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HypomimiaCoach: An AU-based Digital Therapy System for Hypomimia Detection & Rehabilitation with Parkinson's Disease

arXiv 2410.09772 ↗First posted 13 Oct 2024cs.HC cs.AIcited by 0

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Abstract (arXiv)

Hypomimia is a non-motor symptom of Parkinson's disease that manifests as delayed facial movements and expressions, along with challenges in articulation and emotion. Currently, subjective evaluation by neurologists is the primary method for hypomimia detection, and conventional rehabilitation approaches heavily rely on verbal prompts from rehabilitation physicians. There remains a deficiency in accessible, user-friendly and scientifically rigorous assistive tools for hypomimia treatments. To investigate this, we developed HypomimaCoach, an Action Unit (AU)-based digital therapy system for hypomimia detection and rehabilitation in Parkinson's disease. The HypomimaCoach system was designed to facilitate engagement through the incorporation of both relaxed and controlled rehabilitation exercises, while also stimulating initiative through the integration of digital therapies that incorporated traditional face training methods. We extract action unit(AU) features and their relationship for hypomimia detection. In order to facilitate rehabilitation, a series of training programmes have been devised based on the Action Units (AUs) and patients are provided with real-time feedback through an additional AU recognition model, which guides them through their training routines. A pilot study was conducted with seven participants in China, all of whom exhibited symptoms of Parkinson's disease hypomimia. The results of the pilot study demonstrated a positive impact on participants' self-efficacy, with favourable feedback received. Furthermore, physician evaluations validated the system's applicability in a therapeutic setting for patients with Parkinson's disease, as well as its potential value in clinical applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 62 references that are in the map. First posted on arXiv 13 Oct 2024.

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References · 3

  1. 2019Semantic Relationships Guided Representation Learning for Facial Action Unit Recognitioncited by 53
  2. 2017Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsycited by 39
  3. 2019Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detectioncited by 25

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