<b><i>Introduction:</i></b> Kinematic analyses have recently revealed a strong potential to contribute to the assessment of neurological diseases. However, the validation of home-based kinematic assessments using consumer-grade video technology has yet to be performed. In line with best practices for digital biomarker development, we sought to validate webcam-based kinematic assessment against established, laboratory-based recording gold standards. We hypothesized that webcam-based kinematics would possess psychometric properties comparable to those obtained using the laboratory-based gold standards. <b><i>Methods:</i></b> We collected data from 21 healthy participants who repeated the phrase “buy Bobby a puppy” (BBP) at four different combinations of speaking rate and volume: Slow, Normal, Loud, and Fast. We recorded these samples twice back-to-back, simultaneously using (1) an electromagnetic articulography (“EMA”; NDI Wave) system, (2) a 3D camera (Intel RealSense), and (3) a 2D webcam for video recording via an in-house developed app. We focused on the extraction of kinematic features in this study, given their demonstrated value in detecting neurological impairments. We specifically extracted measures of speed/acceleration, range of motion (ROM), variability, and symmetry using the movements of the center of the lower lip during these tasks. Using these kinematic features, we derived measures of (1) agreement between recording methods, (2) test-retest reliability of each method, and (3) the validity of webcam recordings to capture expected changes in kinematics as a result of different speech conditions. <b><i>Results:</i></b> Kinematics measured using the webcam demonstrated good agreement with both the RealSense and EMA (ICC-A values often ≥0.70). Test-retest reliability, measured using the absolute agreement (2,1) formulation of the intraclass correlation coefficient (i.e., ICC-A), was often “moderate” to “strong” (i.e., ≥0.70) and similar between the webcam and EMA-based kinematic features. Finally, the webcam kinematics were typically as sensitive to differences in speech tasks as EMA and the 3D camera gold standards. <b><i>Discussion and Conclusions:</i></b> Our results suggested that webcam recordings display good psychometric properties, comparable to laboratory-based gold standards. This work paves the way for a large-scale clinical validation to continue the development of these promising technologies for the assessment of neurological diseases via home-based methods.
Purpose: This study sought to determine whether clinically interpretable kinematic features extracted automatically from three-dimensional (3D) videos were correlated with corresponding perceptual clinical orofacial ratings in individuals with orofacial impairments due to neurological disorders. Method: 45 participants (19 diagnosed with motor neuron diseases [MNDs] and 26 poststroke) performed two nonspeech tasks (mouth opening and lip spreading) and one speech task (repetition of a sentence “Buy Bobby a Puppy”) while being video-recorded in a standardized lab setting. The color video recordings of participants were assessed by an expert clinician—a speech language pathologist—on the severity of three orofacial measures: symmetry, range of motion (ROM), and speed. Clinically interpretable 3D kinematic features, linked to symmetry, ROM, and speed, were automatically extracted from video recordings, using a deep facial landmark detection and tracking algorithm for each of the three tasks. Spearman correlations were used to identify features that were significantly correlated ( p value < .05) with their corresponding clinical scores. Clinically significant kinematic features were then used in the subsequent multivariate regression models to predict the overall orofacial impairment severity score. Results: Several kinematic features extracted from 3D video recordings were associated with their corresponding perceptual clinical scores, indicating clinical validity of these automatically derived measures. Different patterns of significant features were observed between MND and poststroke groups; these differences were aligned with clinical expectations in both cases. Conclusions: The results show that kinematic features extracted automatically from simple clinical tasks can capture characteristics used by clinicians during assessments. These findings support the clinical validity of video-based automatic extraction of kinematic features.
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