate charge collection without reverse bias. Analysis of the temperature dependence of the dark current and spectral response demonstrates a 2.8-fold increase in detectivity as the temperature was lowered from 44 to -12 °C, a relatively small change when compared to that of inorganic-based devices. The near-infrared photodiode shows a switching speed reaching up to 120 µs without an external bias. An application using our NIR photodiode to detect arterial pulses of a fingertip is demonstrated.