Review of automated emotion-based quantification of facial expression in Parkinson’s patients

Sonawane, Bhakti; Sharma, Priyanka

doi:10.1007/s00371-020-01859-9

Cited by 36 publications

(17 citation statements)

References 105 publications

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“…[10] proposes an approach divided into transfer learning through Alex Nets and hyperparameter tuning through ABC, GA, and PSO algorithms. The methodology produced effective outcomes with an average accuracy of 98.09 percent, beating the best work in the medical sector in [21]; computational analysis techniques are used to measure the emotional facial expression of people who have Parkinson's disease (PD). Since PD experiences hypomania, which often causes a reduction in facial expression, it is important to examine an experimental pilot work for masked face detection in PD.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for emotion analysis in Arabic tweets

Khalil

Houby

Mohamed

2021

Preprint

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Expressing our emotions using text and emojis expressions became widespread through social media such as Facebook, Instagram, Twitter, Weibo, and LinkedIn. Nowadays, both organizations and individuals are interested in using social media to analyze people's opinions and extract sentiments and emotions. We proposed a model for multilabel emotion classification, using a bidirectional Long Short-term Memory BiLSTM deep network. It is evaluated on the Arabic tweets' dataset provided by SemEval 2018 for the E-c task. Several preprocessing steps, including ARLSTEM with some modifications, replacing emojis with corresponding text meaning from a manually built lexicon, and feature vector representation using Aravec word embedding is applied. The novelty in our research that it examines the effect of hyperparameter tuning on model performance, and it uses BiLSTM in all of its deep neural network layers. The proposed model achieves a comparable performance with state-of-the-art models using different machine learning and deep learning techniques. The system achieves about 9% enhancement in validation accuracy compared with the last best model in the same task using Support Vector classifier SVC; it outperforms the other deep neural networks (UNCCTeam) based on fully connected layers in micro F1 metric of about 4.4%.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for emotion analysis in Arabic tweets

Khalil

Houby

Mohamed

2021

Preprint

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“…These studies focus on the analysis of the differences in expressing the emotion of PD versus HC. The most frequent emotions that are taken into consideration are anger, fear, happiness, sadness, disgust or surprise, and neutral [25], [26].…”

Section: Related Workmentioning

confidence: 99%

“…For a better understanding of hypomimia in PD patients, there are a couple of approaches that have been used so far for facial expression. For example the analysis of electromyography (EMG) records, the affectograms, the Action Units, the Maximally Discriminative Facial Movement Coding System, and methods based on machine learning emotion recognition [25].…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Skibińska

Bürget

2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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Parkinson's disease (PD) is the neurodegenerative disease which affects 2-3 % of the population beyond 65 years of age in EU. When PD treatment is administered early, it is significantly more effective. Unfortunately, it is quite challenging to detect this disease at its early stage and when the symptoms can be recognized it is usually quite late. For this reason there is big motivation for development more accessible and accurate solutions for the detection of PD. One of the early symptoms is so-called hypomimia. This paper introduces an automatic method, which can objectively detect PD. The method is based on analysis of emotion changes during pronunciation defined speech exercises. We achieved balanced accuracy 69 % using XGBoost algorithm. As the exercise we proposed to use a Czech tongue twister-the difficult to pronounce sentence. The features can be explained and thus it can be used in clinical practice. We identified that the most valuable emotion for PD detection in this case is fear. 124 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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“…Two different indexes of hypomimia were developed without discriminating among different emotions. A review of automatic techniques for detecting emotions in PD was recently carried out by Sonawane and Sharma [ 22 ]; they investigated both machine and DL algorithms used in the classification of emotions in PD subjects with hypomimia. Moreover, they addressed the problem of expression quantification and related pending issues.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Hypomimia in Parkinson’s Disease: A Face Tracking Approach

Pegolo

Volpe²,

Cucca

et al. 2022

Sensors

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Parkinson’s disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients’ quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric.

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Review of automated emotion-based quantification of facial expression in Parkinson’s patients

Cited by 36 publications

References 105 publications

Deep Learning for emotion analysis in Arabic tweets

Deep Learning for emotion analysis in Arabic tweets

Parkinson’s Disease Detection based on Changes of Emotions during Speech

Quantitative Evaluation of Hypomimia in Parkinson’s Disease: A Face Tracking Approach

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