Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test


  • Lesia Mochurad
  • Yaroslav Hladun



tapping test, mathematical modeling, psychomotor reactions, time series, recurrent neural network


The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.


O. P. Eliseev, Determination of the Coefficient of Functional Asymmetry and Properties of the Nervous System on Psychomotor Parameters. Praktikum on psychology of the person, St. Petersburg, 2003, pp. 200-202.

E. P. Ilyin, “Methods of rapid diagnosis of the properties of the nervous system by psychomotor indicators (tapping test),” Psychological diagnosis, [Online]. Available at:

A. Jobbágy, P. Harcos, R. Karoly, G. Fazekas, “Analysis of finger-tapping movement,” Journal of Neuroscience Methods, vol. 141, pp. 29-39, 2005,

H. Jin, S. Gong, Y. Tao, et al., “A comparative study of asleep and awake deep brain stimulation robot-assisted surgery for Parkinson’s disease,” NPJ Parkinsons Dis., vol. 6, paper 27, 2020,

B. R. Ott, S. A. Ellias, M. C. Lannon, “Quantitative assessment of movement in Alzheimer’s disease,” J Geriatr Psychiatry Neurol, vol. 8, issue 1, pp. 71-75, 1995.

L. W. Welch, A. T. Cunningham, M. J. Eckardt, P. R. Martin, “Fine motor speed deficits in alcoholic Korsakoff’s syndrome,” Alcoholism, Clinical and Experimental Research, vol. 21, issue 1, pp. 134-139, 1997,

N. J. Arts, S. J. Walvoort, R. P. Kessels, “Korsakoff’s syndrome: a critical review,” Neuropsychiatr Dis Treat, vol. 13, pp. 2875-2890, 2017,

G. Giovannoni, J. van Schalkwyk, V.U. Fritz, A.J. Lees, “Bradykinesia akinesia in co-ordination test (BRAIN TEST): an objective computerised assessment of upper limb motor function,” J Neurol Neurosurg Psychiatry, vol. 67, pp. 624-629, 1999,

E. Nalçaci, C. Kalaycioğlu, M. Çiçek, & Y. Genç “The relationships between handedness and fine motor performance,” Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, vol. 37, issue 4, pp. 493–500, 2001,

L. Jancke, G. Schlaug, H. Steinmetz, “Hand skill asymmetry in professional musicians,” Brain and cognition, vol. 34, pp. 424-432, 1997,

M. Çiçek, Y. Genc, “The relationship between handedness and fine motor performance,” Cortex, vol. 37, issue 4, pp. 493-500, 2001,

D. West, V. West. “Model selection for a medical diagnostic decision support system: a breast cancer detection case”, Artif Intell Med., vol. 20, issue 3, pp. 183-204, 2000,

P. Mangiameli, D. West, R. Rampal, “Model selection for medical diagnosis decision support systems,” Decision Support Systems, vol. 36, issue 3, pp. 247-259, 2004,

Yu. A. Chistoedova, A. A. Kylosov, “Assessment and comparison of psychophysiological characteristics of athletes in different sports,” Scientific and Methodical Electronic Journal “Concept”, vol. 2, pp. 575–581, 2017. [Online]. Available at:

R. C. Deo, “Machine Learning in Medicine,” Circulation. Вasic Science for Clinicians, vol. 132, pp. 1920–1930, 2015,

J. Gao, Y. Yang, P. Lin, D. S. Park, “Computer vision in healthcare applications,” Journal of Healthcare Engineering, vol. 2018, Article ID 5157020, 2018,

A. Géron, Hands-On Machine Learning with Scikit-Learn and Tensorflow, O’Reilly, 2017.

I. Goodfellow, Y. Bengio, A Courville, Deep Learning, MIT Press, 2016, 766 p.

R. Mogull, Second-Semester Applied Statistics, Kendall/Hunt Publishing Company, 2004, 59 p.

B. Everitt, Cluster Analysis, 5-th Edition, Chichester, West Sussex, U.K: Wiley, 2011, 330 p.

E. Alpaydin, Introduction to Machine Learning, MIT Press, 2010.

L. Schmitt, “Finger-tapping test,” In: Volkmar F.R. (eds) Encyclopedia of Autism Spectrum Disorders, Springer, New York, NY, 2013,

V. Chandola, A. Banerjee, V. Kumar, “Anomaly detection: A survey,” ACM Computing Surveys, vol. 41, issue 3, pp. 1–58, 2009,

I. Izonin, R. Tkachenko, V. Verhun et al., “An approach towards missing data management using improved GRNN-SGTM ensemblemethod,” Engineering Science and Technology, an International Journal, vol. 24, issue 3, pp. 749-759, 2020,

S. I. Nikolenko, A. A. Kadurin, E. O. Arkhangelskaya, Deep Learning. Immersion in the World of Neural Networks, St. Petersburg: Peter, 2016.

Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, “A neural probabilistic language model,” J. Mach. Learn. Res., vol. 3, pp. 1137–1155, 2003.

R. C. Staudemeyer, E. Rothstein Morris, Understanding LSTM – a tutorial into Long Short-Term Memory Recurrent Neural Networks, CoRR abs/1909.09586 (2019), 42 p., arXiv:1909.09586.

S. Wu, P. Flach, A scored AUC Metric for Classifier Evaluation and Selection, 2005.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Proceedings of the International Conference ICLR, 2015, pp. 1-15.

S. Kostadinov, Understanding Backpropagation Algorithm, [Online]. Available at:

D. Hawkins, S. Basak, D. Mills, “Assessing model fit by cross–validation,” Journal of Chemical Information and Computer Sciences, vol. 43, issue 2, pp. 579–586, 2003,

X. Ying, “An overview of overfitting and its solutions,” Journal of Physics: Conference Series, vol. 1168, pp. 1-6, 2019,

Datasets of Keras library, [Online]. Available at:

L. Giancardo, A. Sánchez-Ferro, T. Arroyo-Gallego, I. Butterworth, C. S. Mendoza, P. Montero, M. Matarazzo, J. A. Obeso, M. L. Gray & R. San José Estépar, “Computer keyboard interaction as an indicator of early Parkinson's disease,” Scientific Reports, vol. 6, pp. 1-10, 2016,

Ç. Barut, E. Kızıltan, E. Gelir, F. Köktürk, “Advanced analysis of finger-tapping performance: A preliminary study,” Balkan Med J, vol. 30, issue 2, pp. 167-171, 2013,




How to Cite

Mochurad, L., & Hladun, Y. (2021). Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test. International Journal of Computing, 20(2), 190-200.