Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements

Śledzianowski, Albert; Nowacki, J.; Przybyszewski, Andrzej W.; Urbanowicz, Krzysztof

doi:10.1007/978-3-031-21743-2_9

Cited by 1 publication

(2 citation statements)

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“…Interestingly, further validation was conducted using the Affectiva-MIT Facial Expression Dataset (AM-FED) which included both human and algorithmic classifications [ 57 ]. It confirmed that EM chaos emerged as a biomarker of happiness, and it can play a more crucial role than the intensity of specific muscle activity (AU12).…”

Section: Emotional States Estimations By Facial Expressions Can Indic...mentioning

confidence: 99%

“…Recent findings suggest that AI methods predict cognitive patterns in normal subjects, indicating pre-dementia stages. For example, Przybyszewski et al (2022) used granular computing rules to classify cognitive data from the BIOCARD study, which has been ongoing for over 20 years with 354 normal subjects. The study's findings suggest that AI methods can predict patterns in cognitive attributes of normal subjects that might indicate their pre-dementia stage, something that may not be visible to neuropsychologists [125].…”

Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

confidence: 99%

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Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Chudzik,

Śledzianowski,

Przybyszewski

2024

Sensors

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Neurodegenerative diseases (NDs) such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores “digital biomarkers” (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

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Section: Emotional States Estimations By Facial Expressions Can Indic...mentioning

confidence: 99%

Section: Ai and Machine Learning Can Predict Symptoms And Progression...mentioning

confidence: 99%

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Chudzik,

Śledzianowski,

Przybyszewski

2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements

Cited by 1 publication

References 20 publications

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

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