Pumpless microfluidic sweat sensing yarn.

Textile sweat sensors possess immense potential for non-invasive health monitoring. Rapid in-situ sweat capture and prevention of its evaporation are crucial for accurate and stable real-time monitoring. Herein, we introduce a unidirectional, pump-free microfluidic sweat management system to tackle this challenge. A nanofiber sheath layer on micrometer-scale sensing filaments enables this pumpless microfluidic design. Utilizing the capillary effect of the nanofibers allows for the swift capture of sweat, while the differential configuration of the hydrophilic and hydrophobic properties of the sheath and core yarns prevents sweat evaporation. The Laplace pressure difference between the cross-scale fibers facilitates the management system to ultimately expulse sweat. This results in microfluidic control of sweat without the need for external forces, resulting in rapid (<5 s), sensitive (19.8 nA µM-1), and stable (with signal noise and drift suppression) sweat detection. This yarn sensor can be easily integrated into various fabrics, enabling the creation of health monitoring smart garments. The garments maintain good monitoring performance even after 20 washes. This work provides a solution for designing smart yarns for high-precision, stable, and non-invasive health monitoring.

An Integrated All-Natural Conductive Supramolecular Hydrogel Wearable Biosensor with Enhanced Biocompatibility and Antibacterial Properties.

Conductive hydrogels exhibit tremendous potential for wearable bioelectronics, biosensing, and health monitoring applications, yet concurrently enhancing their biocompatibility and antimicrobial properties remains a long-standing challenge. Herein, we report an all-natural conductive supramolecular hydrogel (GT5-DACD2-B) prepared via the Schiff base reaction between the biofriendly dialdehyde cyclodextrin and gelatin. The potent antibacterial agent fusidic acid (FA) is incorporated through host-guest inclusion, enabling 100% inhibition of Staphylococcus aureus proliferation. The biocompatibility of our hydrogel is bolstered with tannic acid (TA) facilitating antibacterial effects through interactions with gelatin, while borax augments conductivity. This supramolecular hydrogel not only exhibits stable conductivity and rapid response characteristics but also functions as a flexible sensor for monitoring human movement, facial expressions, and speech recognition. Innovatively integrating biocompatibility, antimicrobial activity, and conductivity into a single system, our work pioneers a paradigm for developing multifunctional biosensors with integrated antibacterial functionalities, paving the way for advanced wearable bioelectronics with enhanced safety and multifunctionality.

A wearable nanozyme-enzyme electrochemical biosensor for sweat lactate monitoring.

In this study, we developed a wearable nanozyme-enzyme electrochemical biosensor that enablies sweat lactate monitoring. The biosensor comprises a flexible electrode system prepared on a polyimide (PI) film and the Janus textile for unidirectional sweat transport. We obtained favorable electrochemical activities for hydrogen peroxide reduction by modifying the laser-scribed graphene (LSG) electrode with cerium dioxide (CeO2)-molybdenum disulphide (MoS2) nanozyme and gold nanoparticles (AuNPs). By further immobilisation of lactate oxidase (LOx), the proposed biosensor achieves chronoamperometric lactate detection in artificial sweat within a range of 0.1-50.0 mM, a high sensitivity of 25.58 µA mM-1cm-2 and a limit of detection (LoD) down to 0.135 mM, which fully meets the requirements of clinical diagnostics. We demonstrated accurate lactate measurements in spiked artificial sweat, which is consistent with standard ELISA results. To monitor the sweat produced by volunteers while exercising, we conducted on-body tests, showcasing the wearable biosensor's ability to provide clinical sweat lactate diagnosis for medical treatment and sports management.

Role of wearable electrochemical biosensors in monitoring renal function biomarkers in sweat: a review.

Real-time detection of renal biomarkers is crucial for immediate and continuous monitoring of kidney function, facilitating early diagnosis and intervention in kidney-related disorders. This proactive approach enables timely adjustments in treatment plans, particularly in critical situations, and enhances overall patient care. Wearable devices emerge as a promising solution, enabling non-invasive and real-time data collection. This comprehensive review investigates numerous types of wearable sensors designed to detect kidney biomarkers in body fluids such as sweat. It critically evaluates the precision, dependability, and user-friendliness of these devices, contemplating their seamless integration into daily life for continuous health tracking. The review highlights the potential influence of wearable technology on individualized renal healthcare and its role in preventative medicine while also addressing challenges and future directions. The review's goal is to provide guidance to academics, healthcare professionals, and technologists working on wearable solutions for renal biomarker detection by compiling the body of current knowledge and advancements.

Quantum-Dot Light-Emitting Fiber Toward All-In-One Clothing-Type Health Monitoring.

In the Fourth Industrial Revolution, as the connection between objects and people becomes increasingly important, interest in wearable optoelectronic device-based medical diagnosis is on the rise. Pulse oximetry sensors based on a fiber platform, which is the smallest unit of clothing, could be considered an attractive candidate for this application. In this study, red and green quantum-dot light-emitting fibers (QDLEFs) based on a 250 µm-diameter 1-dimensional fiber were successfully implemented, achieving high current efficiencies of approximately 22.46 mW/sr/A and 23.6 mW/sr/A and narrow full-width at half-maximum (FWHM) of about 33 nm, respectively. In addition, its omnidirectional flexibility was confirmed through a vertical and lateral bending test with 0.92% strain. By employing a transparent and flexible elastomer, a wearable pulse oximeter incorporating QDLEFs was successfully demonstrated for oxygen saturation level (SpO2) monitoring on finger and wrist. It was demonstrated to be washable, and could be operated for up to about 18 h. Due to the elastomer and bottom emission, it exhibited excellent wear resistance characteristics in a 50 cycle reciprocating test conducted at about 2180.43 kPa with 220-grit abrasive paper sheet. A theoretical investigation based on modified photon diffusion analysis (MPDA) modeling also determined that using narrow FWHM light sources, such as QDLEFs, improves the resolution and accuracy of SpO2 monitoring. Accordingly, the proposed QDLEF showed distinguished potential as an all-in-one clothing type pulse oximetry.

Synergistic enhancement of wearable biosensor through Pt single-atom catalyst for sweat analysis.

Real-time monitoring of biological markers in sweat is a valuable tool for health assessment. In this study, we have developed an innovative wearable biosensor for precise analysis of glucose in sweat during physical activities. The sensor is based on a single-atom catalyst of platinum (Pt) uniformly dispersed on tricobalt tetroxide (Co3O4) nanorods and reduced graphene oxide (rGO), featuring a unique three-dimensional nanostructure and excellent glucose electrocatalytic performance with a wide detection range of 1-800 µM. Additionally, density functional theory calculations have revealed the synergetic role of Pt active sites in the Pt single-atom catalyst (Co3O4/rGO/Pt) in glucose adsorption and electron transfer, thereby enhancing sensor performance. To enable application in wearable devices, we designed an S-shaped microfluidic chip and a point-of-care testing (POCT) device, both of which were validated for effectiveness through actual use by volunteers. This research provides valuable insights and innovative approaches for analyzing sweat glucose using wearable devices, contributing to the advancement of personalized healthcare.

Wearable Biosensor Technology in Education: A Systematic Review.

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

Large-Scale Wearable Textile-Based Sweat Sensor with High Sensitivity, Rapid Response, and Stable Electrochemical Performance.

Textile-based sweat sensors display great potential to enhance wearable comfort and health monitoring; however, their widespread application is severely hindered by the intricate manufacturing process and electrochemical characteristics. To address this challenge, we combined both impregnation coating technology and conjugated electrospinning technology to develop an electro-assisted impregnation core-spinning technology (EAICST), which enables us to simply construct a sheath-core electrochemical sensing yarn (TPFV/CPP yarn) via coating PEDOT:PSS-coated carbon fibers (CPP) with polyurethane (TPU)/polyacrylonitrile (PAN)/poloxamer (F127)/valinomycin as shell. The TPFV/CPP yarn was sewn into the fabric and integrated with a sensor to achieve a detachable feature and efficiently monitor K+ levels in sweat. By introducing EAICST, a speed of 10 m/h can be realized in the continuous preparation of the TPFV/CPP yarn, while the interconnected pores in the yarn sheath enable it to quickly capture and diffuse sweat. Besides, the sensor exhibited excellent sensitivity (54.26 mV/decade), fast response (1.7 s), anti-interference, and long-term stability (5000 s or more). Especially, it also possesses favorable washability and wear resistance properties. Taken together, this study provides a crucial technical foundation for the development of advanced wearable devices designed for sweat analysis.

Fully integrated wearable microneedle biosensing platform for wide-range and real-time continuous glucose monitoring.

Wearable microneedle sensors for continuous glucose monitoring (CGM) have great potential for clinical impact by allowing access to large data sets to provide individualized treatment plans. To date, their development has been challenged by the accurate wide linear range tracking of interstitial fluid (ISF) glucose (Glu) levels. Here, we present a CGM platform consisting of a three-electrode microneedle electrochemical biosensor and a fully integrated radio-chemical analysis system. The long-term performance of the robust CGM on diabetic rats was achieved by electrodepositing Prussian blue (PB), and crosslinking glucose oxidase (GOx) and chitosan to form a 3D network using glutaraldehyde (GA). After redox by GOx, PB rapidly decomposes hydrogen peroxide and mediates charge transfer, while the 3D network and graphite powder provide enrichment and release sites for Glu and catalytic products, enabling a sensing range of 0.25-35 mM. Microneedle CGM has high sensitivity, good stability, and anti-interference ability. In diabetic rats, CGM can accurately monitor Glu levels in the ISF in real-time, which are highly consistent with levels measured by commercial Glu meters. These results indicate the feasibility and application prospects of the PB-based CGM for the clinical management of diabetes. STATEMENT OF SIGNIFICANCE: This study addresses the challenge of continuous glucose monitoring system design where the narrow linear range of sensing due to the miniaturization of sensors fails to meet the monitoring needs of clinical diabetic patients. This was achieved by utilizing a three-dimensional network of glutaraldehyde cross-linked glucose oxidase and chitosan. The unique topology of the 3D network provides a large number of sites for glucose enrichment and anchors the enzyme to the sensing medium and the conductive substrate through covalent bonding, successfully blocking the escape of the enzyme and the sensing medium and shortening the electron transfer and transmission path.

Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor-A wrist angel feasibility study.

Introduction: Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models. Methods: Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients. Results: Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation. Conclusion: Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes. Clinical trial registration: ClinicalTrials.gov: NCT05064527, registered October 1, 2021.

Continuous monitoring of multiple biomarkers with an ultrasensitive 3D-structured wearable biosensor.

Chronic diseases call for routine management of frequent monitoring of specific biomarkers. Traditional in vitro diagnostics technologies suffer from complex sampling processes and long detection intervals, which cannot meet the need of continuous monitoring. Wearable devices taking advantage of compact size, rapid detection process, and small sample consumption are promising to take the place of endpoint detection, providing more comprehensive information about human health. Here, we proposed a fully integrated wearable system with an ultrasensitive 3D-structured biosensor for real-time monitoring of multiple metabolites. The 3D-structured biosensor shows wide linear ranges of 400-1,400 µM and 0.1-8 mM and high sensitivities of 460.5 and 283.09 µA/(mM·cm2) for lactate and glucose detection, respectively. We have conducted in vivo animal experiments, and the proposed wearable biosensor demonstrated high consistency with established methods. We envision that this system could provide a real-time wearable detection platform for multiple biomarker detection.

Expanded Carbon Nanotube Fiber at the Liquid-Air Interface for High-Performance Fiber-Based Supercapacitors and Electrochemical Sensors.

Carbon nanotube fibers (CNTFs) are widely utilized in flexible and wearable electronics due to their outstanding electrical and mechanical properties. However, the spinning process of CNTFs has limited the CNTs from exposure, leading to an ultralow usage efficiency of individual CNTs. Here, we propose an electrochemical expansion strategy of a single CNTF at the liquid-air interface, forming a macroscopic spindle-shaped CNTF (SS-CNTF) with an enlarged volume of up to 5000-fold upon the spindle. The obtained spindle-shaped structure endows CNTF with a high specific surface area together with excellent conductivity and good mechanical properties. Therefore, the SS-CNTF-based devices exhibit outstanding performances both in energy storage (electrical double-layer supercapacitor, energy density: 11.22 Wh kg-1, power density: 203.9 kW kg-1) and electrochemical sensing (ascorbic acid: 1.26 µA µM-1 cm-2; dopamine: 103.91 µA µM-1 cm-2; uric acid: 11.53 µA µM-1 cm-2). The novel architecture of SS-CNTF prepared by one-step electrochemical expansion at the liquid-air interface enabled its high performance in multiple applications, providing new insight into the development of CNTF-based devices.

Wearable Insulin Biosensors for Diabetes Management: Advances and Challenges.

We present a critical review of the current progress in wearable insulin biosensors. For over 40 years, glucose biosensors have been used for diabetes management. Measurement of blood glucose is an indirect method for calculating the insulin administration dosage, which is critical for insulin-dependent diabetic patients. Research and development efforts aiming towards continuous-insulin-monitoring biosensors in combination with existing glucose biosensors are expected to offer a more accurate estimation of insulin sensitivity, regulate insulin dosage and facilitate progress towards development of a reliable artificial pancreas, as an ultimate goal in diabetes management and personalised medicine. Conventional laboratory analytical techniques for insulin detection are expensive and time-consuming and lack a real-time monitoring capability. On the other hand, biosensors offer point-of-care testing, continuous monitoring, miniaturisation, high specificity and sensitivity, rapid response time, ease of use and low costs. Current research, future developments and challenges in insulin biosensor technology are reviewed and assessed. Different insulin biosensor categories such as aptamer-based, molecularly imprinted polymer (MIP)-based, label-free and other types are presented among the latest developments in the field. This multidisciplinary field requires engagement between scientists, engineers, clinicians and industry for addressing the challenges for a commercial, reliable, real-time-monitoring wearable insulin biosensor.

Epidermal Wearable Biosensors for the Continuous Monitoring of Biomarkers of Chronic Disease in Interstitial Fluid.

Healthcare technology has allowed individuals to monitor and track various physiological and biological parameters. With the growing trend of the use of the internet of things and big data, wearable biosensors have shown great potential in gaining access to the human body, and providing additional functionality to analyze physiological and biochemical information, which has led to a better personalized and more efficient healthcare. In this review, we summarize the biomarkers in interstitial fluid, introduce and explain the extraction methods for interstitial fluid, and discuss the application of epidermal wearable biosensors for the continuous monitoring of markers in clinical biology. In addition, the current needs, development prospects and challenges are briefly discussed.

A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study.

BACKGROUND: Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. An essential component of therapy involves increasing awareness of the links among thoughts, emotions, behaviors, bodily sensations, and family interactions. An automatic assessment tool using physiological signals from a wearable biosensor may enable continuous symptom monitoring inside and outside of the clinic and support cognitive behavioral therapy for OCD. OBJECTIVE: The primary aim of this study is to evaluate the feasibility and acceptability of using a wearable biosensor to monitor OCD symptoms. The secondary aim is to explore the feasibility of developing clinical and research tools that can detect and predict OCD-relevant internal states and interpersonal processes with the use of speech and behavioral signals. METHODS: Eligibility criteria for the study include children and adolescents between 8 and 17 years of age diagnosed with OCD, controls with no psychiatric diagnoses, and one parent of the participating youths. Youths and parents wear biosensors on their wrists that measure pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes, while control youths and their parents mark youth fear episodes. Continuous, in-the-wild data collection will last for 8 weeks. Controlled experiments designed to link physiological, speech, behavioral, and biochemical signals to mental states are performed at baseline and after 8 weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, and at weeks 4, 7, and 8. Feasibility criteria were set for recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, and safety related to the biosensor. As a first step in learning the associations between signals and OCD-related parameters, we will use paired t tests and mixed effects models with repeated measures to assess associations between oxytocin, individual biosignal features, and outcomes such as stress-rest and case-control comparisons. RESULTS: The first participant was enrolled on December 3, 2021, and recruitment closed on December 31, 2022. Nine patient dyads and nine control dyads were recruited. Sixteen participating dyads completed follow-up assessments. CONCLUSIONS: The results of this study will provide preliminary evidence for the extent to which a wearable biosensor that collects physiological signals can be used to monitor OCD severity and events in youths. If we find the study to be feasible, further studies will be conducted to integrate biosensor signals output into machine learning algorithms that can provide patients, parents, and therapists with actionable insights into OCD symptoms and treatment progress. Future definitive studies will be tasked with testing the accuracy of machine learning models to detect and predict OCD episodes and classify clinical severity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/ct2/show/NCT05064527. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45123.

A wearable electrochemical fabric for cytokine monitoring.

Wearable biosensors monitoring various biomarkers in sweat provide comprehensive and prompt profiling of health states at molecular levels. Cytokines existed in sweat with trace amounts play an important role in cellular activity modulation. Unfortunately, flexible and wearable biosensors for cytokine monitoring have not yet been achieved due to the limitation of membrane-based structure and sensing strategy. Herein, we develop a novel electrochemical fabric based on aptamer-functionalized carbon nanotube/graphene fibers for real-time and in situ monitoring of IL-6, a paramount cytokine biomarker for inflammation and cancer. This fabric system possesses flexibility, anti-fatigue ability and breathability for wearable applications and can apply to different body parts in various forms. Moreover, the electrochemical fabric can track other biomarkers by replacing the coupling aptamer, serving as a universal platform for sweat analysis. This fabric-based platform holds the potential to facilitate an intelligent and personalized health monitoring approach.

Nonenzymatic Flexible Wearable Biosensors for Vitamin C Monitoring in Sweat.

Nutritional status monitoring plays an important role in the maintenance of human health and disease prevention. Monitoring the intake of vitamins can support the improvement of diet behavior. In this work, a polyaniline (PANI) film-based nonenzymatic electrochemical sensor was prepared to track the vitamin C level in sweat. The PANI film was modified with organic acids (ethylformic acid, malic acid, tartaric acid, and phytic acid). The phytic acid-modified PANI film based on sensor has a wide detection range (0.5-500 µmol·L-1), high sensitivity (665.5 and 326.2 µA·(mmol·L-1)-1·cm-2), and low detection limit (0.17 µmol·L-1) toward vitamin C in sweat. The phytate enhances the band transport between PANI chains, which increases the electrical conductivity of the film to improve the electrochemical properties of the sensor. In addition, we monitored changes of vitamin C levels in human body after taking vitamin C pills by detecting sweat or saliva. The ability to track the pharmacological profile demonstrates the potential of PANI film-based sensors for applications in personalized nutritional intake and tracking. And a simple and portable vitamin C detection system was developed to improve the practicability of the sensor. This work provides an idea for the application of wearable electrochemical sensing devices in nutrition guidance.

Epidermal Wearable Biosensors for Monitoring Biomarkers of Chronic Disease in Sweat.

Biological information detection technology is mainly used for the detection of physiological and biochemical parameters closely related to human tissues and organ lesions, such as biomarkers. This technology has important value in the clinical diagnosis and treatment of chronic diseases in their early stages. Wearable biosensors can be integrated with the Internet of Things and Big Data to realize the detection, transmission, storage, and comprehensive analysis of human physiological and biochemical information. This technology has extremely wide applications and considerable market prospects in frontier fields including personal health monitoring, chronic disease diagnosis and management, and home medical care. In this review, we systematically summarized the sweat biomarkers, introduced the sweat extraction and collection methods, and discussed the application and development of epidermal wearable biosensors for monitoring biomarkers in sweat in preclinical research in recent years. In addition, the current challenges and development prospects in this field were discussed.

Fully integrated wearable humidity sensor for respiration monitoring.

Respiration monitoring is a promising alternative to medical diagnosis of several diseases. However, current techniques of respiration monitoring often require expensive and cumbersome devices which greatly limit their medical applications. Here, we present a fully integrated wearable device consisting of a flexible LCP-copper interdigital electrode, a sensing layer and a wireless electrochemical analysis system. The developed humidity sensor exhibits a high sensitivity, a good repeatability and a rapid response/recover time. The long-term stability is over 30 days at different relative humidity. By integrating the flexible humidity sensor with miniaturized electrochemical analysis system (0.8 cm × 1.8 cm), response current concerning respiration can be wirelessly transmitted to App-assisted smartphone in real time. Furthermore, the fabricated humidity sensor can realize skin moisture monitoring in a touch-less way. The large-scale production of miniaturized flexible sensor (4 mm × 6 mm) has significantly contributed to commercial deployment.

Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.