Abstract
Our study’s aim is two-fold. Firstly, to assess the technical capabilities of digital tablets with digital pen inputs to establish their suitability as data collection equipment for use in screening for Mild Cognitive Impairment (MCI). Secondly, to test such equipments’ usability in clinical settings by test subjects who are over 65 years of age and would be the typical participants in such screening tests. Before we started to analyze the fine motor movement of older people in order to diagnose the motor movement-based symptoms of Alzheimer’s disease and MCI, we had to gain experience in data collection in this age group. Our goal was to check the quality of the data collected with different devices from older people. First, data was collected using our standard measurement protocol and also we collected real-life handwritten signatures. The collected “in vitro” and “in vivo” data were analyzed. In the second part of the research, we asked older people to solve different writing and drawing tasks on certain digital devices that are able to collect data about their hand motor movement. We found that every device had pros and cons. Overall, the data we collected with them were good quality and provided a good basis for further research. We have also established that the use of such tablet devices to collect data did not pose any usability challenges for participants.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bowie, C.R., Harvey, P.D.: Administration and interpretation of the Trail Making Test. Nat. Protoc. 1(5), 2277–2281 (2006)
Griechisch, E., Ward, J.R., Hanczár, G.: Anomalies in measuring speed and other dynamic properties with touchscreens and tablets. In: 2019 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–6, September 2019
Griechisch, E., Malik, M.I., Liwicki, M.: Online signature verification based on Kolmogorov-Smirnov distribution distance. In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2014, pp. 738–742. IEEE (2014). https://doi.org/10.1109/ICFHR.2014.129
Hawkins, K.M., Sergio, L.E.: Visuomotor impairments in older adults at increased Alzheimer’s disease risk. J. Alzheimers Dis. 42(2), 607–621 (2014)
Mollica, M.A., et al.: Subtle visuomotor difficulties in preclinical Alzheimer’s disease. J. Neuropsychol. 11(1), 56–73 (2017)
Muramatsu, D., Matsumoto, T.: Effectiveness of pen pressure, azimuth, and altitude features for online signature verification. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 503–512. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_53
NeuroScript: testing the accuracy of pen tablets. www.tinyurl.com/tablet-test-neuroscript. Accessed 25 Apr 2022
Okawa, M., Yoshida, K.: Offline writer verification based on forensic expertise: analyzing multiple characters by combining the shape and advanced pen pressure information. J. Jpn. Soc. Forensic Sci. Technol. 22(2), 61–75 (2017)
Phillips, M.: Several simple tests can help you choose the correct digitizer. Comput. Technol. Rev. 7(1) (1987)
Tippett, W.J., Sergio, L.E.: Visuomotor integration is impaired in early stage Alzheimer’s disease. Brain Res. 1102(1), 92–102 (2006). https://doi.org/10.1016/j.brainres.2006.04.049. www.sciencedirect.com/science/article/pii/S0006899306010936
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Ortega-Garcia, J.: Feature-based dynamic signature verification under forensic scenarios. In: 3rd International Workshop on Biometrics and Forensics (IWBF 2015), pp. 1–6 (2015). https://doi.org/10.1109/IWBF.2015.7110241
Ward, J.R., Phillips, M.J.: Digitizer technology: performance characteristics and the effects on the user interface. IEEE Comput. Graph. Appl. 7(4), 31–44 (1987). https://doi.org/10.1109/MCG.1987.276869
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Hanczár, G., Griechisch, E., Molnár, Á., Tóth, G. (2022). Motor Movement Data Collection from Older People with Tablets and Digital Pen-Based Input Devices. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-19745-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19744-4
Online ISBN: 978-3-031-19745-1
eBook Packages: Computer ScienceComputer Science (R0)