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Keywords = facial bradykinesia

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16 pages, 323 KiB  
Review
The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson’s Disease
by Edoardo Bianchini, Domiziana Rinaldi, Marika Alborghetti, Marta Simonelli, Flavia D’Audino, Camilla Onelli, Elena Pegolo and Francesco E. Pontieri
Brain Sci. 2024, 14(1), 109; https://doi.org/10.3390/brainsci14010109 - 22 Jan 2024
Cited by 7 | Viewed by 4255
Abstract
Facial movements are crucial for social and emotional interaction and well-being. Reduced facial expressions (i.e., hypomimia) is a common feature in patients with Parkinson’s disease (PD) and previous studies linked this manifestation to both motor symptoms of the disease and altered emotion recognition [...] Read more.
Facial movements are crucial for social and emotional interaction and well-being. Reduced facial expressions (i.e., hypomimia) is a common feature in patients with Parkinson’s disease (PD) and previous studies linked this manifestation to both motor symptoms of the disease and altered emotion recognition and processing. Nevertheless, research on facial motor impairment in PD has been rather scarce and only a limited number of clinical evaluation tools are available, often suffering from poor validation processes and high inter- and intra-rater variability. In recent years, the availability of technology-enhanced quantification methods of facial movements, such as automated video analysis and machine learning application, led to increasing interest in studying hypomimia in PD. In this narrative review, we summarize the current knowledge on pathophysiological hypotheses at the basis of hypomimia in PD, with particular focus on the association between reduced facial expressions and emotional processing and analyze the current evaluation tools and management strategies for this symptom, as well as future research perspectives. Full article
14 pages, 885 KiB  
Article
Psychometric Values of a New Scale: The Rett Syndrome Fear of Movement Scale (RSFMS)
by Meir Lotan, Moti Zwilling and Alberto Romano
Diagnostics 2023, 13(13), 2148; https://doi.org/10.3390/diagnostics13132148 - 23 Jun 2023
Cited by 2 | Viewed by 1743
Abstract
(1) Background: One of the characteristics associated with Rett syndrome (RTT) is a fear of movement (FOM). Despite the grave consequences on health, function, and the caregiver’s burden associated with bradykinesia accompanying FOM, there is no specific FOM assessment tool for RTT. (2) [...] Read more.
(1) Background: One of the characteristics associated with Rett syndrome (RTT) is a fear of movement (FOM). Despite the grave consequences on health, function, and the caregiver’s burden associated with bradykinesia accompanying FOM, there is no specific FOM assessment tool for RTT. (2) Objective: To construct and assess the psychometric values of a scale evaluating FOM in RTT (Rett syndrome fear of movement scale—RSFMS). (3) Methods: Twenty-five girls aged 5–33, including a research group (N = 12 individuals with RTT) and control group (N = 13 typically developing girls at equivalent ages). The Pain and Discomfort Scale (PADS) and Facial Action Coding System (FACS) assessed the participants’ behavior and facial expressions in rest and movement situations. (4) Results: Significant behavioral differences were recorded in these rest and movement situations within the research groups using the RSFMS (p = 0.003), FACS (p = 0.002) and PADS (p = 0.002). No differences in reactions were found within the control group. The new scale, RSFMS, was found to show a high inter- and intra-rater reliability (r = 0.993, p < 0.001; r = 0.958, p < 0.001; respectively), good internal consistency (α = 0.77), and high accuracy (94.4%). (5) Conclusions: The new scale for measuring FOM in RTT, the RSFMS, was validated using the FACS and PADS. The RSFMS was found to be a tool that holds excellent psychometric values. The new scale can help clinicians working with individuals with RTT to plan appropriate management strategies for this population. Full article
(This article belongs to the Section Pathology and Molecular Diagnostics)
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13 pages, 5140 KiB  
Article
A Study on the Possible Diagnosis of Parkinson’s Disease on the Basis of Facial Image Analysis
by Jacek Jakubowski, Anna Potulska-Chromik, Kamila Białek, Monika Nojszewska and Anna Kostera-Pruszczyk
Electronics 2021, 10(22), 2832; https://doi.org/10.3390/electronics10222832 - 18 Nov 2021
Cited by 9 | Viewed by 3930
Abstract
One of the symptoms of Parkinson’s disease is the occurrence of problems with the expression of emotions on the face, called facial masking, facial bradykinesia or hypomimia. Recent medical studies show that this symptom can be used in the diagnosis of this disease. [...] Read more.
One of the symptoms of Parkinson’s disease is the occurrence of problems with the expression of emotions on the face, called facial masking, facial bradykinesia or hypomimia. Recent medical studies show that this symptom can be used in the diagnosis of this disease. In the presented study, the authors, on the basis of their own research, try to answer the question of whether it is possible to build an automatic Parkinson’s disease recognition system based on the face image. The research used image recordings in the field of visible light and infrared. The material for the study consisted of registrations in a group of patients with Parkinson’s disease and a group of healthy patients. The patients were asked to express a neutral facial expression and a smile. In the detection, both geometric and holistic methods based on the use of convolutional network and image fusion were used. The obtained results were assessed quantitatively using statistical measures, including F1score, which was a value of 0.941. The results were compared with a competitive work on the same subject. A novelty of our experiments is that patients with Parkinson’s disease were in the so-called ON phase, in which, due to the action of drugs, the symptoms of the disease are reduced. The results obtained seem to be useful in the process of early diagnosis of this disease, especially in times of remote medical examination. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning for Biosignals Interpretation)
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