Biotechnical system for the study of facial expressions and motor activity of the hands of patients with Parkinson’s disease

2020 ◽  
pp. 54-60
Author(s):  
Anastasia А. Moshkova ◽  
Andrey B. Samorodov ◽  
Natalia A. Voinova ◽  
Alexander K. Volkov ◽  
Ekaterina O. Ivanova ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Activity ◽  
Facial Expressions ◽  
Biotechnical System

Disturbance of spontaneous and posed facial expressions in Parkinson's disease

2003 ◽  
Vol 17 (5) ◽  
pp. 759-778 ◽  
Author(s):  
Gwenda Simons ◽  
Heiner Ellgring ◽  
Marcia Smith Pasqualini
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Facial Expressions

Effects of the Lee Silverman Voice Treatment (LSVT® LOUD) on Hypomimia in Parkinson's Disease

2014 ◽  
Vol 20 (3) ◽  
pp. 302-312 ◽  
Author(s):  
Aleksey I. Dumer ◽  
Harriet Oster ◽  
David McCabe ◽  
Laura A. Rabin ◽  
Jennifer L. Spielman ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Facial Expressions ◽  
Psychosocial Problems ◽  
Coding System ◽  
Emotional Experiences ◽  
Facial Movement ◽  
Lee Silverman Voice Treatment ◽  
Voice Treatment ◽  
Facial Expressivity

AbstractGiven associations between facial movement and voice, the potential of the Lee Silverman Voice Treatment (LSVT) to alleviate decreased facial expressivity, termed hypomimia, in Parkinson's disease (PD) was examined. Fifty-six participants—16 PD participants who underwent LSVT, 12 PD participants who underwent articulation treatment (ARTIC), 17 untreated PD participants, and 11 controls without PD—produced monologues about happy emotional experiences at pre- and post-treatment timepoints (“T1” and “T2,” respectively), 1 month apart. The groups of LSVT, ARTIC, and untreated PD participants were matched on demographic and health status variables. The frequency and variability of facial expressions (Frequency and Variability) observable on 1-min monologue videorecordings were measured using the Facial Action Coding System (FACS). At T1, the Frequency and Variability of participants with PD were significantly lower than those of controls. Frequency and Variability increases of LSVT participants from T1 to T2 were significantly greater than those of ARTIC or untreated participants. Whereas the Frequency and Variability of ARTIC participants at T2 were significantly lower than those of controls, LSVT participants did not significantly differ from controls on these variables at T2. The implications of these findings, which suggest that LSVT reduces parkinsonian hypomimia, for PD-related psychosocial problems are considered. (JINS, 2014, 20, 1–11)

S-adenosyl-l-methionine decreases motor activity in the rat: Similarity to Parkinson's disease-like symptoms

1993 ◽  
Vol 59 (3) ◽  
pp. 186-193 ◽  
Author(s):  
Bernard G. Crowell ◽  
Richard Benson ◽  
Dolores Shockley ◽  
Clivel G. Charlton
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Activity

scholarly journals Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammad Rafayet Ali ◽  
Taylor Myers ◽  
Ellen Wagner ◽  
Harshil Ratnu ◽  
E. Ray Dorsey ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Facial Expressions ◽  
Preliminary Evidence ◽  
Support Vector ◽  
Web Based ◽  
Neutral Face ◽  
Automated Classifier ◽  
Objectively Measured ◽  
Prevalent Symptom

AbstractA prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.

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