Swallowing is a complex neuromuscular activity regulated by the autonomic nervous system. Millions of adults suffer from dysphagia (impaired or difficulty swallowing), including patients with neurological disorders, head and neck cancer, gastrointestinal diseases, and respiratory disorders. Therapeutic treatments for dysphagia include interventions by speech-language pathologists designed to improve the physiology of the swallowing mechanism by training patients to initiate swallows with sufficient frequency and during the expiratory phase of the breathing cycle. These therapeutic treatments require bulky, expensive equipment to synchronously record swallows and respirations, confined to use in clinical settings. This paper introduces a wireless, wearable technology that enables continuous, mechanoacoustic tracking of respiratory activities and swallows through movements and vibratory processes monitored at the skin surface. Validation studies in healthy adults (n = 67) and patients with dysphagia (n = 4) establish measurement equivalency to existing clinical standard equipment. Additional studies using a differential mode of operation reveal similar performance even during routine daily activities and vigorous exercise. A graphical user interface with real-time data analytics and a separate, optional wireless module support both visual and haptic forms of feedback to facilitate the treatment of patients with dysphagia.

Background: Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder with broad manifestations of motor and non-motor symptoms. While significant progress has been made in assessing motor dysfunction through wearable sensors, less attention has been directed towards bulbar issues like swallowing difficulties. Objective: We introduce a digital health solution leveraging advanced acousto-mechanic (ADAM) sensors capable of comprehensively evaluating motor and bulbar dysfunction in PD that additionally offers artificial intelligence-driven haptic biofeedback to enhance swallowing frequency. Methods: The swallow detection algorithm developed with data from n = 58 healthy subjects yielded an F1 score of 0.89 for swallow event detection. Results: In a pilot study with PD patients (n = 20) experiencing mild (60%) or moderate (40%) dysphagia, the use of ADAM sensors with biofeedback significantly increased swallow frequency by 45%, from 0.77 to 1.10 swallows per minute (p < 0.0001). The sensors demonstrated high sensitivity (89%) and a strong correlation with visual observations by speech language pathologists (r = 0.92, p < 0.05), with 100% agreement on respiratory-swallow phase patterning. Feedback from patients and caregivers underscored the utility and comfort of the technology. Conclusions: This tailored digital health solution not only monitors PD symptoms but also holds potential as an assistive device, marking a significant step in improving the quality of life for PD patients.