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An improved customized CNN model for adaptive recognition of cerebral palsy people’s handwritten digits in assessment

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Abstract

Cerebral palsy (CP) is used to describe a group of disorders, characterized by non-progressive, but permanent damage to the developing brain that results in motor deficits, functional difficulties, sensory impairments, and cognitive impairments. AI is rapidly developing with special importance to education and healthcare. Interdisciplinary research perspectives and applications have come up as a helping hand to meet out the needs in different paradigm of Assessment and Biomedicine. At this juncture, building assessment tools for people with motor coordinate impairment is a valuable yet novel research challenge. So, authors implement a handwritten digit recognition system using optical character recognition (OCR) on dataset of CP people’s handwritten digits and based on study the problem statement is defined to detect variation in jerking hands on written to optical digits by proposing a custom convolutional neural network (CNN) architecture on the augmented dataset (CP handwritten data). The proposed research will look into a variety of design preferences for CNN-based handwritten digit recognition, including kernel size, layer count, stride size, and dilation. In addition, it evaluated the confusion matrix for maximum number prediction using various optimization algorithms. The extensive trials were conducted and obtained 85% accuracy for the CP dataset and 97% for the MNIST dataset.

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Data will be shared on reasonable request.

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Acknowledgements

We thank Mr. C. Shanthakumar (Director, The Spastics Society of Tiruchirappalli, Tamil Nadu, India.) for providing permission for data collection. We thank Mr. B. Balachandar (physiotherapist, The spastics Society of Tiruchirapalli, Tamil Nadu, India.) and his team for giving information about cerebral palsy people. We also thank all CP people who gave their time and effort for enabling data collection and their parents and special teachers.

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Authors and Affiliations

  1. Machine Learning and Data Analytics Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

    K. Muthureka & U. Srinivasulu Reddy

  2. Centre of Excellence (CoE) in Artificial Intelligence, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

    K. Muthureka & U. Srinivasulu Reddy

  3. Information Processing Lab, Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

    B. Janet

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  1. K. Muthureka
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Contributions

KM collected data, developed core idea, implemented the model and conducted experiments, and wrote the entire manuscript. USR analyzed and verified the results, and reviewed the manuscript. BJ verified the results and reviewed the manuscript.

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Correspondence to U. Srinivasulu Reddy.

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Hereby, I Dr U Srinivasulu Reddy consciously assure that the manuscript entitled “An Improved custom CNN model for adaptive Recognition of Cerebral Palsy People’s Handwritten Digits in Assessment” is not currently being considered for publication elsewhere.

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Muthureka, K., Srinivasulu Reddy, U. & Janet, B. An improved customized CNN model for adaptive recognition of cerebral palsy people’s handwritten digits in assessment. Int J Multimed Info Retr 12, 23 (2023). https://doi.org/10.1007/s13735-023-00291-8

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  • DOI: https://doi.org/10.1007/s13735-023-00291-8

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