Wu Peng, Gonzalez Isabel, Patsis Georgios, Jiang Dongmei, Sahli Hichem, Kerckhofs Eric, Vandekerckhove Marie
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Shaanxi Provincial Key Lab on Speech and Image Information Processing, Northwestern Polytechnical University, Xi'an, China.
Comput Math Methods Med. 2014;2014:427826. doi: 10.1155/2014/427826. Epub 2014 Nov 13.
Patients with Parkinson's disease (PD) can exhibit a reduction of spontaneous facial expression, designated as "facial masking," a symptom in which facial muscles become rigid. To improve clinical assessment of facial expressivity of PD, this work attempts to quantify the dynamic facial expressivity (facial activity) of PD by automatically recognizing facial action units (AUs) and estimating their intensity. Spontaneous facial expressivity was assessed by comparing 7 PD patients with 8 control participants. To voluntarily produce spontaneous facial expressions that resemble those typically triggered by emotions, six emotions (amusement, sadness, anger, disgust, surprise, and fear) were elicited using movie clips. During the movie clips, physiological signals (facial electromyography (EMG) and electrocardiogram (ECG)) and frontal face video of the participants were recorded. The participants were asked to report on their emotional states throughout the experiment. We first examined the effectiveness of the emotion manipulation by evaluating the participant's self-reports. Disgust-induced emotions were significantly higher than the other emotions. Thus we focused on the analysis of the recorded data during watching disgust movie clips. The proposed facial expressivity assessment approach captured differences in facial expressivity between PD patients and controls. Also differences between PD patients with different progression of Parkinson's disease have been observed.
帕金森病(PD)患者可能会出现自发面部表情减少的情况,称为“面部面具样表现”,即面部肌肉变得僵硬的一种症状。为了改善对PD患者面部表情的临床评估,本研究试图通过自动识别面部动作单元(AU)并估计其强度来量化PD患者的动态面部表情(面部活动)。通过比较7名PD患者和8名对照参与者来评估自发面部表情。为了让参与者自愿产生类似于通常由情绪引发的自发面部表情,使用电影片段引发了六种情绪(愉悦、悲伤、愤怒、厌恶、惊讶和恐惧)。在播放电影片段期间,记录参与者的生理信号(面部肌电图(EMG)和心电图(ECG))以及正面面部视频。在整个实验过程中,要求参与者报告他们的情绪状态。我们首先通过评估参与者的自我报告来检验情绪诱导的有效性。厌恶引发的情绪显著高于其他情绪。因此,我们专注于分析观看厌恶电影片段期间记录的数据。所提出的面部表情评估方法捕捉到了PD患者与对照者之间面部表情的差异。此外,还观察到了不同帕金森病进展程度的PD患者之间的差异。