Epidermal sensors for remote healthcare and performance monitoring require the ability to operate under the effects of bodily motion, heat, and perspiration. Here, the use of purpose‐synthesized polymer‐based dry electrodes and graphene‐based strain gauges to obtain measurements of swallowed volume under typical conditions of exercise is evaluated. The electrodes, composed of the common conductive polymer poly(3,4 ethylenedioxythiophene) (PEDOT) electrostatically bound to poly(styrenesulfonate)‐b‐poly(poly(ethylene glycol) methyl ether acrylate) (PSS‐b‐PPEGMEA), collect surface electromyography (sEMG) signals on the submental muscle group, under the chin. Simultaneously, the deformation of the surface of the skin is measured using strain gauges comprising single‐layer graphene supporting subcontinuous coverage of gold and a highly plasticized composite containing PEDOT:PSS. Together, these materials permit high stretchability, high resolution, and resistance to sweat. A custom printed circuit board (PCB) allows this multicomponent system to acquire strain and sEMG data wirelessly. This sensor platform is tested on the swallowing activity of a cohort of 10 subjects while walking or cycling on a stationary bike. Using a machine learning (ML) model, it is possible to predict swallowed volume with absolute errors of 36% for walking and 43% for cycling.
Flexible submental sensor patch with remote monitoring controls for management of oropharyngeal swallowing disorders
Successful rehabilitation of oropharyngeal swallowing disorders (i.e., dysphagia) requires frequent performance of head/neck exercises that primarily rely on expensive biofeedback devices, often only available in large medical centers. This directly affects treatment compliance and outcomes, and highlights the need to develop a portable and inexpensive remote monitoring system for the telerehabilitation of dysphagia. Here, we present the development and preliminarily validation of a skin-mountable sensor patch that can fit on the curvature of the submental (under the chin) area noninvasively and provide simultaneous remote monitoring of muscle activity and laryngeal movement during swallowing tasks and maneuvers. This sensor patch incorporates an optimal design that allows for the accurate recording of submental muscle activity during swallowing and is characterized by ease of use, accessibility, reusability, and cost-effectiveness. Preliminary studies on a patient with Parkinson’s disease and dysphagia, and on a healthy control participant demonstrate the feasibility and effectiveness of this system.
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- Award ID(s):
- 1657455
- NSF-PAR ID:
- 10147486
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 5
- Issue:
- 12
- ISSN:
- 2375-2548
- Page Range / eLocation ID:
- eaay3210
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
null (Ed.)Purpose Safe swallowing requires adequate protection of the airway to prevent swallowed materials from entering the trachea or lungs (i.e., aspiration). Laryngeal vestibule closure (LVC) is the first line of defense against swallowed materials entering the airway. Absent LVC or mistimed/shortened closure duration can lead to aspiration, adverse medical consequences, and even death. LVC mechanisms can be judged commonly through the videofluoroscopic swallowing study; however, this type of instrumentation exposes patients to radiation and is not available or acceptable to all patients. There is growing interest in noninvasive methods to assess/monitor swallow physiology. In this study, we hypothesized that our noninvasive sensor-based system, which has been shown to accurately track hyoid displacement and upper esophageal sphincter opening duration during swallowing, could predict laryngeal vestibule status, including the onset of LVC and the onset of laryngeal vestibule reopening, in real time and estimate the closure duration with a comparable degree of accuracy as trained human raters. Method The sensor-based system used in this study is high-resolution cervical auscultation (HRCA). Advanced machine learning techniques enable HRCA signal analysis through feature extraction and complex algorithms. A deep learning model was developed with a data set of 588 swallows from 120 patients with suspected dysphagia and further tested on 45 swallows from 16 healthy participants. Results The new technique achieved an overall mean accuracy of 74.90% and 75.48% for the two data sets, respectively, in distinguishing LVC status. Closure duration ratios between automated and gold-standard human judgment of LVC duration were 1.13 for the patient data set and 0.93 for the healthy participant data set. Conclusions This study found that HRCA signal analysis using advanced machine learning techniques can effectively predict laryngeal vestibule status (closure or opening) and further estimate LVC duration. HRCA is potentially a noninvasive tool to estimate LVC duration for diagnostic and biofeedback purposes without X-ray imaging.more » « less
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Abstract Tongue function is vital for chewing and swallowing and lingual dysfunction is often associated with dysphagia. Better treatment of dysphagia depends on a better understanding of hyolingual morphology, biomechanics, and neural control in humans and animal models. Recent research has revealed significant variation among animal models in morphology of the hyoid chain and suprahyoid muscles which may be associated with variation in swallowing mechanisms. The recent deployment of XROMM (X-ray Reconstruction of Moving Morphology) to quantify 3D hyolingual kinematics has revealed new details on flexion and roll of the tongue during chewing in animal models, movements similar to those used by humans. XROMM-based studies of swallowing in macaques have falsified traditional hypotheses of mechanisms of tongue base retraction during swallowing, and literature review suggests that other animal models may employ a diversity of mechanisms of tongue base retraction. There is variation among animal models in distribution of hyolingual proprioceptors but how that might be related to lingual mechanics is unknown. In macaque monkeys, tongue kinematics—shape and movement—are strongly encoded in neural activity in orofacial primary motor cortex, giving optimism for development of brain–machine interfaces for assisting recovery of lingual function after stroke. However, more research on hyolingual biomechanics and control is needed for technologies interfacing the nervous system with the hyolingual apparatus to become a reality.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.aay3210
