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FaceTell: A novel solution to diagnosing Parkinson’s disease with facial expressions and deep learning
- 1 Palo Alto High School
- 2 University of California
* Author to whom correspondence should be addressed.
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease afflicting over 10 million patients worldwide, most commonly the elderly, that causes tremors, stiffness, movement loss, and other symptoms. Since symptoms are often mild and difficult to notice in the early stages of the condition, it can be hard to notice and diagnose until the condition has already become more severe. An earlier diagnosis of PD will allow treatment to begin earlier and lessen the impact of the disease. The goal of this work is to develop an affordable, non-intrusive, and accessible way of diagnosing PD. This neurodegenerative disorder leads to loss of movement control and other symptoms. Since there is no known cure for PD yet, early diagnosis would allow timely treatment and prevent the symptoms from worsening too quickly. Doing so in an affordable and non-intrusive way will minimize costs and maximize efficiency — removing the need for lengthy consulting with doctors and possibly expensive testing and medical equipment. This work presents the FaceTell system, which combines and optimizes traditional machine learning and deep learning to make predictions on the patient’s PD status based on video data of their faces. By analyzing a variety of attributes such as facial expressions and emotion prevalence/intensity, the model was able to achieve a more thorough examination of the patient’s condition and make predictions of similar accuracy compared to prior results. One main innovation was collecting data affordably: sampling publicly available videos from platforms like YouTube. This serves as a proof-of-concept to show that simple, affordable, and non-intrusive data collection methods can still produce viable results. Using methods and tools such as hyperparameter tuning, data cleaning, and Face++, the performance of the system readily improved. The ultimate results obtained include an F1 score of 0.86 and an accuracy of 89%, compared to prior results which reached up to 95% accuracy.
Keywords
Parkinson’s disease, early diagnosis, machine learning, facial expressions
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Cite this article
Shi,H.;Wu,B. (2023). FaceTell: A novel solution to diagnosing Parkinson’s disease with facial expressions and deep learning. Applied and Computational Engineering,21,53-62.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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