Online Analysis of Handwriting for Disease Diagnosis: A Review

  • Authors

    • Seema Kedar
    • D. S. Bormane
    • Sandeep Joshi
    https://doi.org/10.14419/ijet.v7i3.24.22802

    Received date: December 2, 2018

    Accepted date: December 2, 2018

    Published date: April 25, 2026

  • Handwriting Analysis, Handwriting Features, Tablet, Disease.

  • Abstract

    Background/Objectives: Handwriting is an action governed by brain like any other action. This process is usually unconscious and is closely tied to impulses from brain. Any kind of disease affects the kinetic movement and reflects in subject’s handwriting. To understand the health and mental problems, it is important to focus on how subject writes instead of what subject writes. This also makes the process of handwriting analysis independent of any language. Handwriting analysis is a pseudo-science used to study physical and behavioral characteristics of handwriting. In this paper, the general approach used for the disease diagnosis based on digital handwriting analysis has been presented. The research work carried out to diagnose diseases such as Alzheimer, Mild Cognitive Impairment, Dysgraphia, Schizophrenia, Autism, Parkinson’s disease and Mental illness based on digital handwriting analysis has been reviewed in this paper. The features related to motion, time and pressure have been used for diagnosis of disease. The experiments and results are also summarized in this paper.

  • References

    1. Cronje, Pierre E. and Hester E Roets, “Graphology in Psychological Assessment: A Diagnosis in Writing”, Universal Journal of Psychology, vol. 1, No. 4, pages: 163-168, 2013.
    2. Bora Ugurlu, Rembiye Kandemir, Aydn Carus, Ercan Abay, “An Expert System for Determining the Emotional Change on a Critical Event Using Handwriting Features”, TEM Journal, vol. 5, Issue 4, Pages 480-486, 2016.
    3. Emotions and Feelings, Available from: http://graphicinsight.co.za/emotions.htm.
    4. Handwriting analysis and health, Available from: http://www.handwriting-graphology.com/handwriting-analysis-and-health-graphopathology.
    5. Learn Graphology, Available from: http://www.handwriting-graphology.com/learn-graphology/
    6. Zainab Harbiab, Yulia Hicksa and Rossitza Setchia, “Clock Drawing Test Digit Recognition Using Static and Dynamic Features”, Procedia Computer Science, vol. 112, Issue C, September 2017, Pages 1641-1650, 2017.
    7. MMSE Test, Available from: https://www.healthdirect.gov.au/mini-mental-state-examination-mmse
    8. House Tree and Person test, Available from: https://healthfully.com/interpret-housetreeperson-test-8631546.html
    9. Xu-Yao Zhang, Guo-Sen Xie, Cheng-Lin Liu, and Yoshua Bengio, “End-to-End Online Writer Identification with Recurrent Neural Network”, IEEE Transactions on Human-Machine Systems, vol. 47, Issue: 2, 2017.
    10. Andrew Seniuk and Dorothea Blostein, “Pen Acoustic Emissions for Text and Gesture Recognition”, 10th International Conference on Document Analysis and Recognition, 2009.
    11. Laurence Likforman-Sulem, Anna Esposito, Marcos Faundez-Zanuy, Stephan Clemencon and Gennaro Cordasco, “EMOTHAW: A Novel Database for Emotional State Recognition from Handwriting and Drawing”, IEEE Transaction on Human-Machine Systems, 2017.
    12. Essentials of Machine Learning Algorithms, Available from: https://www.analyticsvidhya.com/blog/2017/09/common-machine- learning-algorithms
    13. Anova Test, Available from: http://www.statisticshowto.com/probability-and-statistics/hypothesis-testing/anova/
    14. Naive Bayes Classifier, Available from: https://en.wikipedia.org/wiki/Naive Bayes classifier
    15. K-means Algorithm, Available from: https://en.wikipedia.org/wiki/K-meansclustering
    16. Random Forest Tree Generation, Available from: http://dataaspirant.com/2017/05/22/random-forest-algorithm-machine-learing/
    17. Alzheimer’s disease, Available from: https://simple.wikipedia.org/wiki/Alzheimer’s disease
    18. Perla Werner, Sara Rosenblum, Gady Bar-On, Jeremia Heinik and Amos Korczyn, “Handwriting Process Variables Discriminating Mild Alzheimer’s Disease and Mild Cognitive Impairment”, The Journals of Gerontology: Series B, vol 61, Issue 4, Pages P228–P236, 2006.
    19. Paola Fontana, Francesca Dagnino, Leonardo Cocito and Maurizio Balestrino, “Handwriting as a gauge of cognitive status: a novel forensic tool for posthumous evaluation of testamentary capacity”, Neurol Sci., 29(4):257-61, 2008.
    20. Murad Badarna, Ilan Shimshoni, Gil Luria and Sara Rosenblum, “The Importance of Pen Motion Pattern Groups for Semi-Automatic Classification of Handwriting into Mental Workload Classes”, Cognitive Computation, vol. 10(2): 215-227, 2017.
    21. Understanding Dysgraphia By Erica Patino, Available from: https://www.understood.org/en/learningattentionissues/childlearning-disabilities/dysgraphia/understanding-dysgraphia
    22. Jiri Mekyska, Marcos Faundez-Zanuy, “Identification and Rating of Developmental Dysgraphia by Handwriting Analysis”, IEEE Transactions on Human-Machine Systems, vol. 47, Issue: 2, 2017.
    23. Sara Rosenblum, Patrice L. Weiss and Shula Parush, “Handwriting evaluation for developmental dysgraphia: Process versus product”, Kluwer Academic Publishers, 2004.
    24. Sara Rosenblum and Gideon Dror, “Identifying Developmental Dysgraphia Characteristics Utilizing Hand- writing Classification Methods”, IEEE Transactions on Human-Machine Systems, vol. 47, Issue: 2, 2017.
    25. Schizophrenia, Available from: https://faculty.washington.edu/chudler/schiz.html
    26. Michael P. Caligiuri, Hans-Leo Teulings, Charles E. Alexander B. Niculescu, James Lohr, “Handwriting Movement Analyses for Monitoring Drug-Induced Motor Side Effects in Schizophrenia Patients Treated with Risperidone”, Hum Mov Sci., vol. 28(5): 633–642, 2009.
    27. M. Borjkhani, M. Ahmadlou, F. Towhidkhah, “Extracting Reliable Handwriting Kinematic Features by Using Neural Network for Diagnosing Schizophrenia Disease”, Biomedical Engineering Conference, 2008.
    28. Crystal R. Blyler, Brendan A. Maher, Theo C. Manschreck and Wayne S. Fenton, “Line drawing as a possible measure of lateralized motor performance in schizophrenia”, Schizophrenia Research, vol. 26, Issue 1, Pages 15-23, 1997.
    29. What is Autism in Simple Terms, Available from: https://www.dealwithautism.com/whatisautisminsimpleterms/
    30. Sara Rosenblum, “Children with high-functioning autism spectrum disorder show unique handwriting patterns”, University of Haifa. Available from: https://www.sciencedaily.com/releases/2016/06/160601084649.htm.
    31. Nicci Grace, Peter Gregory Enticott, Beth Patricia Johnson, Nicole Joan Rinehart, “Do Handwriting Difficulties Correlate with Core Symptomology, Motor Proficiency and Attentional Behaviours?”, Journal of autism and developmental disorders, 2017.
    32. Azadeh Kushki, Tom Chau, Evdokia Anagnostou, “Handwriting Difficulties in Children with Autism Spectrum Disorders: A Scoping Review”, Journal of Autism and Developmental Disorders, vol. 41, Issue 12, pp 1706–1716, 2011.
    33. What is Depression, Anxiety and Stress, Available from: https://www.psychologytoday.com/basics/depression
    34. Psychology Today, Available from: https://www.psychologytoday.com/
    35. DASS Assessment, Available from: https://openpsychometrics.org/tests/DASS/
    36. Clara Rispler, Gil Luria, Allon Kahana, Sara Rosenblum, “Mood Impact on Automaticity of Performance: Handwriting as Exemplar”, Cognitive Computation, 2018.
    37. Sara Rosenblum, Perla Werner, Tal Dekel, Ilya Gurevitz and Jeremia Heinik, “Handwriting process variables among elderly people with mild Major Depressive Disorder: A Preliminary Study”, Aging Clinical and Experimental Research, vol. 22, Issue 2, pp 141–147, 2010.
    38. Parkinson’s disease, Available from: https://simple.wikipedia.org/wiki/Parkinson
    39. Michael P. Caligiuri, Hans-Leo Teulings, J. Vincent Filoteo, David Song, James B. Lohr, “Quantitative measurement of handwriting in the assessment of drug- induced parkinsonism”, Human Movement Science, vol. 25, pp 510–522, 2006.
    40. Naiqian Zhi, Beverly Kris Jaeger, “Toward Monitoring Parkinson’s through Analysis of Static Handwriting Samples”, IEEE Journal of Biomedical and Health Informatics, vol.: 21, Issue: 2, 2017.
    41. O. Tucha, L. Mecklinger, J. Thome, A. Reiter, G. L. Alders, H. Sartor, M. Naumann, and K. W. Lange, “Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease”, Journal of Neural Transmission, vol. 113, Issue 5, pp 609–623, 2006.

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  • How to Cite

    Kedar, S., S. Bormane, D., & Joshi, S. (2026). Online Analysis of Handwriting for Disease Diagnosis: A Review. International Journal of Engineering and Technology, 7(3.24), 505-511. https://doi.org/10.14419/ijet.v7i3.24.22802