FaceTell: A novel solution to diagnosing Parkinson’s disease with facial expressions and deep learning

Research Article
Open access

FaceTell: A novel solution to diagnosing Parkinson’s disease with facial expressions and deep learning

Hanwen Shi 1* , Ben Wu 2
  • 1 Palo Alto High School    
  • 2 University of California    
  • *corresponding author hanwenshi6@gmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/21/20231117
ACE Vol.21
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-033-2
ISBN (Online): 978-1-83558-034-9

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

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.
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References

[1]. Parkinson’s Disease: Challenges, Progress, and Promise. (2023, January 30). National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/current-research/focus-disorders/focus-parkinsons-disease-research/parkinsons-disease-challenges-progress-and-promise

[2]. Mayo Clinic Staff. (2023, May 26). Parkinson’s disease. Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/

[3]. National Health Service. (2022, November 11). Parkinson’s disease. The NHS Website. https://www.nhs.uk/conditions/parkinsons-disease/

[4]. Yang, W., Hamilton, J. L., Kopil, C., Beck, J. C., Tanner, C. M., Albin, R. L., ... & Thompson, T. (2020). Current and projected future economic burden of Parkinson’s disease in the US. npj Parkinson's Disease, 6(1), 1-9.

[5]. Jin, B., Qu, Y., Zhang, L., & Gao, Z. (2020). Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis. Journal of medical Internet research, 22(7), e18697. https://doi.org/10.2196/18697

[6]. Ali, M. R., Myers, T., Wagner, E., Ratnu, H., Dorsey, E., & Hoque, E. (2021). Facial expressions can detect Parkinson’s disease: Preliminary evidence from videos collected online. NPJ digital medicine, 4(1), 1-4.

[7]. Hou, X., Zhang, Y., Wang, Y., Wang, X., Zhao, J., Zhu, X., & Su, J. (2021). A Markerless 2D Video, Facial Feature Recognition–Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study. Journal of Medical Internet Research, 23(11), e29554.

[8]. Yang, L., Chen, X., Guo, Q., Zhang, J., Luo, M., Chen, X., ... & Xu, F. (2022). Changes in facial expressions in patients with Parkinson's disease during the phonation test and their correlation with disease severity. Computer Speech & Language, 72, 10

[9]. Pegolo, E., Volpe, D., Cucca, A., Ricciardi, L., & Sawacha, Z. (2022). Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach. Sensors (Basel, Switzerland), 22(4), 1358. https://doi.org/10.3390/s22041358

[10]. U.S. Copyright Office Fair Use Index. (2023, February). U.S. Copyright Office. https://www.copyright.gov/fair-use/

[11]. Megvii. (n.d.). Face Detection. Face++ Cognitive Services. Retrieved April 21, 2023, from https://www.faceplusplus.com/face-detection/

[12]. Scikit-learn: Machine Learning in Python, Pedregosa, F. et al., JMLR 12, pp. 2825-2830, 2011.

[13]. Banoula, M. (2023, May 29). An Overview on Multilayer Perceptron (MLP). Simplilearn. https://www.simplilearn.com/tutorials/deep- learning-tutorial/multilayer-perceptron

[14]. Korstanje, J. (2021, August 31). The F1 score. Towards Data Science. https://towardsdatascience.com/the-f1-score-bec2bbc38aa6

[15]. Hajian-Tilaki, K. (2013). Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian journal of internal medicine, 4(2), 627–635.

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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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-033-2(Print) / 978-1-83558-034-9(Online)
Editor：Roman Bauer, Alan Wang, Marwan Omar
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.21
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Parkinson’s Disease: Challenges, Progress, and Promise. (2023, January 30). National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/current-research/focus-disorders/focus-parkinsons-disease-research/parkinsons-disease-challenges-progress-and-promise

[2]. Mayo Clinic Staff. (2023, May 26). Parkinson’s disease. Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/

[3]. National Health Service. (2022, November 11). Parkinson’s disease. The NHS Website. https://www.nhs.uk/conditions/parkinsons-disease/

[4]. Yang, W., Hamilton, J. L., Kopil, C., Beck, J. C., Tanner, C. M., Albin, R. L., ... & Thompson, T. (2020). Current and projected future economic burden of Parkinson’s disease in the US. npj Parkinson's Disease, 6(1), 1-9.

[5]. Jin, B., Qu, Y., Zhang, L., & Gao, Z. (2020). Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis. Journal of medical Internet research, 22(7), e18697. https://doi.org/10.2196/18697

[6]. Ali, M. R., Myers, T., Wagner, E., Ratnu, H., Dorsey, E., & Hoque, E. (2021). Facial expressions can detect Parkinson’s disease: Preliminary evidence from videos collected online. NPJ digital medicine, 4(1), 1-4.

[7]. Hou, X., Zhang, Y., Wang, Y., Wang, X., Zhao, J., Zhu, X., & Su, J. (2021). A Markerless 2D Video, Facial Feature Recognition–Based, Artificial Intelligence Model to Assist With Screening for Parkinson Disease: Development and Usability Study. Journal of Medical Internet Research, 23(11), e29554.

[8]. Yang, L., Chen, X., Guo, Q., Zhang, J., Luo, M., Chen, X., ... & Xu, F. (2022). Changes in facial expressions in patients with Parkinson's disease during the phonation test and their correlation with disease severity. Computer Speech & Language, 72, 10

[9]. Pegolo, E., Volpe, D., Cucca, A., Ricciardi, L., & Sawacha, Z. (2022). Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach. Sensors (Basel, Switzerland), 22(4), 1358. https://doi.org/10.3390/s22041358

[10]. U.S. Copyright Office Fair Use Index. (2023, February). U.S. Copyright Office. https://www.copyright.gov/fair-use/

[11]. Megvii. (n.d.). Face Detection. Face++ Cognitive Services. Retrieved April 21, 2023, from https://www.faceplusplus.com/face-detection/

[12]. Scikit-learn: Machine Learning in Python, Pedregosa, F. et al., JMLR 12, pp. 2825-2830, 2011.

[13]. Banoula, M. (2023, May 29). An Overview on Multilayer Perceptron (MLP). Simplilearn. https://www.simplilearn.com/tutorials/deep- learning-tutorial/multilayer-perceptron

[14]. Korstanje, J. (2021, August 31). The F1 score. Towards Data Science. https://towardsdatascience.com/the-f1-score-bec2bbc38aa6

[15]. Hajian-Tilaki, K. (2013). Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian journal of internal medicine, 4(2), 627–635.

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