A Virtual Assistant to Help Dysphagia Patients Eat Safely at Home

For millions of people with swallowing disorders, preventing potentially deadly aspiration pneumonia requires following prescribed safe eating strategies. But adherence is poor, and caregivers’ ability to encourage adherence is limited by the onerous and socially aversive need to monitoring another’s eating. We have developed an early prototype for an intelligent assistant that monitors adherence and provides feedback to the patient, and tested monitoring precision with healthy subjects for one strategy called a “chin tuck.” Results indicate that adaptations of current generation machine vision and personal assistant technologies can effectively monitor chin tuck adherence, and suggest feasibility of a more general assistant that encourages adherence to a range of safe eating strategies. Dysphagia Patients Need Help With Adherence Dysphagia, or difficulty swallowing, is a widespread and often devastating disorder that affects 10–30% of the elderly population and high percentages of patients with neurological conditions such as stroke (50–75%) and Parkinson’s Disease (up to 95%). Dysphagia creates numerous risks; chief among them is aspiration pneumonia—an infection caused by accidental ingestion of bacteria-laden food into the lungs with mortality ranging from 10–70% (DeLegge, 2002). Clinicians frequently prescribe risk-reducing compensatory strategies such as tucking the chin to the chest before swallowing to protect the airway, and making an “effortful swallow” to clear residual food in the pharynx. These strategies have been shown to significantly improve patient health and well-being (Low et al., 2001). However, few patients can continuously self-monitor eating behavior (Shinn et al., 2013), leaving the task of policing adherence to caregivers. This creates significant relationship strain and caregiver burden. “It is poignant to note that we can assist people with dysphagia by first assisting their caregivers (Cichero and Altman, 2012).” Our preliminary results indicate the feasibility of shifting the burden of monitoring and encouraging adherence to a device that actively monitors patient-eating behavior and provides appropriate real-time feedback. Dysphagia patients are often advised to eat in front of a mirror to enhance self-awareness. We have constructed a proof-ofconcept prototype for a laptopor tablet-based application that, when positioned in front of the patient, functions as both a mirror and a source of feedback to assist use of safe compensatory strategies. The prototype successfully monitors compliance for a single eating strategy—specifically, whether the patient is compliant with flexing the head downward into a “chin tuck” after spooning food into the mouth. We believe that currently available machine-vision and machine-learning algorithms can be used to monitor adherence with the most commonly prescribed safe eating strategies, and that this monitoring can be done in real time by using widely available consumer electronics hardware. Our ultimate goal is to develop a fully functional, patientfriendly Dysphagia Coach application that increases patients’ eating safety and well-being while decreasing dysphagia-related mortality and caregiver burden. Figure 1. Dysphagia Coach prototype showing detection of user and feedback on a chin tuck maneuver. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)