This paper is in the following e-collection/theme issue:

New Methods (1134) Artificial Intelligence (2129) Machine Learning (2076)

Published on in Vol 22, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18697, first published .
Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis

Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis

Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis

Authors of this article:

Bo Jin1 Author Orcid Image ;   Yue Qu1 Author Orcid Image ;   Liang Zhang2 Author Orcid Image ;   Zhan Gao3 Author Orcid Image
Article Authors Cited by (99) Tweetations (16) Metrics

Journals

  1. Sibley K, Girges C, Hoque E, Foltynie T, Mirelman A, Dorsey E, Brundin P, Bloem B. Video-Based Analyses of Parkinson’s Disease Severity: A Brief Review. Journal of Parkinson's Disease 2021;11(s1):S83 View
  2. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  3. Küntzler T, Höfling T, Alpers G. Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions. Frontiers in Psychology 2021;12 View
  4. Ko H, Kim K, Bae M, Seo M, Nam G, Park S, Park S, Ihm J, Lee J. Changes in Computer-Analyzed Facial Expressions with Age. Sensors 2021;21(14):4858 View
  5. Neethirajan S. Happy Cow or Thinking Pig? WUR Wolf—Facial Coding Platform for Measuring Emotions in Farm Animals. AI 2021;2(3):342 View
  6. Giannakopoulou K, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review. Sensors 2022;22(5):1799 View
  7. Calvo-Ariza N, Gómez-Gómez L, Orozco-Arroyave J. Classical FE Analysis to Classify Parkinson’s Disease Patients. Electronics 2022;11(21):3533 View
  8. Sibley K, Girges C, Candelario J, Milabo C, Salazar M, Esperida J, Dushin Y, Limousin P, Foltynie T. An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III. Journal of Parkinson’s Disease 2022;12(7):2223 View
  9. Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson’s Disease: Towards a New Era of Research and Clinical Care. Phenomics 2022;2(5):349 View
  10. Lasri I, Riadsolh A, Elbelkacemi M. Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learning. Education and Information Technologies 2023;28(4):4069 View
  11. Hou X, Zhang Y, Wang Y, Wang X, Zhao J, Zhu X, Su J. 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 2021;23(11):e29554 View
  12. Chen M, Sun Z, Su F, Chen Y, Bu D, Lyu Y. An Auxiliary Diagnostic System for Parkinson’s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2022;30:2254 View
  13. Dixit S, Bohre K, Singh Y, Himeur Y, Mansoor W, Atalla S, Srinivasan K. A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis. Electronics 2023;12(4):783 View
  14. Su G, Lin B, Luo W, Yin J, Deng S, Gao H, Xu R. Hypomimia Recognition in Parkinson’s Disease With Semantic Features. ACM Transactions on Multimedia Computing, Communications, and Applications 2021;17(3s):1 View
  15. Alajmi M, A. Elshakankiry O, El-Shafai W, S. El-Sayed H, I. Sallam A, M. El-Hoseny H, Sedik A, S. Faragallah O. Smart and Automated Diagnosis of COVID-19 Using Artificial Intelligence Techniques. Intelligent Automation & Soft Computing 2022;32(3):1403 View
  16. Hoang T, Zehni M, Xu H, Heintz G, Zallek C, Do M. Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination. IEEE Journal of Biomedical and Health Informatics 2022;26(8):4020 View
  17. Zhang X, Wang Y, Zhang L, Jin B, Zhang H. Exploring unsupervised multivariate time series representation learning for chronic disease diagnosis. International Journal of Data Science and Analytics 2023;15(2):173 View
  18. Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi M, Marín Valero M, Corvol J, Eskofier B, Van Gyseghem J, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Frontiers in Neurology 2022;13 View
  19. Maremmani C, Rovini E, Salvadori S, Pecori A, Pasquini J, Ciammola A, Rossi S, Berchina G, Monastero R, Cavallo F. Hands–feet wireless devices: Test–retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring. Acta Neurologica Scandinavica 2022;146(3):304 View
  20. He S, Su H, Li Y, Xu W, Wang X, Han D. Detecting obstructive sleep apnea by craniofacial image–based deep learning. Sleep and Breathing 2022;26(4):1885 View
  21. Zhang A, Lou J, Pan Z, Luo J, Zhang X, Zhang H, Li J, Wang L, Cui X, Ji B, Chen L. Prediction of anemia using facial images and deep learning technology in the emergency department. Frontiers in Public Health 2022;10 View
  22. Gat L, Gerston A, Shikun L, Inzelberg L, Hanein Y, Komiyama T. Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions. PLOS ONE 2022;17(2):e0262286 View
  23. Gomez L, Morales A, Fierrez J, Orozco-Arroyave J, Damaševičius R. Exploring facial expressions and action unit domains for Parkinson detection. PLOS ONE 2023;18(2):e0281248 View
  24. Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H. Review on Facial-Recognition-Based Applications in Disease Diagnosis. Bioengineering 2022;9(7):273 View
  25. Bendig J, Wolf A, Mark T, Frank A, Mathiebe J, Scheibe M, Müller G, Stahr M, Schmitt J, Reichmann H, Loewenbrück K, Falkenburger B. Feasibility of a Multimodal Telemedical Intervention for Patients with Parkinson’s Disease—A Pilot Study. Journal of Clinical Medicine 2022;11(4):1074 View
  26. Huang W, Zhou Y, Cheung Y, Zhang P, Zha Y, Pang M. Facial Expression Guided Diagnosis of Parkinson's Disease via High-Quality Data Augmentation. IEEE Transactions on Multimedia 2023;25:7037 View
  27. Kuzin A, Glushakov R, Parfenov S, Sapozhnikov K, Lazarev A. Development of an artificial intelligence system for the forecasting of infectious diseases. Fundamental and Clinical Medicine 2023;8(3):143 View
  28. Fang B, Zhao Y, Han G, He J. Expression-Guided Deep Joint Learning for Facial Expression Recognition. Sensors 2023;23(16):7148 View
  29. Yang Y, Lyu J, Wang R, Xu F, Dai Q, Lin H. Reply to: Concerns about using a digital mask to safeguard patient privacy. Nature Medicine 2023;29(7):1660 View
  30. Sun J, Dodge H, Mahoor M. MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos. Expert Systems with Applications 2024;238:121929 View
  31. Oliveira G, Ngo Q, Passos L, Papa J, Jodas D, Kumar D. Tabular data augmentation for video-based detection of hypomimia in Parkinson’s disease. Computer Methods and Programs in Biomedicine 2023;240:107713 View
  32. Ho A, Bavli I, Mahal R, McKeown M. Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson’s Disease. AJOB Empirical Bioethics 2024;15(3):178 View
  33. Sharma P, Nayak D, Balabantaray B, Tanveer M, Nayak R. A survey on cancer detection via convolutional neural networks: Current challenges and future directions. Neural Networks 2024;169:637 View
  34. Skaramagkas V, Pentari A, Kefalopoulou Z, Tsiknakis M. Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:2399 View
  35. Phienphanich P, Lerthirunvibul N, Charnnarong E, Munthuli A, Tantibundhit C, Suwanwela N. Generalizing a Small Facial Image Dataset Using Facial Generative Adversarial Networks for Stroke’s Facial Weakness Screening. IEEE Access 2023;11:64886 View
  36. Park S, No C, Kim S, Han K, Jung J, Kwon K, Lee M. A multimodal screening system for elderly neurological diseases based on deep learning. Scientific Reports 2023;13(1) View
  37. Saadi I, cunningham D, Taleb-Ahmed A, Hadid A, Hillali Y. Driver’s facial expression recognition: A comprehensive survey. Expert Systems with Applications 2024;242:122784 View
  38. Huang J, Lin L, Yu F, He X, Song W, Lin J, Tang Z, Yuan K, Li Y, Huang H, Pei Z, Xian W, Yu-Chian Chen C. Parkinson's severity diagnosis explainable model based on 3D multi-head attention residual network. Computers in Biology and Medicine 2024;170:107959 View
  39. Tang Y, Wu Y, Zhou P, Hu J. Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2022;41(11):3910 View
  40. Bianchini E, Rinaldi D, Alborghetti M, Simonelli M, D’Audino F, Onelli C, Pegolo E, Pontieri F. The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson’s Disease. Brain Sciences 2024;14(1):109 View
  41. Theofilou P, Tsatiris G, Kollias S. Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia. Electronics 2024;13(3):635 View
  42. Zadoo S, Singh Y, Singh P. Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges. International Journal on Smart Sensing and Intelligent Systems 2024;17(1) View
  43. Hall N, Hallquist M, Martin E, Lian W, Jonas K, Kotov R. Automating the analysis of facial emotion expression dynamics: A computational framework and application in psychotic disorders. Proceedings of the National Academy of Sciences 2024;121(14) View
  44. Lv C, Fan L, Li H, Ma J, Jiang W, Ma X. Leveraging multimodal deep learning framework and a comprehensive audio-visual dataset to advance Parkinson’s detection. Biomedical Signal Processing and Control 2024;95:106480 View
  45. Kaur M, Kumar M. Facial emotion recognition: A comprehensive review. Expert Systems 2024;41(10) View
  46. Munsif M, Sajjad M, Ullah M, Tarekegn A, Cheikh F, Tsakanikas P, Muhammad K. Optimized efficient attention-based network for facial expressions analysis in neurological health care. Computers in Biology and Medicine 2024;179:108822 View
  47. Zhang F, Chai L. A review of research on micro-expression recognition algorithms based on deep learning. Neural Computing and Applications 2024 View
  48. Tang W, van Ooijen P, Sival D, Maurits N. Automatic two-dimensional & three-dimensional video analysis with deep learning for movement disorders: A systematic review. Artificial Intelligence in Medicine 2024;156:102952 View
  49. Sun Y, Wang Z, Liang Y, Hao C, Shi C. Digital biomarkers for precision diagnosis and monitoring in Parkinson’s disease. npj Digital Medicine 2024;7(1) View
  50. Zhu J, Ding Y, Liu H, Chen K, Lin Z, Hong W. Emotion knowledge-based fine-grained facial expression recognition. Neurocomputing 2024;610:128536 View
  51. Nahass G, Peterson J, Heinze K, Choudhary A, Khandwala N, Purnell C, Setabutr P, Tran A. FaceFinder: A machine learning tool for identification of facial images from heterogenous datasets. AJO International 2024;1(4):100083 View
  52. Li Y, He Y, Liu Y, Wang B, Li B, Qiu X. Identification of Intracranial Germ Cell Tumors Based on Facial Photo: Software Development Using Deep Learning Technology (Preprint). Journal of Medical Internet Research 2024 View
  53. Guo R, Wang L, Zhang C, Gu L, Li D, Qian X. A Causality-Informed Graph Convolutional Network for Video Assessment of Parkinsonian Leg Agility. IEEE Transactions on Circuits and Systems for Video Technology 2024;34(11):11101 View
  54. ZHENG T, WANG X, PENG X, SU N, XU T, XIE X, HUANG J, XIE L, TIAN F. Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies. Virtual Reality & Intelligent Hardware 2024;6(6):421 View
  55. Jiao L, Yao J. RETRACTED: Infrared thermal image detection and facial expression recognition based on genetic algorithm in sports prediction simulation: Sports thermal modeling. Thermal Science and Engineering Progress 2025;58:103218 View
  56. Lee H, Kim M, Chung M. Pain-Related Responses in Preterm Babies Using Automated and Laser Heel-Lancing Devices. Creative Nursing 2025;31(2):190 View
  57. Goudarzi N, Taheri Z, Nezhad Salari A, Kazemzadeh K, Tafakhori A. Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders. Reviews in the Neurosciences 2025;36(5):479 View
  58. Oliveira G, Ngo Q, Passos L, Jodas D, Papa J, Kumar D. Facial Expression Analysis in Parkinsons’s Disease Using Machine Learning: A Review. ACM Computing Surveys 2025;57(8):1 View
  59. 周 琬. Current Status and Outlook of the Application of Artificial Intelligence Technology in the Diagnosis and Treatment of Parkinson’s Disease. International Journal of Psychiatry and Neurology 2025;14(01):1 View
  60. Huang J, Lin L, Lin J, Zhou J, Yin B, Lyu W, Su F, Yuan K, Cai M, Li Y, Kong H, Burgunder J, Pei Z, Chen C. A Novel End-to-End 3-D Residual and Attention Enhancement Joint Few-Shot Learning Model for Huntington Clinical Assessment. IEEE Transactions on Instrumentation and Measurement 2025;74:1 View
  61. Rodrigues J, De Melo Souza Veras R, De Sousa Britto Neto L, Henrique Ximenes Ramalho Barros P, Da Silva Moura W, James Silva De Almeida K, Romulo Teixeira Aires K. Identification of Parkinson’s Disease Through Facial Image Classification: A Systematic Review. IEEE Access 2025;13:46720 View
  62. Ren W, Gao Y, Chen X, Han Z, Wang Z, Wang J, Liu H. Facial Expression Monitoring via Fine-Grained Vision-Language Alignment. IEEE Transactions on Automation Science and Engineering 2025;22:7492 View
  63. Filali Razzouki A, Jeancolas L, Sambin S, Mangone G, Chalançon A, Gomes M, Lehéricy S, Vidailhet M, Arnulf I, Corvol J, Petrovska-Delacrétaz D, El-Yacoubi M. Explaining facial action units' correlation with hypomimia and clinical scores in Parkinson’s disease. npj Parkinson's Disease 2025;11(1) View
  64. Pecoraro P, Marsili L, Espay A, Bologna M, di Biase L. Computer Vision Technologies in Movement Disorders: A Systematic Review. Movement Disorders Clinical Practice 2025;12(9):1229 View
  65. Song J, He M, Zheng X, Zhang Y, Bi C, Feng J, Du J, Li H, Shen B. Face-based machine learning diagnostics: applications, challenges and opportunities. Artificial Intelligence Review 2025;58(8) View
  66. Rizzo M, Dawson J. AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice. Annals of Neurology 2025;98(2):211 View
  67. Yang N, Liu J, Sun D, Ding J, Sun L, Qi X, Yan W. Motor symptoms of Parkinson’s disease: critical markers for early AI-assisted diagnosis. Frontiers in Aging Neuroscience 2025;17 View
  68. Ortiz-Perez D, Benavent-Lledo M, Garcia-Rodriguez J, Tomás D, Vizcaya-Moreno M. Deep insights into cognitive decline: A survey of leveraging non-intrusive modalities with deep learning techniques. Applied Soft Computing 2025;184:113787 View
  69. Xu Y, Zhou Y, Li Z, Huang J, Huang W. In Vitro Diagnosis of Parkinson's Disease Based on Facial Expression and Behavioral Gait Data. IEEE Journal of Biomedical and Health Informatics 2025;29(9):6856 View
  70. Yang N, Liu J, Wang L, Ding J, Sun L, Qi X, Lu Y, Yan W. Automated identification of early to mid-stage Parkinson’s disease using deep convolutional neural networks on static facial images. Frontiers in Medical Technology 2025;7 View
  71. Vanitha V, Dhilip Kumar V, Geman O, Postolache O, Krishna Bellam S, Toderean R. Modeling and Classifying Parkinsonian Tremors With Nonlinear Dynamics and Kalman Optimization. IEEE Access 2025;13:156930 View
  72. Rios-Urrego C, Tykalova T, Dusek P, Orozco-Arroyave J, Rusz J, Novotny M. Dynamic cheek surface modeling for enhanced hypomimia detection in Parkinson’s disease. Computers in Biology and Medicine 2025;197:110896 View
  73. Toptaş A, Bozkurt H, Ekinci E, Güzey Aras Y, Garip Z. Ensemble Deep Learning with Majority Voting for Parkinson’s Diagnosis via Facial Images. Journal of Intelligent Systems: Theory and Applications 2025;8(2):95 View
  74. Huang X, Li H, Ma J, Bi X, Meng F, Jiang W, Ma X. Facial expression-based hypomimia detection for Parkinson’s disease diagnosis: A static-dynamic mixed feature approach. Biomedical Signal Processing and Control 2026;112:108762 View
  75. Filali Razzouki A, Jeancolas L, Petrovska-Delacrétaz D, El-Yacoubi M. Facial digital markers For hypomimia detection in Parkinson’s disease: A systematic review. Pattern Recognition 2026;172:112573 View
  76. Zhang A, Yang Y, Yang B, Gao Q, Wang Y, Jing D, Li H, Chen J, Yu L, Li Y, Zeng T, Liu B, Zhao S, Zu X, Zhang X, Lei Z, Hu M. Prediction model of liver function through facial images using deep learning for liver cancer patients. Biomedical Signal Processing and Control 2026;113:108965 View
  77. di Biase L, Pecoraro P, Bugamelli F. AI Video Analysis in Parkinson’s Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management. Sensors 2025;25(20):6373 View
  78. Mantri S, Ghilardi M, Lessard N, Norcini M, Di Rocco A, Wallock K, Lehr J, Okun M. Proceedings of the world summit on parkinson’s disease. npj Parkinson's Disease 2025;11(1) View
  79. Ali S, Sajjad M, Lee I, Cheikh F, Ribigan A, Pedullà L, Papagiannakis N, Hijji M, Muhammad K. IoT-Driven Facial Expression Recognition for Personalized Healthcare in Industry 5.0. IEEE Internet of Things Journal 2025;12(22):45995 View

Books/Policy Documents

  1. Munsif M, Ullah M, Ahmad B, Sajjad M, Cheikh F. Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. View
  2. Wibowo H, Soflano M, Suharso W. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems. View
  3. Hou X, Qin S, Su J. Proceedings of 2021 Chinese Intelligent Automation Conference. View
  4. . Digital Technologies in Movement Disorders. View
  5. Wright L. Digital Mental Health. View
  6. Bordallo López M, Álvarez Casado C, Susarla P, Lage Cañellas M, Nguyen L. Handbook of Digital Technologies in Movement Disorders. View
  7. Vanitha V, Vijay Hari B, Adithyan B. Harnessing AI and Machine Learning for Precision Wellness. View
  8. Muñoz J, Villaroel B, Taramasco C, Gatica G. Advanced Research in Technologies, Information, Innovation and Sustainability. View
  9. Castillo-Chicaiza L, Ríos-Urrego C, Escobar-Grisales D, Orozco-Arroyave J. Applied Computer Sciences in Engineering. View

Conference Proceedings

  1. Rani S, Bhambri P, Chauhan M. 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT). A Machine Learning Model for Kids’ Behavior Analysis from Facial Emotions using Principal Component Analysis View
  2. Huang S, Yang D, Zhong C, Zhang L. 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). IVSA: Facial Expression Recognition Method with Salient Attention View
  3. Cacciatori F, Nikolaev S, Grigorev D, Archangelskaya A. 2023 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS). On Developing Facial Stress Analysis and Expression Recognition Platform View
  4. Wu F, Li X. 2023 8th International Conference on Information Systems Engineering (ICISE). Facial Expression Recognition Based on Time Series Self-Attention Feature Fusion View
  5. Islam M, Lee S, Abdelkader A, Park S, Hoque E. 2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). PARK: Parkinson’s Analysis with Remote Kinetic-tasks View
  6. Zhou Y, Pang M, Huang W, Wang B. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Early Diagnosing Parkinson's Disease Via a Deep Learning Model Based on Augmented Facial Expression Data View
  7. Nourivandi T, Hinduja S, Srivastava S, Cohn J, Canavan S. 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG). Mitigating Class Imbalance for Facial Expression Recognition Using SMOTE on Deep Features View
  8. Ting Z, Yadav A, Jiang S, Khan A. 2024 Asia Pacific Conference on Innovation in Technology (APCIT). Parameters Research of Facial Emotion Detection Algorithm Based on Machine Learning View
  9. Rodrigues J, Pereira C, Veras R, Barros P, Almeida K, Aires K. Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025). Análise de Imagens Faciais para Identificação do Parkinson em Contextos de Dados Limitados View
  10. Zadoo S, Singh Y. 2025 International Conference on Electronics, AI and Computing (EAIC). Challenges in the Automated Diagnosis of Parkinson’s Disease across various modalities View
  11. Han L, Sun Y. Proceedings of the 2025 13th International Conference on Computer and Communications Management. ParkinAid: A Multimodal System for Parkinson's Diagnosis through Motor and Speech Disorder Analysis using Machine Learning and Computer Vision View