A Wearable Swallowing Recognition System Based on Motion and Dual Photoplethysmography Sensing of Laryngeal Movements

Swallowing recognition is the leading step in the evaluation of dysphagia which seriously affects people's life. Current medical swallowing monitoring methods require an in-hospital environment and overly rely on professional knowledge of the medical staff. In this study, we developed a wearable swallowing recognition system that consists of an on-neck wearable swallowing sensing device and a data processing module on a host computer. The wearable device collects inertial signals including acceleration and angular velocity, as well as dual photoplethysmography (PPG) signals based on infrared and green light from the neck. A novel processing framework for dual PPG signals is proposed to extract and enhance the laryngeal motion component introduced by swallowing activities in the data processing module. The laryngeal motion component of dual PPG signals together with the preprocessed inertial signals are further used for feature extraction to proceed swallowing recognition based on random forest classifier. We collected data from 32 healthy subjects in the center and side positions on the neck using our system to analyze their swallowing activities. As a result, we achieved a high average area under curve (AUC) of the swallowing recognition by 86.6%. We also find the sensing position has a significant impact on gender-specific swallowing recognition performance, as the center position was better for females (92.9%), while the side position was better for males (87.6%). The results indicate that the proposed system could achieve high integrity and good performance, which is helpful for the future swallowing research.