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.
Athletes frequently face risks like dehydration, fatigue, and heart issues due to high-intensity performances. Although there have been advancements in sports science training, a wearable system that can monitor multiple health parameters is crucial to prevent these conditions. Existing devices, often bulky and limited to single-parameter monitoring like heart rate, sweat, or skin hydration, focus mainly on performance. We present a multi-sensor wearable system incorporating a microfabricated, ultra-thin, flexible sensor. This system, consisting of mouthguards and chest patches, continuously monitors saliva osmolality, skin temperature, and cardiac function. It offers an athlete's hydration level and physiological stress in intense perspiration and heat conditions. The system's effectiveness in tracking physiological changes was proven in field tests, capturing significant increases in dehydration and physical strain during hour-long training sessions. This demonstration showcases the system's ability to detect rapid physiological changes, offering crucial data for mitigating athletic risks and aiding clinical decisions, ultimately improving medical care in sports.
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