Bandini Andrea, Orlandi Silvia, Escalante Hugo Jair, Giovannelli Fabio, Cincotta Massimo, Reyes-Garcia Carlos A, Vanni Paola, Zaccara Gaetano, Manfredi Claudia
Department of Information Engineering, Università degli Studi di Firenze, Via di S. Marta 3, 50139 Firenze, Italy; Department of Electrical, Electronic and Information Engineering (DEI) "Guglielmo Marconi", Università di Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy.
Department of Information Engineering, Università degli Studi di Firenze, Via di S. Marta 3, 50139 Firenze, Italy.
J Neurosci Methods. 2017 Apr 1;281:7-20. doi: 10.1016/j.jneumeth.2017.02.006. Epub 2017 Feb 20.
The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is the study of facial bradykinesia in Parkinson's disease (PD), which is a major motor sign of this neurodegenerative illness. Facial bradykinesia consists in the reduction/loss of facial movements and emotional facial expressions called hypomimia.
In this work we propose an automatic method for studying facial expressions in PD patients relying on video-based METHODS: 17 Parkinsonian patients and 17 healthy control subjects were asked to show basic facial expressions, upon request of the clinician and after the imitation of a visual cue on a screen. Through an existing face tracker, the Euclidean distance of the facial model from a neutral baseline was computed in order to quantify the changes in facial expressivity during the tasks. Moreover, an automatic facial expressions recognition algorithm was trained in order to study how PD expressions differed from the standard expressions.
Results show that control subjects reported on average higher distances than PD patients along the tasks.
This confirms that control subjects show larger movements during both posed and imitated facial expressions. Moreover, our results demonstrate that anger and disgust are the two most impaired expressions in PD patients.
Contactless video-based systems can be important techniques for analyzing facial expressions also in rehabilitation, in particular speech therapy, where patients could get a definite advantage from a real-time feedback about the proper facial expressions/movements to perform.
面部表情的自动分析是一个不断发展的领域，有多种临床应用。其中一个应用是帕金森病（PD）中面部运动迟缓的研究，这是这种神经退行性疾病的一个主要运动症状。面部运动迟缓表现为面部运动和被称为面无表情的情感面部表情减少/丧失。
在这项工作中，我们提出了一种基于视频的自动方法来研究帕金森病患者的面部表情。方法：应临床医生要求并在模仿屏幕上的视觉提示后，17名帕金森病患者和17名健康对照受试者被要求展示基本面部表情。通过现有的面部跟踪器，计算面部模型与中性基线的欧几里得距离，以量化任务期间面部表现力的变化。此外，训练了一种自动面部表情识别算法，以研究帕金森病患者的表情与标准表情有何不同。
结果表明，在整个任务过程中，对照受试者的平均距离报告值高于帕金森病患者。
这证实了对照受试者在摆姿势和模仿面部表情时都表现出更大的动作。此外，我们的结果表明，愤怒和厌恶是帕金森病患者中受损最严重的两种表情。
基于视频的非接触式系统在康复领域，特别是言语治疗中，也可能是分析面部表情的重要技术，在言语治疗中，患者可以从关于要执行的正确面部表情/动作的实时反馈中获得明确的优势。
