Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography

Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements

PURPOSE

This review aims to highlight the pivotal role of the mandibular jaw movement (MJM) signal in advancing artificial intelligence (AI)-powered technologies for diagnosing obstructive sleep apnea (OSA).

METHODS

A scoping review was conducted to evaluate various aspects of the MJM signal and their contribution to improving signal proficiency for users.

RESULTS

The comprehensive literature analysis is structured into four key sections, each addressing factors essential to signal proficiency. These factors include (1) the comprehensiveness of research, development, and application of MJM-based technology; (2) the physiological significance of the MJM signal for various clinical tasks; (3) the technical transparency; and (4) the interpretability of the MJM signal. Comparisons with the photoplethysmography (PPG) signal are made where applicable.

CONCLUSIONS

Proficiency in biosignal interpretation is essential for the success of AI-driven diagnostic tools and for maximizing the clinical benefits through enhanced physiological insight. Through rigorous research ensuring an enhanced understanding of the signal and its extensive validation, the MJM signal sets a new benchmark for the development of AI-driven diagnostic solutions in OSA diagnosis.

Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics

Underpinned by rigorous clinical trial data, the use of existing home sleep apnoea testing is now commonly employed for sleep disordered breathing diagnostics in most clinical sleep centres globally. This has been a welcome addition for the field given the considerable burden of disease, cost, and access limitations with in-laboratory polysomnography testing. However, most existing home sleep apnoea testing approaches predominantly aim to replicate elements of conventional polysomnography in different forms with a focus on the estimation of the apnoea-hypopnoea index. New, simplified technology for sleep disordered breathing screening, detection/diagnosis, or monitoring has expanded exponentially in recent years. Emerging innovations in sleep monitoring technology now go beyond simple single-night replication of varying numbers of polysomnography signals in the home setting. These novel approaches have the potential to provide important new insights to overcome many of the existing limitations of sleep disordered breathing diagnostics and transform disease diagnosis and management to improve outcomes for patients. Accordingly, the current review summarises the existing evidence for sleep study testing in people with suspected sleep-related breathing disorders, discusses novel and emerging technologies and approaches according to three key categories: (1) wearables (e.g., body-worn sensors including wrist and finger sensors), (2) nearables (e.g., bed-embedded and bedside sensors), and (3) airables (e.g., audio and video recordings), and outlines their potential disruptive role to transform sleep disordered breathing diagnostics and care.

Assessment of Craniofacial Growth Pattern Relative to Respiratory Mandibular Movement and Sleep Characteristics: A Pilot Study

OBJECTIVES

 The primary objective was to evaluate the influence of sagittal skeletal pattern on mandibular movement (MM) during sleep in growing orthodontic populations. The secondary objective was to compare MM according to obstructive sleep apnea (OSA) status.

MATERIALS AND METHODS

 This cross-sectional study included subjects between 6 and 17 years old, presenting with class I, II, and III skeletal patterns and no previous history of orthodontic treatment. A wireless sensor connected to the patient's chin before bedtime and removed the next day was used to record MM signals. The signals were analyzed using a machine learning algorithm to measure sleep and MM outcomes. MM variables included percentage change in waveform prominence (%), variance in peak prominence, mean prominence values, length of events (seconds), respiratory rate per minute, dominant frequency, and amplitude of dominant frequency. The obstructive respiratory disturbance index determined from the sensor was used to confirm OSA status.

RESULTS

 There was no statistically significant difference in MM variables between class I, II, and III subjects. When compared according to OSA status, the amplitude of dominant frequency was significantly higher in the OSA than the non-OSA group (p = 0.005). When evaluated according to both skeletal classification and OSA status, the class I OSA subjects showed a higher median value than the non-OSA class I group (p = 0.016).

CONCLUSION

 Within the limits of this study, the sagittal skeletal pattern had no effect on the respiratory MM. This study did not find a correlation between craniofacial pattern and MM and OSA.

OSA diagnosis goes wearable: are the latest devices ready to shine?

STUDY OBJECTIVES

From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature.

METHODS

We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible.

RESULTS

In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized.

CONCLUSIONS

In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies.

CITATION

Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med. 2024;20(11):1823-1838.

Estimating the Severity of Obstructive Sleep Apnea Using ECG, Respiratory Effort and Neural Networks

OBJECTIVE

wearable sensor technology has progressed significantly in the last decade, but its clinical usability for the assessment of obstructive sleep apnea (OSA) is limited by the lack of large and representative datasets simultaneously acquired with polysomnography (PSG). The objective of this study was to explore the use of cardiorespiratory signals common in standard PSGs which can be easily measured with wearable sensors, to estimate the severity of OSA.

METHODS

an artificial neural network was developed for detecting sleep disordered breathing events using electrocardiogram (ECG) and respiratory effort. The network was combined with a previously developed cardiorespiratory sleep staging algorithm and evaluated in terms of sleep staging classification performance, apnea-hypopnea index (AHI) estimation, and OSA severity estimation against PSG on a cohort of 653 participants with a wide range of OSA severity.

RESULTS

four-class sleep staging achieved a κ of 0.69 versus PSG, distinguishing wake, combined N1-N2, N3 and REM. AHI estimation achieved an intraclass correlation coefficient of 0.91, and high diagnostic performance for different OSA severity thresholds.

CONCLUSIONS

this study highlights the potential of using cardiorespiratory signals to estimate OSA severity, even without the need for airflow or oxygen saturation (SpO2), traditionally used for assessing OSA.

SIGNIFICANCE

while further research is required to translate these findings to practical and unobtrusive sensors, this study demonstrates how existing, large datasets can serve as a foundation for wearable systems for OSA monitoring. Ultimately, this approach could enable long-term assessment of sleep disordered breathing, facilitating new avenues for clinical research in this field.

Mandibular movement monitor provides faster, yet accurate diagnosis for obstructive sleep apnoea: A randomised controlled study

Many patients with obstructive sleep apnoea (OSA) remain undiagnosed and thus untreated, and in part this relates to delay in diagnosis. Novel diagnostic strategies may improve access to diagnosis. In a multicentre, randomised study, we evaluated time to treatment decision in patients referred for suspected OSA, comparing a mandibular movement (MM) monitor to respiratory polygraphy, the most commonly used OSA detection method in the UK. Adults with high pre-test probability OSA were recruited from both northern Scotland and London. 40 participants (70 % male, mean±SD age 46.8 ± 12.9 years, BMI 36.9 ± 7.5 kg/m2, ESS 14.9 ± 4.1) wore a MM monitor and respiratory polygraphy simultaneously overnight and were randomised (1:1) to receive their treatment decision based on results from either device. Compared to respiratory polygraphy, MM monitor reduced time to treatment decision by 6 days (median(IQR): 13.5 (7.0-21.5) vs. 19.5 (13.7-35.5) days, P = 0.017) and saved an estimated 29 min of staff time per patient.

Analysis of mandibular jaw movements to assess ventilatory support management of children with obstructive sleep apnea syndrome treated with positive airway pressure therapies

BACKGROUND

The polysomnography (PSG) is the gold-standard for obstructive sleep apnea (OSA) syndrome diagnosis and assessment under positive airway pressure (PAP) therapies in children. Recently, an innovative digital medicine solution, including a mandibular jaw movement (MJM) sensor coupled with automated analysis, has been validated as an alternative to PSG for pediatric application.

OBJECTIVE

This study aimed to assess the reliability of MJM automated analysis for the assessment of residual apnea/hypopnea events during sleep in children with OSA treated with noninvasive ventilation (NIV) or continuous PAP (CPAP).

METHODS

In this open-label prospective non-randomized multicentric trial, we included children aged from 5 to 18 years with a diagnosis of severe OSA. The children underwent in-laboratory PSG with simultaneous MJM monitoring and at-home recording with MJM monitoring 3 months later. Agreement between PSG and MJM analysis in measuring the residual apnea-hypopnea index (AHI) was evaluated by the Bland-Altman method. The treatment effect on residual AHI was estimated for both PSG and MJM analysis.

RESULTS

Fifteen (60% males) children were included with a median age of 12 years [interquartile range 8-15]. Two (17%) were ventilated with NIV and 13 (83%) with CPAP. There was a good agreement between MJM-AHI and PSG-AHI with a median bias of -0.25 (95% CI: -3.40 to +2.04) events/h. The reduction in AHI under treatment was consistently significant across the three measurement methods: in-laboratory PSG and MJM recordings in the laboratory and at home.

CONCLUSION

Automated analysis of MJM is a highly reliable alternative method to assess residual events in a small population treated with PAP therapies.

The Future of Telemedicine for Obstructive Sleep Apnea Treatment: A Narrative Review

Obstructive sleep apnea is a common type of sleep-disordered breathing associated with multiple comorbidities. Nearly a billion people are estimated to have obstructive sleep apnea, which carries a substantial economic burden, but under-diagnosis is still a problem. Continuous positive airway pressure (CPAP) is the first-line treatment for OSAS. Telemedicine-based interventions (TM) have been evaluated to improve access to diagnosis, increase CPAP adherence, and contribute to easing the follow-up process, allowing healthcare facilities to provide patient-centered care. This narrative review summarizes the evidence available regarding the potential future of telemedicine in the management pathway of OSA. The potential of home sleep studies to improve OSA diagnosis and the importance of remote monitoring for tracking treatment adherence and failure and to contribute to developing patient engagement tools will be presented. Further studies are needed to explore the impact of shifting from teleconsultations to collaborative care models where patients are placed at the center of their care.

Mandibular Jaw Movement Automated Analysis for Oral Appliance Monitoring in Obstructive Sleep Apnea: A Prospective Cohort Study

Rationale: Oral appliances are second-line treatments after continuous positive airway pressure for obstructive sleep apnea (OSA) management. However, the need for oral appliance titration limits their use as a result of monitoring challenges to assess the treatment effect on OSA. Objectives: To assess the validity of mandibular jaw movement (MJM) automated analysis compared with polysomnography (PSG) and polygraphy (PG) in evaluating the effect of oral appliance treatment and the effectiveness of MJM monitoring for oral appliance titration at home in patients with OSA. Methods: This observational, prospective study included 135 patients with OSA eligible for oral appliance therapy. The primary outcome was the apnea-hypopnea index (AHI), measured through in-laboratory PSG/PG and MJM-based technology. Additionally, MJM monitoring at home was conducted at regular intervals during the titration process. The agreement between PSG/PG and MJM automated analysis was revaluated using Bland-Altman analysis. Changes in AHI during the home-based oral appliance titration process were evaluated using a generalized linear mixed model and a generalized estimating equation model. Results: The automated MJM analysis demonstrated strong agreement with PG in assessing AHI at the end of titration, with a median bias of 0.24/h (limits of agreement, -11.2 to 12.8/h). The improvement of AHI from baseline in response to oral appliance treatment was consistent across three evaluation conditions: in-laboratory PG (-59.6%; 95% confidence interval, -59.8% to -59.5%), in-laboratory automated MJM analysis (-59.2%; -65.2% to -52.2%), and at-home automated MJM analysis (-59.7%; -67.4% to -50.2%). Conclusions: Incorporating MJM automated analysis into the oral appliance titration process has the potential to optimize oral appliance therapy outcomes for OSA.

Clinical Phenotypes Supporting the Relationship Between Sleep Disturbance and Impairment of Placebo Effects

Lack of good sleep or insomnia can lead to many health issues, including an elevated risk of cardiovascular disease, obesity, fatigue, low mood, and pain. While chronic pain negatively impacts sleep quality, the relationship between descending pain modulatory systems like placebo effects and sleep quality is not thoroughly known. We addressed this aspect in a cross-sectional study in participants with chronic pain. Placebo effects were elicited in a laboratory setting using thermal heat stimulations delivered with visual cues using classical conditioning and verbal suggestions. We estimated the levels of insomnia severity with the Insomnia Severity Index and the sleep quality with the Pittsburg Sleep Quality Index. The previous night's sleep continuity was assessed as total sleep time, sleep efficiency, and sleep midpoint the night before the experiment. 277 people with chronic pain and 189 pain-free control individuals participated. Participants with chronic pain and insomnia showed smaller placebo effects than those with chronic pain without insomnia. Similarly, poor sleep quality was associated with reduced placebo effects among participants with chronic pain. Clinical anxiety measured by Depression Anxiety Stress Scales partially mediated these effects. In contrast, placebo effects were not influenced by the presence of insomnia or poor sleep quality in pain-free participants. Sleep continuity the night before the experiment did not influence the placebo effects. Our results indicate that participants who experience insomnia and/or poor sleep quality and chronic pain have smaller placebo effects, and that the previous night sleep continuity does not influence the magnitude of placebo effects. PERSPECTIVE: This study examined the relationship between sleep disturbances and experimentally induced placebo effects. We found that individuals with chronic pain who experience insomnia and poor sleep quality demonstrated reduced placebo effects compared to their counterparts with good sleep quality and no insomnia.

Validation of a Wearable Medical Device for Automatic Diagnosis of OSA against Standard PSG

STUDY OBJECTIVE

The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis.

MATERIAL AND METHODS

This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022.

RESULTS

A total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07.

CONCLUSIONS

The AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.

Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea

A strong and specific comprehensive physiological association has been documented between mandibular jaw movements and related periods of normal or disturbed breathing across different sleep stages. The mandibular jaw movement biosignal can be incorporated in the polysomnography, displayed on the screen as a function of time like any standard polysomnography signal (eg, airflow, oxygen saturation, respiratory inductance plethysmography bands) and interpreted in the context of the target period of breathing and its associated respiratory effort level. Overall, the mandibular jaw movement biosignal that depicts the muscular trigeminal respiratory drive is a highly effective tool for differentiating between central and obstructive sleep episodes including hypopneas and for providing clinicians with valuable insights into wake/sleep states, arousals, and sleep stages. These fundamental characteristics of the mandibular jaw movement biosignal contrast with photoplethysmography, airflow, or oxygen saturation signals that provide information more about the consequence of the disturbed breathing episode than about the event itself.

CITATION

Malhotra A, Martinot J-B, Pépin J-L. Insights on mandibular jaw movements during polysomnography in obstructive sleep apnea. J Clin Sleep Med. 2024;20(1):151-163.

Artificial intelligence in sleep medicine: Present and future

Artificial intelligence (AI) has impacted many areas of healthcare. AI in healthcare uses machine learning, deep learning, and natural language processing to analyze copious amounts of healthcare data and yield valuable outcomes. In the sleep medicine field, a large amount of physiological data is gathered compared to other branches of medicine. This field is primed for innovations with the help of AI. A good quality of sleep is crucial for optimal health. About one billion people are estimated to have obstructive sleep apnea worldwide, but it is difficult to diagnose and treat all the people with limited resources. Sleep apnea is one of the major contributors to poor health. Most of the sleep apnea patients remain undiagnosed. Those diagnosed with sleep apnea have difficulty getting it optimally treated due to several factors, and AI can help in this situation. AI can also help in the diagnosis and management of other sleep disorders such as insomnia, hypersomnia, parasomnia, narcolepsy, shift work sleep disorders, periodic leg movement disorders, etc. In this manuscript, we aim to address three critical issues about the use of AI in sleep medicine: (1) How can AI help in diagnosing and treating sleep disorders? (2) How can AI fill the gap in the care of sleep disorders? and (3) What are the ethical and legal considerations of using AI in sleep medicine?

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

Determinants of apnea-hypopnea index variability during home sleep testing

BACKGROUND

A single-night attended in-laboratory polysomnography or home sleep testing are common approaches for obstructive sleep apnea (OSA) diagnosis. However, internight variability in apnea-hypopnea index value is common, and may result in misclassification of OSA severity and inapropriate treatment decisions.

OBJECTIVE

To investigate factors determining short-term apnea-hypopnea index variability using multi-night automated home sleep testing, and to determine how this variability impacts clinical decisions.

PATIENTS/METHODS

Adults with suspected OSA who successfully performed three home sleep tests using measurements of mandibular jaw movements (Sunrise, Namur, Belgium) combined with automated machine learning analysis were enrolled. Data analysis included principal component analysis, generalized estimating equation regression and qualitative agreement analysis.

RESULTS

160 individuals who performed three sleep tests over a mean of 8.78 ± 8.48 days were included. The apnea-hypopnea index varied by -0.88 events/h (5th-95th percentile range: -14.33 to 9.72 events/h). Based on a single-night recording, rates of overtreatment and undertreatment would have been of 13.5% and 6.0%, respectively. Regression analysis adjusted for age, sex, body mass index, total sleep time, and time between home sleep tests showed that time spent in deep non-rapid eye movement sleep and with head in supine position were independent significant predictors of the apnea-hypopnea index variability.

CONCLUSIONS

At the individual level, short-term internight variability in the apnea-hypopnea index was significantly associated with time spent in deep non-rapid eye movement sleep and head in supine position. Clinical decisions based on a single-night testing may lead to errors in OSA severity classification and incorrect therapeutic decisions.

Perspective: A resident's role in promoting safe machine-learning tools in sleep medicine

Residents and fellows can play a helpful role in promoting safe and effective machine-learning tools in sleep medicine. Here we highlight the importance of establishing ground truths, considering key variables, and prioritizing transparency and accountability in the development of machine-learning tools within the field of artificial intelligence. Through understanding, communication, and collaboration, in-training physicians have a meaningful opportunity to help progress the field toward safe machine-learning tools in sleep medicine.

CITATION

Smith CM, Vendrame M. Perspective: a resident's role in promoting safe machine-learning tools in sleep medicine. J Clin Sleep Med. 2023;19(11):1985-1987.

Creating an Optimal Approach for Diagnosing Sleep Apnea

Sleep apnea is nowadays recognized as a treatable chronic disease and awareness of it has increased, leading to an upsurge in demand for diagnostic testing. Conventionally, diagnosis depends on overnight polysomnography in a sleep clinic, which is highly human-resource intensive and ignores the night-to-night variability in classical sleep apnea markers, such as the apnea-hypopnea index. In this review, the authors summarize the main improvements that could be made in the sleep apnea diagnosis strategy; how technological innovations and multi-night home testing could be used to simplify, increase access, and reduce costs of diagnostic testing while avoiding misclassification of severity.

Automatic scoring of drug-induced sleep endoscopy for obstructive sleep apnea using deep learning

BACKGROUND

Treatment of obstructive sleep apnea is crucial for long term health and reduced economic burden. For those considered for surgery, drug-induced sleep endoscopy (DISE) is a method to characterize location and pattern of sleep-related upper airway collapse. According to the VOTE classification system, four upper airway sites of collapse are characterized: velum (V), oropharynx (O), tongue (T), and epiglottis (E). The degree of obstruction per site is classified as 0 (no obstruction), 1 (partial obstruction), or 2 (complete obstruction). Here we propose a deep learning approach for automatic scoring of VOTE obstruction degrees from DISE videos.

METHODS

We included 281 DISE videos with varying durations (6 s-16 min) from two sleep clinics: Copenhagen University Hospital and Stanford University Hospital. Examinations were split into 5-s clips, each receiving annotations of 0, 1, 2, or X (site not visible) for each site (V, O, T, and E), which was used to train a deep learning model. Predicted VOTE obstruction degrees per examination was obtained by taking the highest predicted degree per site across 5-s clips, which was evaluated against VOTE degrees annotated by surgeons.

RESULTS

Mean F1 score of 70% was obtained across all DISE examinations (V: 85%, O: 72%, T: 57%, E: 65%). For each site, sensitivity was highest for degree 2 and lowest for degree 0. No bias in performance was observed between videos from different clinicians/hospitals.

CONCLUSIONS

This study demonstrates that automating scoring of DISE examinations show high validity and feasibility in degree of upper airway collapse.

Wearable Orofacial Technology and Orthodontics

Wearable technology to augment traditional approaches are increasingly being added to the arsenals of treatment providers. Wearable technology generally refers to electronic systems, devices, or sensors that are usually worn on or are in close proximity to the human body. Wearables may be stand-alone or integrated into materials that are worn on the body. What sets medical wearables apart from other systems is their ability to collect, store, and relay information regarding an individual's current body status to other devices operating on compatible networks in naturalistic settings. The last decade has witnessed a steady increase in the use of wearables specific to the orofacial region. Applications range from supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and better understanding the jaw's functional and parafunctional activities. Orofacial wearable devices may be unimodal or incorporate multiple sensing modalities. The objective data collected continuously, in real time, in naturalistic settings using these orofacial wearables provide opportunities to formulate accurate and personalized treatment strategies. In the not-too-distant future, it is anticipated that information about an individual's current oral health status may provide patient-centric personalized care to prevent, diagnose, and treat oral diseases, with wearables playing a key role. In this review, we examine the progress achieved, summarize applications of orthodontic relevance and examine the future potential of orofacial wearables.