Physiological Sensors Research Articles

Spray-Coated Ultrathin and Porous Films for Physiological Sensing and Force Detection.

Epidermal electronics employed on human skin for the long term require good breathability and nonforeign wearing. In this work, we combine phase separation and spray coating to fabricate a porous and ultrathin electrode within minutes as well as micrometer-scale porous pressure sensors. The resulting electrodes show a water vapor transmission rate of 18.4 mg·cm-2·h-1, sheet resistance of 5.2 Ω/sq, and thickness below 5 μm. The introduction of the biogel further reduced the electrode-skin impedance, which is lower than that of the commercial gel electrode, indicating that the electrode can have a high degree of conformal contact with the skin. The epidermal electronics prepared by this strategy exhibit an excellent performance in force sensing. Such results strongly prove the efficiency and practicality of the strategy.

Biofeedback-Based Closed-Loop Phytoactuation in Vertical Farming and Controlled-Environment Agriculture.

This work focuses on biohybrid systems-plants with biosensors and actuating mechanisms that enhance the ability of biological organisms to control environmental parameters, to optimize growth conditions or to cope with stress factors. Biofeedback-based phytoactuation represents the next step of development in hydroponics, vertical farming and controlled-environment agriculture. The sensing part of the discussed approach uses (electro)physiological sensors. The hydrodynamics of fluid transport systems, estimated electrochemically, is compared with sap flow data provided by heat-based methods. In vivo impedance spectroscopy enables the discrimination of water, nutrient and photosynthates in the plant stem. Additionally to plant physiology, the system measures several air/soil and environmental parameters. The actuating part includes a multi-channel power module to control phytolight, irrigation, fertilization and air/water preparation. We demonstrate several tested in situ applications of a closed-loop control based on real-time biofeedback. In vertical farming, this is used to optimize energy and water consumption, reduce growth time and detect stress. Biofeedback was able to reduce the microgreen production cycle from 7 days to 4-5 days and the production of wheatgrass from 10 days to 7-8 days, and, in combination with biofeedback-based irrigation, a 30% increase in pea biomass was achieved. Its energy optimization can reach 25-30%. In environmental monitoring, the system performs the biological monitoring of environmental pollution (a low concentration of O3) with tomato and tobacco plants. In AI research, a complex exploration of biological organisms, and in particular the adaptation mechanisms of circadian clocks to changing environments, has been shown. This paper introduces a phytosensor system, describes its electrochemical measurements and discusses its tested applications.

TMTT CFP Special Issue on Latest Advances on Radar-Based Physiological Sensors and Their Applications

TMTT CFP Special Issue on Latest Advances on Radar-Based Physiological Sensors and Their Applications

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

Behave Yourself! Behavioral Indicators of Trust in Human-Agent Teams

As advancements in artificial intelligence accelerate, there is a rise in the complexity and number of autonomous agents placed in human-agent teams (HATs). With this expansion, it is important to understand how trust in agent teammates evolves and is influenced by contextual events. In support of this, significant research has focused on the factors that influence human trust in an agent and elements that negatively impact this trust. In this research, human trust in agent teammates is typically measured via self-report surveys. Although a reliable format, surveys are not without limitations, as they can be disruptive and lack the temporal resolution needed to capture nuanced and dynamic changes in trust. As a result, researchers are adopting alternative measurement approaches that aim to unobtrusively capture measures that correlate with trust using behavioral and physiological sensors that capture measures such as voice pitch and eye tracking. However, most of these studies utilize only one unobtrusive measure, which can be indicative of various states (e.g., heart rate varies with psychological and physical responses). Given the lack of selectivity in such measures, it may be necessary to utilize multiple unobtrusive measures at once, such as real-time recorded behavioral indicators, to effectively capture changes in these indicators that are due to changes in trust over time. This paper investigated the following research questions: (1) do multiple unobtrusive indicators provide a more holistic picture of trust compared to a single behavioral indicator, and (2) are certain behavioral indicators more indicative of self-reported trust at different times during a mission (e.g., before/after a trust violation event)? Results indicated that following a significant event, changes in self-reported trust are associated with specific behaviors related to improving or mitigating perceived deficiencies in their agent teammates. These insights highlight the opportunity to integrate dynamic behavioral measures in trust assessment frameworks, specifically in scenarios where a significant event compromises trust.

The Impact of Wearable Technology on Health Monitoring

Wearable technology is transforming health monitoring by enabling real-time, continuous tracking of various physiological parameters. This paper examines the evolution, benefits, challenges, and future trends of wearables in healthcare. The integration of sensors and mobile technology allows for more efficient personal and population health management. However, concerns about data accuracy, privacy, and ethical implications persist. As innovations such as smart clothing and AI-driven diagnostics emerge, the role of wearables in healthcare is expected to expand, offering new possibilities for early detection, preventive care, and personalized medicine. Keywords: Wearable technology, Health monitoring, Smart devices, Physiological sensors, Data privacy

IEEE Transactions on Microwave Theory and Techniques: Special Issue on Latest Advances on Radar-Based Physiological Sensors and Their Applications

IEEE Transactions on Microwave Theory and Techniques: Special Issue on Latest Advances on Radar-Based Physiological Sensors and Their Applications

Recent Advances of Stretchable Nanomaterial-Based Hydrogels for Wearable Sensors and Electrophysiological Signals Monitoring.

Electrophysiological monitoring is a commonly used medical procedure designed to capture the electrical signals generated by the body and promptly identify any abnormal health conditions. Wearable sensors are of great significance in signal acquisition for electrophysiological monitoring. Traditional electrophysiological monitoring devices are often bulky and have many complex accessories and thus, are only suitable for limited application scenarios. Hydrogels optimized based on nanomaterials are lightweight with excellent stretchable and electrical properties, solving the problem of high-quality signal acquisition for wearable sensors. Therefore, the development of hydrogels based on nanomaterials brings tremendous potential for wearable physiological signal monitoring sensors. This review first introduces the latest advancement of hydrogels made from different nanomaterials, such as nanocarbon materials, nanometal materials, and two-dimensional transition metal compounds, in physiological signal monitoring sensors. Second, the versatile properties of these stretchable composite hydrogel sensors are reviewed. Then, their applications in various electrophysiological signal monitoring, such as electrocardiogram monitoring, electromyographic signal analysis, and electroencephalogram monitoring, are discussed. Finally, the current application status and future development prospects of nanomaterial-optimized hydrogels in wearable physiological signal monitoring sensors are summarized. We hope this review will inspire future development of wearable electrophysiological signal monitoring sensors using nanomaterial-based hydrogels.

Users' Perspectives on Multimodal Menstrual Tracking Using Consumer Health Devices

Previous menstrual health literature highlights a variety of signals not included in existing menstrual trackers because they are either difficult to gather or are not typically associated with menstrual health. Since it has become increasingly convenient to collect biomarkers through wearables and other consumer-grade devices, our work examines how people incorporate unconventional signals (e.g., blood glucose levels, heart rate) into their understanding of menstrual health. In this paper, we describe a three-month-long study on fifty participants' experiences as they tracked their health using physiological sensors and daily diaries. We analyzed their experiences with both conventional and unconventional menstrual health signals through surveys and interviews conducted throughout the study. We delve into the various aspects of menstrual health that participants sought to affirm using unconventional signals, explore how these signals influenced their daily behaviors, and examine how multimodal menstrual tracking expanded their scope of menstrual health. Finally, we provide design recommendations for future multimodal menstrual trackers.

Functional seizure therapy via transauricular vagus nerve stimulation

This article explores the potential of transauricular Vagus nerve stimulation (taVNS) in treating Functional Seizures (FS), a condition that significantly burdens healthcare systems. Traditional seizure treatments are ineffective for FS due to its unique pathophysiology, highlighting the urgent need for alternative therapeutic strategies. While invasive VNS has shown promise in improving autonomic balance, its invasive nature poses limitations in FS treatment. taVNS, a non-invasive alternative, enhances parasympathetic tone and reduces sympathetic activity. Additionally, it is hypothesized to modulate interoceptive processing by influencing Heart Rate Evoked Potential and normalizing interoceptive signals. This hypothesis article examines taVNS from a closed-loop perspective, focusing on the controllability and observability of its effects using wearable physiological sensors. It postulates regulating desired therapeutic states through physiological sensor feedback, suggesting the potential for customized, adaptive stimulation in FS treatment. However, rigorous testing of its controller and observer functions will be necessary for optimal clinical translation of adaptive taVNS.

Using Physiological Biomarkers to Optimize Management of TBI in Austere Environments.

Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

PTPN2 copper-sensing relays copper level fluctuations into EGFR/CREB activation and associated CTR1 transcriptional repression

Fluxes in human copper levels recently garnered attention for roles in cellular signaling, including affecting levels of the signaling molecule cyclic adenosine monophosphate. We herein apply an unbiased temporal evaluation of the signaling and whole genome transcriptional activities modulated by copper level fluctuations to identify potential copper sensor proteins responsible for driving these activities. We find that fluctuations in physiologically relevant copper levels modulate EGFR signal transduction and activation of the transcription factor CREB. Both intracellular and extracellular assays support Cu1+ inhibition of the EGFR phosphatase PTPN2 (and potentially PTPN1)–via ligation to the PTPN2 active site cysteine side chain–as the underlying mechanism. We additionally show i) copper supplementation drives weak transcriptional repression of the copper importer CTR1 and ii) CREB activity is inversely correlated with CTR1 expression. In summary, our study reveals PTPN2 as a physiological copper sensor and defines a regulatory mechanism linking feedback control of copper stimulated EGFR/CREB signaling and CTR1 expression.

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Current Trends in the Development of Electrochemical Biosensor for Detecting Analytes from Sweat.

The need for wearable bioelectronics continues to grow, and this technology might significantly alter the medical field. In order to diagnose and treat a patient, conventional medicine takes a "reactive" approach and waits for symptoms to appear first. Therefore, it is preferable to progress toward continuous non-invasive wearable biomonitoring, a preventative strategy that may assist individuals in diagnosing or treating illnesses at the earliest stages, sometimes before any outward symptoms have appeared. Wearable physiological sensors, such as the Apple Watch and FitBit, have arrived on the market as a result of technology advances and have quickly become commonplace. However, few devices currently exist that can report directly on these biomarkers of relevance. This is mostly due to the challenges involved in real-time fluid sampling and generating correct readouts utilising extremely selective and sensitive sensors. Sweat is an excretory fluid that is only allowed to be used in order to reduce invasiveness, but this restriction places additional strain on sensors owing to the diluted concentration of the relevant biomarkers and the changes in pH, salinity, and other biophysical parameters that directly influence the read-out of real-time biosensors. Sweat is favoured amid slightly invasive biofluids due to its low concentration of interfering chemicals and the fact that it may be collected without touching the mucosal layers. This review offers a concise outline of the latest advances in sweat-based wearable sensors, their promise in healthcare monitoring, and the problems faced in analysis based on sweat.

Physical performance estimation in practice: A systematic review of advancements in performance prediction and modeling in cycling

Physical performance in cycling is commonly evaluated with laboratory-based performance markers. However, these markers are not monitored on a regular basis, mainly due to the high costs of testing equipment, invasive sampling and time-intensive protocols. The use of mathematical modeling offers a promising alternative allowing for consistent performance monitoring, identification of influential variables affecting performance, and facilitation of planning, monitoring, and predictive analysis. Wearable technology, such as physiological and biomechanical sensors, can be integrated with mathematical models to enhance the practicality of performance monitoring and enable real-time feedback and personalized training recommendations. In this systematic review, we attempted to provide an overview of the developments in predicting and modeling of performance in cycling and their respective practical applications. The PRISMA framework yielded 52 studies that met the inclusion criteria. The models were discussed according to their modeling goal: characterizing kinetics, alternatives to the gold-standard, training control, observing training effects, predicting competitive performance and optimizing performance. Field-based models and technological advancements were highlighted as solutions to the limitations of gold-standard testing. Due to the lower accuracies of modeling techniques, the gold-standard laboratory-based methods of testing will not be replaced by mathematical models. However, models do form a more practical alternative for regular monitoring and a powerful tool for training and competition optimization. A modeling technique needs to be individualized to the goal and the person and be as simple as possible to allow regular monitoring. Ideally, the technique would work in the field, uses submaximal exercise intensities and integrates technological advancements such as wearable technology and machine learning to increase the practicality even more.

Safety culture and worker fatigue management in the offshore oil and gas industry: An interview study

Multiple incidents in the offshore oil and gas industry have been associated with poor safety culture. Regular assessments of safety culture among operators and contractors is recommended as part of a safety management system. Poor safety culture has also shown to impact how operators manage offshore hazards, such as worker fatigue. Assessing workers' fatigue states is also critical to ensure safety in the offshore oil industry. This paper describes findings from an interview study that aimed to identify current safety culture assessment and worker fatigue management practices in the offshore oil and gas industry. One-hour virtual semi-structured interviews were conducted with eighteen offshore oil rig supervisors. Various state-of-the-art methods for assessing safety culture (e.g., experience sampling method) and worker fatigue (e.g., physiological sensors and psychomotor vigilance test) were introduced to the participants. Participants commented on the feasibility and potential barriers to implementation/administration of the various methods, as well as how the information might be useful in their supervisory decisions. User expectations for a safety dashboard displaying data from such tools and user requirements for such a dashboard were elicited. In addition, participants completed a modified technology readiness and acceptance model questionnaire to assess participants' readiness levels and perceived usefulness of a safety dashboard. The interview results revealed a mixed understanding of what safety culture is and opinions about safety culture measurements. Participants indicated that efforts to manage fatigue currently relied solely on supervisors' observation and workers' self-reports. Participants’ opinions about the new assessment methods varied. Some were supportive and commented that the new methods will be helpful to improve supervisory-level decisions, whereas others pointed out potential compliance issues.

Enhancing Aviation Safety through AI-Driven Mental Health Management for Pilots and Air Traffic Controllers.

This article provides an overview of the mental health challenges faced by pilots and air traffic controllers (ATCs), whose stressful professional lives may negatively impact global flight safety and security. The adverse effects of mental health disorders on their flight performance pose a particular safety risk, especially in sudden unexpected startle situations. Therefore, the early detection, prediction and prevention of mental health deterioration in pilots and ATCs, particularly among those at high risk, are crucial to minimize potential air crash incidents caused by human factors. Recent research in artificial intelligence (AI) demonstrates the potential of machine and deep learning, edge and cloud computing, virtual reality and wearable multimodal physiological sensors for monitoring and predicting mental health disorders. Longitudinal monitoring and analysis of pilots' and ATCs physiological, cognitive and behavioral states could help predict individuals at risk of undisclosed or emerging mental health disorders. Utilizing AI tools and methodologies to identify and select these individuals for preventive mental health training and interventions could be a promising and effective approach to preventing potential air crash accidents attributed to human factors and related mental health problems. Based on these insights, the article advocates for the design of a multidisciplinary mental healthcare ecosystem in modern aviation using AI tools and technologies, to foster more efficient and effective mental health management, thereby enhancing flight safety and security standards. This proposed ecosystem requires the collaboration of multidisciplinary experts, including psychologists, neuroscientists, physiologists, psychiatrists, etc. to address these challenges in modern aviation.

Generalisable sensor-free frustration detection in online learning environments using machine learning

Learning can generally be categorised into three domains, which include cognitive (thinking), affective (emotions or feeling) and psychomotor (physical or kinesthetic). In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners’ affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students’ affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students’ frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students’ affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors’ performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers.

Designing a practical fatigue detection system: A review on recent developments and challenges

IntroductionFatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work. MethodsThis paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed. Practical ApplicationsThe directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

Calibrating Glucose Sensors at the Edge: A Stress Generation Model for Tiny ML Drift Compensation

Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a dataset generation model and this, in turn, enables the design of improved CGM systems. Methods: The presented approach uses a combination of physiological data and sensor characteristics to construct a model that considers the impact of these variables on the accuracy of CGM measures. A dataset of 500 sensor responses over a 15-day period is generated and analyzed using machine learning algorithms (random forest regressor and support vector regressor). Results: The random forest and support vector regression models achieved Mean Absolute Errors (MAEs) of 16.13 mg/dL and 16.22 mg/dL, respectively. In contrast, models trained solely on single sensor outputs recorded an average MAE of 11.01±5.12 mg/dL. These findings demonstrate the variable impact of integrating multiple data sources on the predictive accuracy of CGM systems, as well as the complexity of the dataset. Conclusions: This approach provides a foundation for developing more precise algorithms and introduces its initial application of Tiny Machine Control Units (MCUs). More research is recommended to refine these models and validate their effectiveness in clinical settings.