Digital health technology for Parkinson's disease with comprehensive monitoring and artificial intelligence-enabled haptic biofeedback for bulbar dysfunction
Shuai Xu, Çagla Kantarcigil, Rabab Rangwala, Abigail B. Nellis, Keum San Chun, Dylan Richards, Ignacio Albert-Smet, Matthew W. Keller, Hope Chen, Le Huang, Shiv Patel, Albert F. Yang, Aejin Shon, Jacqueline Topping, Jessica Walter, Sarah Coughlin, Hyoyoung Jeong, Jong Yoon Lee, Bonnie Martin‐Harris
Abstract
BackgroundParkinson'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.ObjectiveWe 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.MethodsThe swallow detection algorithm developed with data from n = 58 healthy subjects yielded an F1 score of 0.89 for swallow event detection.ResultsIn 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.ConclusionsThis 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.