Related Experiment Video
Updated: May 27, 2026
Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
Published on: March 14, 2017
Smartphone-Based Grading and Rehabilitation in Patients With Facial Palsy Using Computer Vision: Prospective
Franz-Tassilo Müller-Graff1, Fabian Essig2, Maximilian U Friedrich3
1Department of Oto-Rhino-Laryngology, Head and Neck Surgery, University Hospital Wuerzburg, Josef-Schneider-Strasse 11, Wuerzburg, 97080, Germany, 49 093120121485.
Background:
Peripheral facial palsy causes significant functional and psychosocial impairments, requiring precise assessment and patient engagement for effective rehabilitation. However, conventional clinician-graded scales (eg, House-Brackmann Scale, Sunnybrook Facial Grading System, and Stennert Index) are subjective and prone to interobserver variability, limiting their reliability for tracking recovery. Smartphone-based computer vision solutions offer objective, standardized facial movement grading, and interactive home-based training to improve adherence and outcomes.
Objective:
This pilot study evaluated a novel iOS smartphone app (Apple Inc.) for facial palsy management. The app uses the iPhone TrueDepth 3D camera and on-device computer vision to compute a Digital Facial Index (DFI) for objective facial movement analysis, and provides guided neuromuscular facial exercises with real-time biofeedback. The study aimed to validate DFI against standard clinical grading scales and assess patient-reported outcomes and usability.
Methods:
A 4-week single-arm pilot included 21 patients with unilateral facial palsy. Participants used the app at home for daily facial exercises and periodic self-assessments with DFI. Clinicians, blinded to DFI, rated facial function from standardized video exams at baseline and 4 weeks using the House-Brackmann Scale, the Sunnybrook Facial Grading System, and the Stennert Index. DFI concurrent validity was evaluated via correlation with these clinician scores. Patient-reported outcomes included pre- and postintervention Facial Disability Index (FDI) physical and social scores, the System Usability Scale, and a poststudy user feedback questionnaire.
Results:
During the study period, strong correlations were observed between DFI and conventional clinical scores. FDI physical and social showed significant functional improvement. Mean System Usability Scale was 88.3 (SD 15.4), indicating excellent usability, and participants reported high satisfaction, preferring the app over traditional paper-based exercises.
Conclusions:
The app's DFI provided objective facial function grading that correlated well with standard clinical scales. Patients' FDI scores improved significantly over 4 weeks. High usability and patient preference support the app's feasibility for home-based rehabilitation. This digital approach is promising for facial palsy management, and controlled studies are needed to confirm efficacy and improve long-term engagement.
