Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Regular snoring is associated with uncontrolled hypertension.

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence. In this study, 12,287 participants were monitored nightly for approximately six months using under-the-mattress sensor technology to estimate the average percentage of sleep time spent snoring per night and the estimated apnea-hypopnea index (eAHI). Blood pressure cuff measurements from multiple daytime assessments were averaged to define uncontrolled hypertension based on mean systolic blood pressure≥140 mmHg and/or a mean diastolic blood pressure ≥90 mmHg. Associations between snoring and uncontrolled hypertension were examined using logistic regressions controlled for age, body mass index, sex, and eAHI. Participants were middle-aged (mean ± SD; 50 ± 12 y) and most were male (88%). There were 2467 cases (20%) with uncontrolled hypertension. Approximately 29, 14 and 7% of the study population snored for an average of >10, 20, and 30% per night, respectively. A higher proportion of time spent snoring (75th vs. 5th; 12% vs. 0.04%) was associated with a ~1.9-fold increase (OR [95%CI]; 1.87 [1.63, 2.15]) in uncontrolled hypertension independent of sleep apnea. Multi-night objective snoring assessments and repeat daytime blood pressure recordings in a large global consumer sample, indicate that snoring is common and positively associated with hypertension. These findings highlight the potential clinical utility of simple, objective, and noninvasive methods to detect snoring and its potential adverse health consequences.

A novel TASK channel antagonist nasal spray reduces sleep apnea severity in physiological responders: a randomized, blinded, trial.

Preclinical and human physiological studies indicate that topical, selective TASK 1/3 K+ channel antagonism increases upper airway dilator muscle activity and reduces pharyngeal collapsibility during anesthesia and nasal breathing during sleep. The primary aim of this study was to determine the effects of BAY2586116 nasal spray on obstructive sleep apnea (OSA) severity and whether individual responses vary according to differences in physiological responses and route of breathing. Ten people (5 females) with OSA [apnea-hypopnea index (AHI) = 47 ± 26 events/h (means ± SD)] who completed previous sleep physiology studies with BAY2586116 were invited to return for three polysomnography studies to quantify OSA severity. In random order, participants received either placebo nasal spray (saline), BAY2586116 nasal spray (160 µg), or BAY2586116 nasal spray (160 µg) restricted to nasal breathing (chinstrap or mouth tape). Physiological responders were defined a priori as those who had improved upper airway collapsibility (critical closing pressure ≥2 cmH2O) with BAY2586116 nasal spray (NCT04236440). There was no systematic change in apnea-hypopnea index (AHI3) from placebo versus BAY2586116 with either unrestricted or nasal-only breathing versus placebo (47 ± 26 vs. 43 ± 27 vs. 53 ± 33 events/h, P = 0.15). However, BAY2586116 (unrestricted breathing) reduced OSA severity in physiological responders compared with placebo (e.g., AHI3 = 28 ± 11 vs. 36 ± 12 events/h, P = 0.03 and ODI3 = 18 ± 10 vs. 28 ± 12 events/h, P = 0.02). Morning blood pressure was also lower in physiological responders after BAY2586116 versus placebo (e.g., systolic blood pressure = 137 ± 24 vs. 147 ± 21 mmHg, P < 0.01). In conclusion, BAY2586116 reduces OSA severity during sleep in people who demonstrate physiological improvement in upper airway collapsibility. These findings highlight the therapeutic potential of this novel pharmacotherapy target in selected individuals.NEW & NOTEWORTHY Preclinical findings in pigs and humans indicate that blocking potassium channels in the upper airway with topical nasal application increases pharyngeal dilator muscle activity and reduces upper airway collapsibility. In this study, BAY2586116 nasal spray (potassium channel blocker) reduced sleep apnea severity in those who had physiological improvement in upper airway collapsibility. BAY2586116 lowered the next morning's blood pressure. These findings highlight the potential for this novel therapeutic approach to improve sleep apnea in certain people.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data.

OBJECTIVES: Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range. METHODS: Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated. RESULTS: The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants. CONCLUSIONS: These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Associations of baseline obstructive sleep apnea and sleep macroarchitecture with cognitive function after 8 years in middle-aged and older men from a community-based cohort study.

Previous prospective studies examining associations of obstructive sleep apnea and sleep macroarchitecture with future cognitive function recruited older participants, many demonstrating baseline cognitive impairment. This study examined obstructive sleep apnea and sleep macroarchitecture predictors of visual attention, processing speed, and executive function after 8 years among younger community-dwelling men. Florey Adelaide Male Ageing Study participants (n = 477) underwent home-based polysomnography, with 157 completing Trail-Making Tests A and B and the Mini-Mental State Examination. Associations of obstructive sleep apnea (apnea-hypopnea index, oxygen desaturation index, and hypoxic burden index) and sleep macroarchitecture (sleep stage percentages and total sleep time) parameters with future cognitive function were examined using regression models adjusted for baseline demographic, biomedical, and behavioural factors, and cognitive task performance. The mean (standard deviation) age of the men at baseline was 58.9 (8.9) years, with severe obstructive sleep apnea (apnea-hypopnea index ≥30 events/h) in 9.6%. The median (interquartile range) follow-up was 8.3 (7.9-8.6) years. A minority of men (14.6%) were cognitively impaired at baseline (Mini-Mental State Examination score <28/30). A higher percentage of light sleep was associated with better Trail-Making Test A performance (B = -0.04, 95% confidence interval [CI] -0.06, -0.01; p = 0.003), whereas higher mean oxygen saturation was associated with worse performance (B = 0.11, 95% CI 0.02, 0.19; p = 0.012). While obstructive sleep apnea and sleep macroarchitecture might predict cognitive decline, future studies should consider arousal events and non-routine hypoxaemia measures, which may show associations with cognitive decline.

Unraveling the complexities of pathological voice through saliency analysis.

The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice pathology disorders can be effectively detected using Artificial Intelligence and computer-aided voice pathology classification tools. Previous studies focused primarily on binary classification, leaving limited attention to multi-class classification. This study proposes three different neural network architectures to investigate the feature characteristics of three voice pathologies-Hyperkinetic Dysphonia, Hypokinetic Dysphonia, Reflux Laryngitis, and healthy voices using multi-class classification and the Voice ICar fEDerico II (VOICED) dataset. The study proposes UNet++ autoencoder-based denoiser techniques for accurate feature extraction to overcome noisy data. The architectures include a Multi-Layer Perceptron (MLP) trained on structured feature sets, a Short-Time Fourier Transform (STFT) model, and a Mel-Frequency Cepstral Coefficients (MFCC) model. The MLP model on 143 features achieved 97.1% accuracy, while the STFT model showed similar performance with increased sensitivity of 99.8%. The MFCC model maintained 97.1% accuracy but with a smaller model size and improved accuracy on the Reflux Laryngitis class. The study identifies crucial features through saliency analysis and reveals that detecting voice abnormalities requires the identification of regions of inaudible high-pitch sounds. Additionally, the study highlights the challenges posed by limited and disjointed pathological voice databases and proposes solutions for enhancing the performance of voice abnormality classification. Overall, the study's findings have potential applications in clinical applications and specialized audio-capturing tools.

Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database.

This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography.

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension.

Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea-hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants (12% female) are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50-70% increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm.

Sleep Irregularity Is Associated With Hypertension: Findings From Over 2 Million Nights With a Large Global Population Sample.

BACKGROUND: Irregularities in sleep duration and sleep timing have emerged as potential risk factors for hypertension. This study examined associations between irregularity in sleep duration and timing with hypertension in a large, global sample over multiple months. METHODS: Data from 12 287 adults, who used an under-mattress device to monitor sleep duration and timing and also provided blood pressure recordings on ≥5 separate occasions, were analyzed. Sleep duration irregularity was assessed as the SD in total sleep time across the ≈9-month recording period. Sleep timing irregularity was assessed as SDs in sleep onset time, sleep midpoint, and sleep offset time. Logistic regressions were conducted to investigate associations between sleep irregularity and hypertension, defined as median systolic blood pressure ≥140 mm Hg or median diastolic blood pressure ≥90 mm Hg. RESULTS: Participants were middle-aged (mean±SD, 50±12 years), mostly men (88%) and overweight (body mass index, 28±6 kg/m-2). Sleep duration irregularity was consistently associated with an ≈9% to 17% increase in hypertension independently of the total sleep time. A ≈34-minute increase in sleep onset time irregularity was associated with a 32% increase in hypertension (1.32 [1.20-1.45]). A 32-minute increase in sleep midpoint irregularity was associated with an 18% increase in hypertension (1.18 [1.09-1.29]), while a 43-minute increase in sleep offset time irregularity was associated with an 8.9% increase in hypertension (1.09 [1.001-1.18]). CONCLUSIONS: These findings support that sleep irregularity, both in duration and timing, is a risk marker for poor cardiovascular health. Further mechanistic investigations of temporal relationships between day-to-day fluctuations in sleep duration and timing, next-day blood pressure, and other cardiovascular outcomes are warranted.

Methanol as a Potential Hydrogen Source for Reduction Reactions Enabled by a Commercial Pt/C Catalyst.

Catalytic reduction reactions using methanol as a transfer hydrogenating agent is gaining significant attention because this simple alcohol is inexpensive and produced on a bulk scale. Herein, we report the catalytic utilization of methanol as a hydrogen source for the reduction of different functional organic compounds such as nitroarenes, olefins, and carbonyl compounds. The key to the success of this transformation is the use of a commercially available Pt/C catalyst, which enabled the transfer hydrogenation of a series of simple and functionalized nitroarenes-to-anilines, alkenes-to-alkanes, and aldehydes-to-alcohols using methanol as both the solvent and hydrogen donor. The practicability of this Pt-based protocol is showcased by demonstrating catalyst recycling and reusability as well as reaction upscaling. In addition, the Pt/C catalytic system was also adaptable for the N-methylation and N-alkylation of anilines via the borrowing hydrogen process. This work provides a simple and flexible approach to prepare a variety of value-added products from readily available methanol, Pt/C, and other starting materials.

Seizure Detection: A Low Computational Effective Approach without Classification Methods.

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.

Lignin Residue-Derived Carbon-Supported Nanoscale Iron Catalyst for the Selective Hydrogenation of Nitroarenes and Aromatic Aldehydes.

Heterogeneous iron-based catalysts governing selectivity for the reduction of nitroarenes and aldehydes have received tremendous attention in the arena of catalysis, but relatively less success has been achieved. Herein, we report a green strategy for the facile synthesis of a lignin residue-derived carbon-supported magnetic iron (γ-Fe2O3/LRC-700) nanocatalyst. This active nanocatalyst exhibits excellent activity and selectivity for the hydrogenation of nitroarenes to anilines, including pharmaceuticals (e.g., flutamide and nimesulide). Challenging and reducible functionalities such as halogens (e.g., chloro, iodo, and fluoro) and ketone, ester, and amide groups were tolerated. Moreover, biomass-derived aldehyde (e.g., furfural) and other aromatic aldehydes were also effective for the hydrogenation process, often useful in biomedical sciences and other important areas. Before and after the reaction, the γ-Fe2O3/LRC-700 nanocatalyst was thoroughly characterized by X-ray diffraction (XRD), N2 adsorption-desorption, X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HR-TEM), Raman spectroscopy, and thermogravimetric analysis (TGA). Additionally, the γ-Fe2O3/LRC-700 nanocatalyst is stable and easily separated using an external magnet and recycled up to five cycles with no substantial drop in the activity. Eventually, sustainable and green credentials for the hydrogenation reactions of 4-nitrobenzamide to 4-aminobenzamide and benzaldehyde to benzyl alcohol were assessed with the help of the CHEM21 green metrics toolkit.

Single Channel Surface Electromyogram Deconvolution is a Useful Pre-Processing for Myoelectric Control.

OBJECTIVE: Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system. METHODS: We consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects. We have compared raw EMG and deconvolution signals, as sources of information for two specific classifiers (based on either Support Vector Machines or k-Nearest Neighbours), with classical time-domain input features selected using Mutual Component Analysis. RESULTS: Using the proposed pre-processing technique, classification performances statistically improve. For example, the true positive rates of the best-tested configurations were 80.9% and 86.3% when using the EMG and its deconvoluted signal, respectively. CONCLUSION: Even considering the limited dataset and range of classification approaches investigated, our preliminary results indicate the potential usefulness of the deconvolution pre-processing. SIGNIFICANCE: Deconvolution of EMG is a fast pre-processing that could be easily embedded in different myoelectric control applications.

Morpho-molecular diversity and avirulence genes distribution among the diverse isolates of Magnaporthe oryzae from Southern India.

AIMS: To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence and molecular diversity and relative distribution of five Avr genes. METHODS AND RESULTS: Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India. A majority of them (n = 28) formed a circular colony on culture media. Based on the disease reaction on susceptible cultivar (cv. HR-12), all 52 isolates were classified in to highly virulent (n = 28), moderately virulent (n = 11) and less-virulent (13) types. Among the 52 isolates, 38 were selected for deducing internal transcribed spacer (ITS) sequence diversity. For deducing phylogeny, another set of 36 isolates from other parts of the world was included, which yielded two distinct phylogenetic clusters. We identified eight haplotype groups and 91 variable sites within the ITS sequences, and haplotype-group-2 (Hap_2) was predominant (n = 24). The Tajima's and Fu's Fs neutrality tests exhibited many rare alleles. Furthermore, PCR analysis for detecting the presence of five Avr genes in the different M. oryzae isolates using Avr gene-specific primers in PCR revealed that Avr-Piz-t, Avr-Pik, Avr-Pia and Avr-Pita were present in 73.68%, 73.68%, 63.16% and 47.37% of the isolates studied, respectively; whereas, Avr-Pii was identified only in 13.16% of the isolates. CONCLUSIONS: Morpho-molecular and virulence studies revealed the significant diversity among eco-distinct isolates. PCR detection of Avr genes among the M. oryzae population revealed the presence of five Avr genes. Among them, Avr-Piz-t, Avr-Pik and Avr-Pia were more predominant. SIGNIFICANCE AND IMPACT OF THE STUDY: The study documented the morphological and genetic variability of eco-distinct M. oryzae isolates. This is the first study demonstrating the distribution of the Avr genes among the eco-distinct population of M. oryzae from southern India. The information generated will help plant breeders to select appropriate resistant gene/s combinations to develop blast disease-resistant rice cultivars.

Multinight Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea.

Rationale: Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night, which may have important implications for diagnosis and management. Objectives: This study aimed to assess OSA prevalence from multinight in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, nonrandomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods: A total of 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of approximately 170 nights per participant between July 2020 and March 2021. OSA was defined as a nightly mean apnea-hypopnea index (AHI) of more than 15 events/h. Outcomes were multinight global prevalence and likelihood of OSA misclassification from a single night's AHI value. Measurements and Main Results: More than 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% confidence interval, 20.9-24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between approximately 20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g., 1-night F1-score = 0.77 vs. 0.94 for 14 nights) and remained stable after 14 nights of monitoring. Conclusions: Multinight in-home monitoring using novel, noninvasive under-mattress sensor technology indicates a global prevalence of moderate to severe OSA of approximately 20%, and that approximately 20% of people diagnosed with a single-night study may be misclassified. These findings highlight the need to consider night-to-night variation in OSA diagnosis and management.

Wearable Bluetooth Triage Healthcare Monitoring System.

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients' vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient's comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences.

Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for "non-circadian rhythm" sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The "endpoint" of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.