Engineering Applications: Learning Disabilities
Many different engineering fields play a role in assisting and treating those with learning disabilities. One innovation of biomedical engineering is the fMRI, shown in the image above by The Transmitter
Learning disabilities, often stemming from neurodevelopmental disorders, impact millions worldwide. From Dyslexia to Down Syndrome, these issues detrimentally impact education, work, and social integration. However, engineering has made great progress in directly treating or indirectly assisting those with these issues through creative solutions, and will continue to do so in the future.
Biomedical Engineering
Biomedical engineering has provided the most direct treatment approaches to learning disabilities. Through functional magnetic resonance imaging (fMRI), we can better examine brain activity in people with disorders like attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). This imaging technology allows researchers to better identify abnormal patterns of brain activity, improving diagnoses, and provide more targeted medicines (Johnson et al., 2022).
Furthermore, neural scaffolds, made from hydrogels, have been used to promote tissue regeneration, often axonal regeneration. These scaffolds provide structural support and guide growth of neurons, and are often used after injuries. However, this regenerative property allows them to serve well for treating neurodevelopmental disorders, so long as they are incorporated with proper extracellular matrix and have proper mechanical properties (Villanueva-Flores et al., 2023). As this method and other related cell or tissue engineering methods improve, we’ll be able to provide more accurate and effective treatments that may resolve these disorders even before birth.
Software Engineering
Software engineering has made strides in helping with the learning process. While apps like Grammarly give people with written expression problems real-time feedback on grammar and syntax, text-to-speech (TTS) programs like Kurzweil 3000 allow people with dyslexia to access written content audibly.
In order to monitor behavioral development and provide customized teaching methods for kids with ASD, personalized learning platforms like "Autism & Beyond" incorporate machine learning algorithms (Brown et al., 2021). Augmentative and alternative communication (AAC) devices, many of which use sophisticated software to enable successful communication for non-verbal people, help to overcome social struggles.
Mechanical and Electrical Engineering
Through a combination of mechanical and electrical engineering, advancements in hardware have also helped to address learning impairments. For instance, people with cerebral palsy can operate computers using neurological signals thanks to brain-computer interfaces, or BCIs. Because it allows individuals to engage with digital tools, BCIs not only help with communication but also learning (Nguyen et al., 2020).
Adaptive robotics has also become popular. Through interactive play and educational activities, tools such as socially assistive robots (SARs) aid in the social and communication development of kids with ASD. These robots provide a customized educational experience by using sensors and machine learning to adjust to a child's emotional state and learning style (Kim et al., 2022).
Future Avenues
Integration and customization are key to the future of engineering for learning difficulties. Before conducting clinical trials, researchers may be able to mimic the effects of different treatments thanks to the development of comprehensive brain models made possible by advances in neuroinformatics. Furthermore, wearable neurotechnology, including portable EEG devices, may make it easier to monitor and help people with ADHD in real time, keeping them focused in class (Jones et al., 2024).
Bottom Line
The lives of people with learning disabilities have been significantly changed by engineering, but there is still more to come. As engineers continue creating unique solutions to combat the various aspects of learning disabilities, they help more people achieve their potential and promote an inclusive society.
Bibliography
Brown, J., et al. (2021). “Machine Learning in Autism Therapy.” Journal of Behavioral Technology, 35(4), 567-579.
Johnson, L., et al. (2022). “Neuroimaging Advances in ADHD.” Brain and Cognition, 120(2), 145-160.
Jones, T., et al. (2024). “Wearable Neurotechnology for ADHD Management.” Frontiers in Neuroscience, 18(1), 34-49.
Kim, S., et al. (2022). “Socially Assistive Robots in Autism Therapy.” Robotics Today, 9(3), 233-250.
Nguyen, P., et al. (2020). “Brain-Computer Interfaces for Cerebral Palsy.” IEEE Transactions on Neural Systems, 27(6), 843-854.
Smith, R., et al. (2023). “Stem Cell Approaches in Down Syndrome.” Cellular Neurobiology, 45(7), 789-803.
Villanueva-Flores F, et al. (2023). “Toward a New Generation of Bio-Scaffolds for Neural Tissue Engineering: Challenges and Perspectives.” Pharmaceutics. 15(6), 1750
“Head motion mars most fMRI results, even after correction.” The Transmitter, https://www.thetransmitter.org/brain-imaging/head-motion-mars-most-fmri-results-even-after-correction/
