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Many neuromuscular disorders impair function of cranial nerve enervated muscles. Clinical assessment of cranial muscle function has several limitations. Clinician rating of symptoms suffers from inter-rater variation, qualitative or semi-quantitative scoring, and limited ability to capture infrequent or fluctuating symptoms. Patient-reported outcomes are limited by recall bias and poor precision. Current tools to measure orofacial and oculomotor function are cumbersome, difficult to implement, and non-portable. Here, we show how Earable, a wearable device, can discriminate certain cranial muscle activities such as chewing, talking, and swallowing. We demonstrate using data from a pilot study of 10 healthy participants how Earable can be used to measure features from EMG, EEG, and EOG waveforms from subjects performing mock Performance Outcome Assessments (mock-PerfOs), utilized widely in clinical research. Our analysis pipeline provides a framework for how to computationally process and statistically rank features from the Earable device. Finally, we demonstrate that Earable data may be used to classify these activities. Our results, conducted in a pilot study of healthy participants, enable a more comprehensive strategy for the design, development, and analysis of wearable sensor data for investigating clinical populations. Additionally, the results from this study support further evaluation of Earable or similar devices as tools to objectively measure cranial muscle activity in the context of a clinical research setting. Future work will be conducted in clinical disease populations, with a focus on detecting disease signatures, as well as monitoring intra-subject treatment responses. Readily available quantitative metrics from wearable sensor devices like Earable support strategies for the development of novel digital endpoints, a hallmark goal of clinical research.