A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia
This paper introduces a classification model that detects and classifies argumentative behaviors between two individuals by utilizing a machine learning application, based on the MediaPipe Holistic model. The approach involves the distinction between two different classes based on the behavior of tw...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10266 |
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| author | Ioannis Galanakis Rigas Filippos Soldatos Nikitas Karanikolas Athanasios Voulodimos Ioannis Voyiatzis Maria Samarakou |
| author_facet | Ioannis Galanakis Rigas Filippos Soldatos Nikitas Karanikolas Athanasios Voulodimos Ioannis Voyiatzis Maria Samarakou |
| author_sort | Ioannis Galanakis |
| collection | DOAJ |
| description | This paper introduces a classification model that detects and classifies argumentative behaviors between two individuals by utilizing a machine learning application, based on the MediaPipe Holistic model. The approach involves the distinction between two different classes based on the behavior of two individuals, argumentative and non-argumentative behaviors, corresponding to verbal argumentative behavior. By using a dataset extracted from video frames of hand gestures, body stance and facial expression, and by using their corresponding landmarks, three different classification models were trained and evaluated. The results indicate that Random Forest Classifier outperformed the other two by classifying argumentative behaviors with 68.07% accuracy and non-argumentative behaviors with 94.18% accuracy, correspondingly. Thus, there is future scope for advancing this classification model to a prediction model, with the aim of predicting aggressive behavior in patients suffering with dementia before their onset. |
| format | Article |
| id | doaj-art-f8da128352ce4fdfab5eb9ff0645a699 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-f8da128352ce4fdfab5eb9ff0645a6992024-11-26T17:48:06ZengMDPI AGApplied Sciences2076-34172024-11-0114221026610.3390/app142210266A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with DementiaIoannis Galanakis0Rigas Filippos Soldatos1Nikitas Karanikolas2Athanasios Voulodimos3Ioannis Voyiatzis4Maria Samarakou5Department of Software Engineering, University of West Attica, 12243 Athens, GreeceFirst Department of Psychiatry, Eginition Hospital, National and Kapodistrian University of Athens Medical School, 11528 Athens, GreeceDepartment of Software Engineering, University of West Attica, 12243 Athens, GreeceDepartment of School of Electrical & Computing Engineering, National Technical University of Athens, 15780 Athens, GreeceDepartment of Software Engineering, University of West Attica, 12243 Athens, GreeceDepartment of Software Engineering, University of West Attica, 12243 Athens, GreeceThis paper introduces a classification model that detects and classifies argumentative behaviors between two individuals by utilizing a machine learning application, based on the MediaPipe Holistic model. The approach involves the distinction between two different classes based on the behavior of two individuals, argumentative and non-argumentative behaviors, corresponding to verbal argumentative behavior. By using a dataset extracted from video frames of hand gestures, body stance and facial expression, and by using their corresponding landmarks, three different classification models were trained and evaluated. The results indicate that Random Forest Classifier outperformed the other two by classifying argumentative behaviors with 68.07% accuracy and non-argumentative behaviors with 94.18% accuracy, correspondingly. Thus, there is future scope for advancing this classification model to a prediction model, with the aim of predicting aggressive behavior in patients suffering with dementia before their onset.https://www.mdpi.com/2076-3417/14/22/10266machine learningMediaPipeholistic modelaggressive behavior predictionargumentative detectiondementia |
| spellingShingle | Ioannis Galanakis Rigas Filippos Soldatos Nikitas Karanikolas Athanasios Voulodimos Ioannis Voyiatzis Maria Samarakou A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia Applied Sciences machine learning MediaPipe holistic model aggressive behavior prediction argumentative detection dementia |
| title | A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia |
| title_full | A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia |
| title_fullStr | A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia |
| title_full_unstemmed | A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia |
| title_short | A MediaPipe Holistic Behavior Classification Model as a Potential Model for Predicting Aggressive Behavior in Individuals with Dementia |
| title_sort | mediapipe holistic behavior classification model as a potential model for predicting aggressive behavior in individuals with dementia |
| topic | machine learning MediaPipe holistic model aggressive behavior prediction argumentative detection dementia |
| url | https://www.mdpi.com/2076-3417/14/22/10266 |
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