Predictive sEMG-Driven Assistant for Enhanced Facial Therapy in Paralysis Patients

Abstract

Facial paralysis affects motor function, communication, emotional expression, and patient self-esteem, making rehabilitation a complex clinical challenge. This study presents the development of an intelligent therapeutic assistant for facial rehabilitation based on surface electromyography (sEMG). The system processes muscle biopotentials through filtering, normalization, and feature extraction to identify activation patterns that define optimal time windows for therapeutic interventions such as electrical stimulation or massage therapy. A mixed-method design was applied with patients presenting facial motor dysfunction, classified using the House-Brackmann (HB) scale, who participated in rehabilitation sessions supported by the system. A protocol for electrode placement and size was established to ensure signal quality and minimize contamination. Evaluation included algorithm performance metrics, muscle activation records, and clinical observations regarding detection accuracy and practical usefulness. Sessions were conducted in a controlled environment, considering patient comfort and variability in facial movements. Statistical analyses compared muscle activation detection before and after intervention and assessed the system’s capacity to support therapeutic decision-making. Preliminary results indicate that the assistant enhances precision and personalization in therapy, reduces therapist uncertainty, and improves intervention effectiveness. This research highlights the potential of sEMG-based intelligent systems in facial rehabilitation and their integration into clinical and telemedicine contexts. Future studies should expand sample size, extend intervention duration, incorporate additional physiological markers, and establish standardized protocols for broader clinical application.