title: Objectifying facial expressivity assessment of Parkinson's patients: preliminary study.
abstract: 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.
authors.affiliation: Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Peng
authors.identifier: 0000-0001-7777-1485
authors.initials: P
authors.lastname: Wu
authors.affiliation: Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Isabel
authors.identifier: 0000-0001-9070-7517
authors.initials: I
authors.lastname: Gonzalez
authors.affiliation: Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Georgios
authors.initials: G
authors.lastname: Patsis
authors.affiliation: Shaanxi Provincial Key Lab on Speech and Image Information Processing, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Jiang
authors.affiliation: Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Hichem
authors.initials: H
authors.lastname: Sahli
authors.affiliation: Department of Physical Therapy, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Eric
authors.initials: E
authors.lastname: Kerckhofs
authors.affiliation: Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Marie
authors.initials: M
authors.lastname: Vandekerckhove
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016022:Case-Control Studies; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods; D004576:Electromyography / Q000379:methods; D004644:Emotions; D005145:Face / Q000502:physiology*; D005149:Facial Expression; D005152:Facial Muscles / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D009127:Muscle Rigidity; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology*; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D021641:Recognition, Psychology; D055815:Young Adult