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1. High-Resolution Cervical Auscultation and Data Science: New Tools to Address an Old Problem

   https://doi.org/10.1044/2020_AJSLP-19-00155  

   Coyle, James L.; Sejdić, Ervin (July 2020, American Journal of Speech-Language Pathology)

   null (Ed.)

   High-resolution cervical auscultation (HRCA) is an evolving clinical method for noninvasive screening of dysphagia that relies on data science, machine learning, and wearable sensors to investigate the characteristics of disordered swallowing function in people with dysphagia. HRCA has shown promising results in categorizing normal and disordered swallowing (i.e., screening) independent of human input, identifying a variety of swallowing physiological events as accurately as trained human judges. The system has been developed through a collaboration of data scientists, computer–electrical engineers, and speech-language pathologists. Its potential to automate dysphagia screening and contribute to evaluation lies in its noninvasive nature (wearable electronic sensors) and its growing ability to accurately replicate human judgments of swallowing data typically formed on the basis of videofluoroscopic imaging data. Potential contributions of HRCA when videofluoroscopic swallowing study may be unavailable, undesired, or not feasible for many patients in various settings are discussed, along with the development and capabilities of HRCA. The use of technological advances and wearable devices can extend the dysphagia clinician's reach and reinforce top-of-license practice for patients with swallowing disorders. 

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2. Iterative Patient Testing of a Stimuli-Responsive Swallowing Activity Sensor to Promote Extended User Engagement During the First Year After Radiation: Multiphase Remote and In-Person Observational Cohort Study

   https://doi.org/10.2196/47359  

   Shinn, Eileen H; Garden, Adam S; Peterson, Susan K; Leupi, Dylan J; Chen, Minxing; Blau, Rachel; Becerra, Laura; Rafeedi, Tarek; Ramirez, Julian; Rodriquez, Daniel; et al (January 2024, JMIR Cancer)

   BackgroundFrequent sensor-assisted monitoring of changes in swallowing function may help improve detection of radiation-associated dysphagia before it becomes permanent. While our group has prototyped an epidermal strain/surface electromyography sensor that can detect minute changes in swallowing muscle movement, it is unknown whether patients with head and neck cancer would be willing to wear such a device at home after radiation for several months. ObjectiveWe iteratively assessed patients’ design preferences and perceived barriers to long-term use of the prototype sensor. MethodsIn study 1 (questionnaire only), survivors of pharyngeal cancer who were 3-5 years post treatment and part of a larger prospective study were asked their design preferences for a hypothetical throat sensor and rated their willingness to use the sensor at home during the first year after radiation. In studies 2 and 3 (iterative user testing), patients with and survivors of head and neck cancer attending visits at MD Anderson’s Head and Neck Cancer Center were recruited for two rounds of on-throat testing with prototype sensors while completing a series of swallowing tasks. Afterward, participants were asked about their willingness to use the sensor during the first year post radiation. In study 2, patients also rated the sensor’s ease of use and comfort, whereas in study 3, preferences were elicited regarding haptic feedback. ResultsThe majority of respondents in study 1 (116/138, 84%) were willing to wear the sensor 9 months after radiation, and participant willingness rates were similar in studies 2 (10/14, 71.4%) and 3 (12/14, 85.7%). The most prevalent reasons for participants’ unwillingness to wear the sensor were 9 months being excessive, unwanted increase in responsibility, and feeling self-conscious. Across all three studies, the sensor’s ability to detect developing dysphagia increased willingness the most compared to its appearance and ability to increase adherence to preventive speech pathology exercises. Direct haptic signaling was also rated highly, especially to indicate correct sensor placement and swallowing exercise performance. ConclusionsPatients and survivors were receptive to the idea of wearing a personalized risk sensor for an extended period during the first year after radiation, although this may have been limited to well-educated non-Hispanic participants. A significant minority of patients expressed concern with various aspects of the sensor’s burden and its appearance. Trial RegistrationClinicalTrials.gov NCT03010150; https://clinicaltrials.gov/study/NCT03010150 

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3. Unobtrusive Swallow Monitoring Enabled by Conformal IONOGEL Biopotential Electrodes and Machine Learning

   https://doi.org/10.1002/admt.202500229  

   Dong, Penghao; Ives, Jasmine; Garcia, Ethan; Caporusso, Christopher; Bragina, Polina; Laguatan, Cassandra; Yao, Shanshan (May 2025, Advanced Materials Technologies)

   Abstract Dysphagia or difficulty swallowing is caused by the failure of neurological pathways to properly activate swallowing muscles. Current electromyography (EMG) systems for dysphagia monitoring are bulky and rigid, limiting their potential for long‐term and unobtrusive use. To address this, a machine learning‐assisted wearable EMG system is presented, utilizing self‐adhesive, skin‐conformal, semi‐transparent, and robust ionic gel electrodes. The presented electrodes possess good conductivity, superior skin contact, and good transmittance, ensuring high‐fidelity EMG sensing without impeding daily activities. Moreover, the optimized material and structural designs ensure wearing comfort and conformable skin‐electrode contact, allowing for long‐term monitoring with high accuracy. Machine learning and mel‐frequency cepstral coefficient techniques are employed to classify swallowing events based on food types and volumes. Through an analysis of electrode placement on the chin and neck, the proposed system is able to effectively distinguish between different food types and water volumes using a small number of channels, making it suitable for continuous dysphagia monitoring. This work represents an advancement in machine learning assisted EMG systems for the classification and regression of swallowing events, paving the way for more efficient, unobtrusive, and long‐term dysphagia monitoring systems. 

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4. Flexible submental sensor patch with remote monitoring controls for management of oropharyngeal swallowing disorders

   https://doi.org/10.1126/sciadv.aay3210  

   Kim, Min Ku; Kantarcigil, Cagla; Kim, Bongjoong; Baruah, Ratul Kumar; Maity, Shovan; Park, Yeonsoo; Kim, Kyunghun; Lee, Seungjun; Malandraki, Jaime Bauer; Avlani, Shitij; et al (December 2019, Science Advances)

   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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5. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics

   https://doi.org/10.3390/ijerph17103554  

   Chander, Harish; Burch, Reuben F.; Talegaonkar, Purva; Saucier, David; Luczak, Tony; Ball, John E.; Turner, Alana; Kodithuwakku Arachchige, Sachini N.; Carroll, Will; Smith, Brian K.; et al (May 2020, International Journal of Environmental Research and Public Health)

   Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed. 

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