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10 May 2024 Multimodal wearable swallowing monitor: automatic clinical assessment of swallowing dysfunction
Beomjune Shin, Sung Hoon Lee, Kangkyu Kwon, Yoon Jae Lee, Nikita Crispe, So-Young Ahn, Sandeep Shelly, Nathaniel Sundholm, Andrew Tkaczuk, Min-Kyung Yeo, Hyojung Choo, Woon-Hong Yeo
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Proceedings Volume PC12948, Soft Mechatronics and Wearable Systems; PC129480R (2024) https://doi.org/10.1117/12.3023709
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States
Abstract
Dysphagia, prevalent among Parkinson's and stroke patients, hinders proper eating, impacting their quality of life and potentially leading to fatal outcomes if untreated. Currently, Videofluoroscopic Swallowing Study (VFSS) is the gold standard for diagnosis but requires specialized facilities and trained staff. While many wearable devices have been developed to ease these burdens, none could reliably detect specific dysfunctions like silent aspiration without VFSS. We present a multimodal wearable swallowing monitor incorporating machine learning for automatic dysfunction assessment and silent aspiration diagnosis. The device, featuring a kirigami pattern, is directly mounted on the neck for continuous, high-fidelity monitoring of electromyograms and swallowing sounds. The built-in machine learning algorithm classifies various swallowing patterns, including silent aspiration. Clinical trials with stroke patients underscored the device's significance, matching the VFSS in detecting swallowing disorders. This wearable technology holds promise for advancing dysphagia healthcare and post-stroke rehabilitation therapy.
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Beomjune Shin, Sung Hoon Lee, Kangkyu Kwon, Yoon Jae Lee, Nikita Crispe, So-Young Ahn, Sandeep Shelly, Nathaniel Sundholm, Andrew Tkaczuk, Min-Kyung Yeo, Hyojung Choo, and Woon-Hong Yeo "Multimodal wearable swallowing monitor: automatic clinical assessment of swallowing dysfunction", Proc. SPIE PC12948, Soft Mechatronics and Wearable Systems, PC129480R (10 May 2024); https://doi.org/10.1117/12.3023709
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KEYWORDS
Parkinson disease
Education and training
Gold
Machine learning
Neck
Wearable devices
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