Abstract: A method can use dual-axis accelerometry signals obtained during a swallow to classify the swallow as a normal swallow or as an impaired swallow (e.g., an aspiration-penetration). The method can include representing the dual-axis accelerometry signals as meta-features, comparing the salient time and frequency meta-features, identified by regularized binomial logistic regression with elastic net penalty performed on the time and frequency meta-features in a known training data set, with a preset linear discriminant classifier constructed based on the salient meta-features, and classifying the swallow as a normal swallow or a possibly impaired swallow, based on the comparing. Preferably a processing module operatively connected to the sensor performs the processing of the dual-axis accelerometry signals and also automatically classifies the swallow.