Our team uses accelerometers and machine learning to help detect vocal disorders. We capture data about the motions of patient's vocal folds to determine if their vocal behavior is normal or abnormal.

Our work focuses on using technology to help detect vocal misuse, patterns, and pathologies. In this project, we collected accelerometer data from a wearable device worn around the neck, developed by researchers at the MGH Voice Center. We created a learning algorithm that examines what vocal cord movements are prominent in subjects with disorders, using unsupervised learning, where data is unlabeled at the instance level. By analyzing more than 110 million “glottal pulses” (representing one opening and closing of the vocal folds) and comparing clusters of pulses, we detected significant differences between patients and controls.

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