Introduction: Emergency department providers may be challenged by the presentation of patients who exhibit movement abnormalities that could indicate conditions requiring interventions with potential morbidity and mortality. Crucially, interventions for potential neurological diseases that require immediate intervention (e.g., stroke) are contraindicated in patients exhibiting movements that may be voluntary (e.g., emotional expressions and fabricated symptoms) or functional (e.g., functional movement disorders such as functional tremor or psychogenic nonepileptic seizures). This diagnostic challenge is amplified when evaluating patients who are unable to provide symptom history, so information provided by accompanying persons requires confirmation by corroborating sources. Since accurate and rapid classification of these movement subtypes is essential for proper diagnosis and treatment of patients, we hypothesize that technology incorporating the neural underpinnings of voluntary movements provides a foundation for differentiating subtypes of movements. This difference and these signatures may be quantifiable: Voluntary movements, reflecting complex and specific planning, demonstrate spatial and temporal precision and accuracy, motor smoothness, and longer preparation and execution time.
Methods: We propose that kinematic analysis using three-dimensional position data from three sensors on the thumb, little finger, and wrist can effectively capture these unique temporal and spatial characteristics, allowing differentiation in subtypes of voluntary movements. This protocol and device setup are being implemented to generate preliminary data.
Results: We propose that an algorithm for voluntary movements may appear as follows:
Voluntary movements = The presence of at least one of the following: + malingering + factitious disorder + ululation + applause + . . . .
Conclusion: Our protocol examines the relationship between the neural underpinnings of motor planning in quantifiable kinematic differences among voluntary movements. This distinction, captured by simple, multi-sensor analysis, may provide a foundation for objective and novel diagnostic aid in complex movement disorders and symptoms. This protocol may be a valuable tool to monitor patients participating in clinical trials.
