Research Paper:
Optimizing Interactive Mental Learning Activity Software for Accurate Cognitive Profiling in Individuals with Down Syndrome
Irfan M. Leghari*, , Hamimah Ujir* , Syed Asif Ali** , and Irwandi Hipni*
*Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak
Kota Samarahan, Kuching, Sarawak 94300, Malaysia
Corresponding author
**Faculty of Artificial Intelligence and Mathematical Sciences, Sindh Madresatul Islam University
Hasrat Mohani Road, Karachi, Sindh 74000, Pakistan
Down syndrome is a lifelong cognitive impairment characterized by lower mental skills and intelligence quotient (IQ) compared to their typical peers. The profile is not curable. However, research has been conducted to supplement and improve cognitive functioning through computing and software applications. Conventional cognitive applications and IQ scales pose significant challenges as they are not developed based on specific cognitive guidelines. Therefore, such methods often fail to accurately assess cognitive profiling, resulting in uncertainty. To overcome these challenges, Interactive Mental Learning Activity Software utilizes tailored guidelines incorporating fuzzy logic rules, ensuring accurate cognitive profiling for Down syndrome. Fuzziness was applied within the machine learning framework across three groups structured based on IQ levels. A total of N=200 individuals with Down syndrome participated in the IQ assessment. The findings revealed that individuals with mild impairment demonstrated a higher degree of improvement in cognitive abilities compared to moderate and severe levels. However, the severe category appears to have an unrealistic probability, leading to a standstill in progress. The implementation of the specific guided system led to improvements of 6%, 5%, and 5% in individuals with mild, moderate, and severe cases, respectively.
- [1] C. Gupta et al., “Bringing machine learning to research on intellectual and developmental disabilities: Taking inspiration from neurological diseases,” J. of Neurodevelopmental Disorders, Vol.14, No.1, Article No.28, 2022. https://doi.org/10.1186/s11689-022-09438-w
- [2] D. Brkić et al., “FarmApp: A new assessment of cognitive control and memory for children and young people with neurodevelopmental difficulties,” Child Neuropsychology, Vol.28, No.8, pp. 1097-1115, 2022. https://doi.org/10.1080/09297049.2022.2054968
- [3] S. Hamburg et al., “Assessing general cognitive and adaptive abilities in adults with Down syndrome: A systematic review,” J. of Neurodevelopmental Disorders, Vol.11, No.1, Article No.20, 2019. https://doi.org/10.1186/s11689-019-9279-8
- [4] D. Hessl et al., “A solution to limitations of cognitive testing in children with intellectual disabilities: The case of fragile X syndrome,” J. of Neurodevelopmental Disorders, Vol.1, No.1, pp. 33-45, 2009. https://doi.org/10.1007/s11689-008-9001-8
- [5] D. Hessl et al., “The NIH toolbox cognitive battery for intellectual disabilities: Three preliminary studies and future directions,” J. of Neurodevelopmental Disorders, Vol.8, No.1, Article No.35, 2016. https://doi.org/10.1186/s11689-016-9167-4
- [6] R. H. Shields et al., “Validation of the NIH toolbox cognitive battery in intellectual disability,” Neurology, Vol.94, No.12, pp. e1229-e1240, 2020. https://doi.org/10.1212/WNL.0000000000009131
- [7] J. Shabbir and T. Anwer, “Artificial intelligence and its role in near future,” arXiv:1804.01396, 2018. https://doi.org/10.48550/arXiv.1804.01396
- [8] R. S. T. Lee, “Artificial Intelligence in Daily Life,” Springer, 2020. https://doi.org/10.1007/978-981-15-7695-9
- [9] S. Qazi and K. Raza, “Chapter 4: Fuzzy logic-based hybrid knowledge systems for the detection and diagnosis of childhood autism,” H. D. Jude (Ed.), “Handbook of Decision Support Systems for Neurological Disorders,” pp. 55-69, Academic Press, 2021. https://doi.org/10.1016/B978-0-12-822271-3.00016-5
- [10] F. Dernoncourt, “Fuzzy logic: Between human reasoning and artificial intelligence,” Technical Report, École Normale Supérieure, 2011.
- [11] O. L. da Cruz Netto et al., “Memorization of daily routines by children with Down syndrome assisted by a playful virtual environment,” Scientific Reports, Vol.10, No.1, Article No.3144, 2020. https://doi.org/10.1038/s41598-020-60014-5
- [12] M. K. Jha et al., “Virtual reality orientation game for Alzheimer’s disease using real-time help system,” Poc. of the 2nd Int. Conf. on Brain Function Assessment in Learning (BFAL 2020), pp. 13-23, 2020. https://doi.org/10.1007/978-3-030-60735-7_2
- [13] A. Kirijian and M. Myers, “Web fun central: Online learning tools for individuals with Down syndrome,” J. Lazar (Ed.), “Universal Usability: Designing Computer Interfaces for Diverse Users,” pp. 195-230, John Wiley & Sons, Inc., 2007.
- [14] I. Vieira et al., “Designing gamified e-learning applications for children with Down’s syndrome: The Case of Teaching Literacy and Language Skills,” Proc. of the 10th Int. Conf. on Computer Supported Education, Vol.1, pp. 102-113, 2018. https://doi.org/10.5220/0006684701020113
- [15] J. M. Ortega-Tudela and C. J. Gómez-Ariza, “Computer-assisted teaching and mathematical learning in Down syndrome children,” J. of Computer Assisted Learning, Vol.22, No.4, pp. 298-307, 2006. https://doi.org/10.1111/j.1365-2729.2006.00179.x
- [16] R. Hu et al., “Investigating input technologies for children and young adults with Down syndrome,” Universal Access in the Information Society, Vol.12, pp. 89-104, 2013. https://doi.org/10.1007/s10209-011-0267-3
- [17] J. Lazar, L. Kumin, and J. H. Feng, “Understanding the computer skills of adult expert users with down syndrome: An exploratory study,” The Proc. of the 13th Int. ACM SIGACCESS Conf. on Computers and Accessibility (ASSETS’11), pp. 51-58, 2011. https://doi.org/10.1145/2049536.2049548
- [18] J. Alammary, F. Al-Haiki, and K. Al-Muqahwi, “The impact of assistive technology on down syndrome students in Kingdom of Bahrain,” Turkish Online J. of Educational Technology, Vol.16, No.4, pp. 103-119, 2017.
- [19] A. Alfaraj and A. B. Kuyini, “The use of technology to support the learning of children with Down syndrome in Saudi Arabia,” World J. of Education, Vol.4, No.6, pp. 42-53, 2014. https://doi.org/10.5430/wje.v4n6p42
- [20] V. G. Felix et al., “A pilot study of the use of emerging computer technologies to improve the effectiveness of reading and writing therapies in children with Down syndrome,” British J. of Educational Technology, Vol.48, No.2, pp. 611-624, 2017. https://doi.org/10.1111/bjet.12426
- [21] S. Rus and A. Braun, “Money handling training – Applications for persons with Down syndrome,” 2016 12th Int. Conf. on Intelligent Environments (IE), pp. 214-217, 2016. https://doi.org/10.1109/IE.2016.48
- [22] M. C. Buzzi et al., “Personalized technology-enhanced training for people with cognitive impairment,” Universal Access in the Information Society, Vol.18, No.4, pp. 891-907, 2019. https://doi.org/10.1007/s10209-018-0619-3
- [23] I. M. Leghari et al., “Machine learning techniques to enhance the mental age of Down syndrome individuals: A detailed review,” Int. J. of Advanced Computer Science and Applications, Vol.14, No.1, pp. 990-999, 2023.
- [24] M. M. Keikhayfarzaneh, B. S. Mousavi, and J. Khalatbari, “Designing fuzzy inference system to diagnosis Down syndrome by face processing,” Int. J. of Computer Applications, Vol.24, No.3, pp. 20-28, 2011. https://doi.org/10.5120/2931-3881
- [25] H. O. Adeyemi et al., “Development of fuzzy logic-base diagnosis expert system for typhoid fever,” J. Kejuruteraan, Vol.32, No.1, pp. 9-16, 2020. https://doi.org/10.17576/jkukm-2020-32(1)-02
- [26] A. Di Nuovo et al., “Benefits of fuzzy logic in the assessment of intellectual disability,” 2014 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), pp. 1843-1850, 2014. https://doi.org/10.1109/FUZZ-IEEE.2014.6891834
- [27] Y. C. Youn et al., “Detection of cognitive impairment using a machine-learning algorithm,” Neuropsychiatric Disease and Treatment, Vol.14, pp. 2939-2945, 2018. https://doi.org/10.2147/NDT.S171950
- [28] G. R. Sinha, “Study of assessment of cognitive ability of human brain using deep learning,” Int. J. of Information Technology, Vol.9, No.3, pp. 321-326, 2017. https://doi.org/10.1007/s41870-017-0025-8
- [29] N. Paredes, E. Caicedo-Bravo, and B. Bacca, “Emotion recognition in individuals with down syndrome: A convolutional neural network-based algorithm proposal,” Symmetry, Vol.15, No.7, Article No.1435, 2023. https://doi.org/10.3390/sym15071435
- [30] S. Chattopadhyay, “A neuro-fuzzy approach for the diagnosis of depression,” Applied Computing and Informatics, Vol.13, No.1, pp. 10-18, 2017. https://doi.org/10.1016/j.aci.2014.01.001
- [31] S. Chattopadhyay, “Psyconsultant I: A DSM-IV-based screening tool for adult psychiatric disorders in Indian rural health center,” The Internet J. of Medical Informatics, Vol.3, No.1, 2006.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.