Abstract: A method can classify cervical accelerometry data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method can include receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from an accelerometer operatively coupled to a processing module that is a local or remote computing device. The method can include extracting one or more specific meta-features from the data and then outputting from the processing module a classification of the swallowing event based on the extracted meta-features, for example a first classification indicative of normal swallowing or a second classification indicative of possibly impaired swallowing.

Abstract: A method classifies vibrational data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method includes receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from a sensor operatively coupled to a processing module that is a local or remote computing device. A portion of the axis-specific vibrational data for the A-P axis can be combined with a portion of the axis-specific vibrational data for the S-I axis on the processing module using one or more of linear combination, squared (power) sum, moving window correlation of the two signals, local minimum or local maximum of the two signals, and trigonometric relation. The method can include outputting from the processing module a classification of the swallowing event based on the combined vibrational data.

Abstract: A method classifies vibrational data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method includes receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from a sensor operatively coupled to a processing module that is a local or remote computing device. A portion of the axis-specific vibrational data for the A-P axis can be combined with a portion of the axis-specific vibrational data for the S-I axis on the processing module using one or more of linear combination, squared (power) sum, moving window correlation of the two signals, local minimum or local maximum of the two signals, and trigonometric relation. The method can include outputting from the processing module a classification of the swallowing event based on the combined vibrational data.

Abstract: A method can classify cervical accelerometry data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method can include receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from an accelerometer operatively coupled to a processing module that is a local or remote computing device. At least a portion of the axis-specific vibrational data for the A-P axis can be combined with at least a portion of the axis-specific vibrational data for the S-I axis on the processing module using one or more of linear combination, squared (power) sum, moving window correlation of the two signals, local minimum or local maximum of the two signals, and trigonometric relation.

Abstract: A method can classify cervical accelerometry data acquired for a swallowing event to identify a possible swallowing impairment in a candidate. The method can include receiving axis-specific vibrational data for an anterior-posterior (A-P) axis and a superior-inferior (S-I) axis and representative of the swallowing event, for example from an accelerometer operatively coupled to a processing module that is a local or remote computing device. The method can include extracting one or more specific meta-features from the data and then outputting from the processing module a classification of the swallowing event based on the extracted meta-features, for example a first classification indicative of normal swallowing or a second classification indicative of possibly impaired swallowing.