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Many neuromuscular disorders impair function of cranial nerve enervatedmuscles. Clinical assessment of cranial muscle function has severallimitations. Clinician rating of symptoms suffers from inter-rater variation,qualitative or semi-quantitative scoring, and limited ability to captureinfrequent or fluctuating symptoms. Patient-reported outcomes are limited byrecall bias and poor precision. Current tools to measure orofacial andoculomotor function are cumbersome, difficult to implement, and non-portable.Here, we show how Earable, a wearable device, can discriminate certain cranialmuscle activities such as chewing, talking, and swallowing. We demonstrateusing data from a pilot study of 10 healthy participants how Earable can beused to measure features from EMG, EEG, and EOG waveforms from subjectsperforming mock Performance Outcome Assessments (mock-PerfOs), utilized widelyin clinical research. Our analysis pipeline provides a framework for how tocomputationally process and statistically rank features from the Earabledevice. Finally, we demonstrate that Earable data may be used to classify theseactivities. Our results, conducted in a pilot study of healthy participants,enable a more comprehensive strategy for the design, development, and analysisof wearable sensor data for investigating clinical populations. Additionally,the results from this study support further evaluation of Earable or similardevices as tools to objectively measure cranial muscle activity in the contextof a clinical research setting. Future work will be conducted in clinicaldisease populations, with a focus on detecting disease signatures, as well asmonitoring intra-subject treatment responses. Readily available quantitativemetrics from wearable sensor devices like Earable support strategies for thedevelopment of novel digital endpoints, a hallmark goal of clinical research.

A pilot study of the Earable device to measure facial muscle and eye movement tasks among healthy volunteers