Non-invasive Wearable Sensors Research Articles

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.

Wearable sensor systems measure differences in knee laxity between healthy and affected knees during dynamic exercise activities: Asystematic review.

Knee laxity can be experienced as knee instability which may lead to a limitation in the activity of patients. Current methods to determine knee instability are performed in a static setting, which does not always correlate with dynamic knee laxity during activities. Wearables might be able to measure knee laxity in a dynamic setting and could be of added value in the diagnosis and treatment of excessive knee laxity. Therefore, the aim of this systematic review is to provide an overview of the wearables that have been developed and their ability to measure knee laxity during dynamic activities. The PRISMA guidelines for systematic reviews were followed. A literature search was conducted in EMBASE, PubMed and Cochrane databases. Included studies assessed patients with knee instability using a non-invasive wearable sensor system during dynamic activity, with comparison to a reference system or healthy knees. Data extraction was performed by two authors via a predefined format. The risk of bias was assessed by The Dutch checklist for diagnostic tests. A total of 4734 articles were identified. Thirteen studies were included in the review. The studies showed a great variety of patients, sensor systems, reference tests, outcome measures and performed activities. Nine of the included studies were able to measure differences in patients with knee instability, all including a tri-axial accelerometer. Differences were not measurable in all parameters and activities in these studies. Wearables, including at least a tri-axial accelerometer, seem promising for measuring dynamic knee laxity in the anterior-posterior and mediolateral direction. At this stage, it remains unclear if the measured outcomes completely reflect the knee instability that patients experience in daily life. Level III.

Noninvasive wearable sensor for the continuous monitoring of human sound and movement signals in real-time

Recently, with the development of non-invasive human health monitoring technology including wearable devices, a flexible sensor that monitors ‘human sound and movement signals’ such as human voice and muscle movement is attracting attention. In this experiment, electrospun nanofibers were mixed with highly conductive nanoparticles and coated with polyaniline to detect the patient's electrical signals. Due to the high piezoelectric effect, nanofiber-based sensors do not require charging through a separate battery, so they can be used as self-powered devices. In addition, the LCR meter test confirmed that the sensor has a high capacitance due to its high conductivity and high sensitivity to electrical signals. The sensor produced in this study can visually estimate the electrical signal of the actual human body through real-time comparison with electromyography (EMG) measuring equipment, and it was confirmed that the error is small. This sensor is expected to be widely used in the medical field, from simple sound and movement signals to disease monitoring.

A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

Antibacterial highly sensitive eco-friendly wearable piezoresistive sensor for monitoring multiple physiological parameters

Recent progress in wearable biomedical devices has made real-time monitoring possible through non-invasive wearable strain sensors, which can transform physical or biological signals into electrical responses. This work has fabricated multifunctional wearable strain sensors by coating composites (SiE-µG) of electrochemically exfoliated micro-sized multi-layer graphene flakes (µG) and Silicone elastomer (SiE) onto stretchable textile-based orthopedic bandage having ZnO adhesive (SiE-µG@Bandage). The composite coated textile sensors exhibited a remarkable sensitivity of 18330, large stretchability of 35%, superior mechanical durability of 50000 cycles, minute detection limit of 0.04% strain, and fast response time of 400 and recovery time 900 ms. The SiE-µG@Bandage also showed antibacterial properties against Escherichia coli (E. coli) bacteria. Additionally, zinc oxide-based skin-friendly and non-toxic adhesive on the backside of the bandage allowed stable and conformal attachment with the skin. Owing to the above merits, the sensors have shown applicability in real-time monitoring of various physical activities, physiological signals, joint movements, yoga postures, and meditation. Furthermore, the relevance of the sensors in the man-machine interaction has been demonstrated via real-time monitoring of traffic signals by finger movements. Moreover, the exceptional performance and specific features of the SiE-µG@Bandage sensors have enabled their application as electronics skins in health care and preventive care.

Improving Biological Joint Moment Estimation During Real-World Tasks with EMG and Instrumented Insoles.

Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

Fluid-based wearable sensors: a turning point in personalized healthcare.

Nowadays, it has become very popular to develop wearable devices that can monitor biomarkers to analyze the health status of the human body more comprehensively and accurately. Wearable sensors, specially designed for home care services, show great promise with their ease of use, especially during pandemic periods. Scientists have conducted many innovative studies on new wearable sensors that can noninvasively and simultaneously monitor biochemical indicators in body fluids for disease prediction, diagnosis, and management. Using noninvasive electrochemical sensors, biomarkers can be detected in tears, saliva, perspiration, and skin interstitial fluid (ISF). In this review, biofluids used for noninvasive wearable sensor detection under four main headings, saliva, sweat, tears, and ISF-based wearable sensors, were examined in detail. This report analyzes nearly 50 recent articles from 2017 to 2023. Based on current research, this review also discusses the evolution of wearable sensors, potential implementation challenges, and future prospects.

Wearable nanocomposite hydrogel temperature sensor based on thermally-switchable and mechanical-deformation-insensitive structural colors

Smart and flexible nanocomposite materials with structural colors have great potentials in the field of wearable sensors. However, the mechanochromic property of conventional photonic materials severely interferes with the optical signal outputs that are tunable by non-strain stimuli, which greatly impedes their applications in wearable photonic sensors. Herein, a wearable nanocomposite hydrogel temperature sensor based on thermally-switchable structural colors that are independent of mechanical deformations, is fabricated by the facile in-situ gelation of N-isopropyl acrylamide (NIPAM), nanoclay discs, carbon black, and monodisperse silica or polymeric NPs in their aqueous dispersions. The resulting non-assembled arrangements of the monodisperse NPs account for uniform structural color independent of mechanical deformations and observation angles. Simultaneously, the temperature-induced reconfiguration of the physically cross-linked poly-NIPAM hydrogel matrix, leads to super-large volume changes and in turn wide-range structural color shifts in almost the entire visible spectrum. The smart hydrogel matrix with homogenous polymer chain length by dynamic physical cross-linkings among exfoliated nanoclay discs, makes the photonic hydrogel robust, stretchable, and self-healable. The combination of mechanical-deformation-insensitive and thermally-switchable structural colors in the nanocomposite hydrogel enables skin-based wearable temperature sensing with naked-eye detection. This work opens a new platform for diverse non-invasive wearable photonic sensors beyond the current strain sensing.

Human activity recognition for analyzing stress behavior based on Bi-LSTM.

Stress is one of the critical health factors that could be detected by Human Activity Recognition (HAR) which consists of physical and mental health. HAR can raise awareness of self-care and prevent critical situations. Recently, HAR used non-invasive wearable physiological sensors. Moreover, deep learning techniques are becoming a significant tool for analyzing health data. In this paper, we propose a human lifelog monitoring model for stress behavior recognition based on deep learning, which analyses stress levels during activity. The proposed approach considers activity and physiological data for recognizing physical activity and stress levels. To tackle these issues, we proposed a model that utilizes hand-crafted feature generation techniques compatible with a Bidirectional Long Short-Term Memory (Bi-LSTM) based method for physical activity and stress level recognition. We have used a dataset called WESAD, collected using wearable sensors for model evaluation. This dataset presented four levels of stress emotion, including baseline, amusement, stress, and meditation. The following results are from the hand-crafted feature approaches compatible with the bidirectional LSTM model. The proposed model achieves an accuracy of 95.6% and an F1-score of 96.6%. The proposed HAR model efficiently recognizes stress levels and contributes to maintaining physical and mental well-being.

A highly sensitive and long-term stable wearable patch for continuous analysis of biomarkers in sweat.

Although increasing efforts have been devoted to the development of non-invasive wearable or stretchable electrochemical sweat sensors for monitoring physiological and metabolic information, most of them still suffer from poor stability and specificity over time and fluctuating temperatures. This study reports the design and fabrication of a long-term stable and highly sensitive flexible electrochemical sensor based on nanocomposite-modified porous graphene by simple and facile laser treatment for detecting biomarkers such as glucose in sweat. The laser-reduced and patterned stable conductive nanocomposite on the porous graphene electrode provides the resulting glucose sensor with an excellent sensitivity of 1317.69 μAmM-1cm-2 with an ultra-low limit of detection (LOD) of 0.079 μM. The sensor can also detect pH and exhibit extraordinary stability to maintain more than 91% sensitivity over 21 days in ambient conditions. Taken together with a temperature sensor based on the same material system, the dual glucose and pH sensor integrated with a flexible microfluidic sweat sampling network further results in accurate continuous on-body glucose detection calibrated by the simultaneously measured pH and temperature. The low-cost, highly sensitive, and long-term stable platform could facilitate and pave the way for the early identification and continuous monitoring of different biomarkers for non-invasive disease diagnosis and treatment evaluation.

Fe Single-Atom Nanozyme-Modified Wearable Hydrogel Patch for Precise Analysis of Uric Acid at Rest.

Resting sweat analysis could provide unique insight into the metabolic levels of physiological and pathological states. However, the low secretion rate of resting sweat and the low concentration of metabolic molecules pose challenges for the development of noninvasive wearable sensors. Here, we demonstrated a wearable patch for the precise analysis of uric acid at rest. Fe single-atom nanozymes (FeSAs) with excellent electrocatalytic activity were used to develop a sensor for selective catalysis of uric acid (UA, 1-425 μM), and the catalytic mechanism of UA was later explored by density functional theory. In addition, polyaniline was integrated into the wearable patch for pH detection; thus, accurate analysis of sweat UA molecules can be achieved by pH calibration. Then, we explored the possibility of collecting resting sweat with different ratios of agarose hydrogels to reduce the sweat accumulation time. Finally, the possibility of a wearable patch for accurate UA detection in volunteer sweat samples was experimentally verified. We believe that our work provides novel insights and ideas for the analysis of resting sweat using wearable devices, further driving advancements in the field of personalized medicine.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

Wearable Noninvasive Glucose Sensor Based on CuxO NFs/Cu NPs Nanocomposites.

Designing highly active material to fabricate a high-performance noninvasive wearable glucose sensor was of great importance for diabetes monitoring. In this work, we developed CuxO nanoflakes (NFs)/Cu nanoparticles (NPs) nanocomposites to serve as the sensing materials for noninvasive sweat-based wearable glucose sensors. We involve CuCl2 to enhance the oxidation of Cu NPs to generate Cu2O/CuO NFs on the surface. Due to more active sites endowed by the CuxO NFs, the as-prepared sample exhibited high sensitivity (779 μA mM-1 cm-2) for noninvasive wearable sweat sensing. Combined with a low detection limit (79.1 nM), high selectivity and the durability of bending and twisting, the CuxO NFs/Cu NPs-based sensor can detect the glucose level change of sweat in daily life. Such a high-performance wearable sensor fabricated by a convenient method provides a facile way to design copper oxide nanomaterials for noninvasive wearable glucose sensors.

Recent Advances in Wearable Biosensors for Non-Invasive Detection of Human Lactate.

Lactate, a crucial product of the anaerobic metabolism of carbohydrates in the human body, is of enormous significance in the diagnosis and treatment of diseases and scientific exercise management. The level of lactate in the bio-fluid is a crucial health indicator because it is related to diseases, such as hypoxia, metabolic disorders, renal failure, heart failure, and respiratory failure. For critically ill patients and those who need to regularly control lactate levels, it is vital to develop a non-invasive wearable sensor to detect lactate levels in matrices other than blood. Due to its high sensitivity, high selectivity, low detection limit, simplicity of use, and ability to identify target molecules in the presence of interfering chemicals, biosensing is a potential analytical approach for lactate detection that has received increasing attention. Various types of wearable lactate biosensors are reviewed in this paper, along with their preparation, key properties, and commonly used flexible substrate materials including polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), paper, and textiles. Key performance indicators, including sensitivity, linear detection range, and detection limit, are also compared. The challenges for future development are also summarized, along with some recommendations for the future development of lactate biosensors.

Smartphone light-driven zinc porphyrinic MOF nanosheets-based enzyme-free wearable photoelectrochemical sensor for continuous sweat vitamin C detection

Maintaining normal vitamin C levels is essential for the proper functioning of the body's immune system. The development of real-time non-invasive wearable sensors for monitoring sweat vitamin C holds important promise in guiding personalized health management. Here, we present a smartphone light-driven enzyme-free wearable photoelectrochemical (PEC) sensor based on two-dimensional zinc porphyrinic MOF nanosheets/multi-walled carbon nanotubes (2D-TCPP(Zn)/MCNTs) for the precise monitoring of sweat vitamin C. 2D-TCPP(Zn) nanosheets are prepared with the modulation of the pillared 4,4′-biphenyldicarboxylic acid (BPDC) molecules to form the final square-like structure. After the introduction of MCNTs, the developed 2D-TCPP(Zn)/MCNTs modified screen printing paper electrode exhibits an enhanced cathodic photocurrent response to vitamin C driven by smartphone light due to the effective charge separation with electron injection from 2D-TCPP(Zn) to MCNTs. A low limit of detection (LOD) of 3.61 μM and a wide detection range of 10–1100 μM for vitamin C are achieved. Also, the proposed electrode possesses high selectivity, satisfactory PEC stability and long-time storage stability. Moreover, a novel flexible PEC sensing patch was designed for the effective collection and continuous monitoring of sweat. The smartphone light-driven enzyme-free wearable PEC sensor detects the vitamin C concentration in real sweat with good accuracy, which can help to ensure proper nutritional balance.

Predicting Hydration Status Using Machine Learning Models From Physiological and Sweat Biomarkers During Endurance Exercise: A Single Case Study.

Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices.

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry.

Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model. The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

An Observational Study of Heart Rate Variability Using Wearable Sensors Provides a Target for Therapeutic Monitoring of Autonomic Dysregulation in Patients with Rett Syndrome.

Rett Syndrome (RTT) is a complex neurodevelopmental disorder that has multi-system involvement with co-occurring epilepsy, breathing problems and autonomic dysregulation. Autonomic dysregulation can increase the risk of cardiorespiratory vulnerability in this patient group. Assessment of heart rate variability (HRV) provides an overview of autonomic health in RTT and offers insight into how the sympathetic and parasympathetic components of the nervous system function. However, to our knowledge, no study has evaluated HRV in Rett patients to assess how the dynamics of autonomic function vary with age and changes during the day and/or night. Using non-invasive wearable sensors, we measured HRV in 45 patients with RTT and examined the time and frequency domain sympathetic and parasympathetic indices. Among the HRV indices assessed, heart rate decreases with age and is lower in the night across all ages studied. The sympathetic index (SDNN) and the parasympathetic indices (RMSSD and pNN50) are not seen to change with age. Nevertheless, these indices were all higher during the day when compared to the night. Our findings appear to show that Rett patients are less adaptable to autonomic changes during the night. In the clinical setting, this might be more relevant for patients with severe psychopathology.

Wearable Acetone Monitoring

Most research aimed at measuring biomarkers on the skin is only concerned with sensing chemicals in sweat, but these methods are not truly noninvasive because they require substantial and often inconvenient amounts of sweat to get a reading. This project aims to create a truly non-invasive wearable sensor that continuously detects the gaseous acetone (a biomarker related to metabolic disorders) that ambiently comes out of the skin. Composite films of the intrinsically conducting polymer polyaniline and cellulose acetate, previously shown to exhibit a mechano-chemical actuation response to gaseous acetone, were tested for electrical response to low concentrations of gaseous acetone and possible interferents in dry, breathing quality air at room temperature.