The integration of personalized deep learning in autism therapy will enable clinics to reach better outcomes.
FREMONT, CA – The emergence of artificial intelligence (AI) has revolutionized the healthcare sector, empowering enhanced treatment for a range of conditions, including autism. Individuals who have autism often find it challenging to distinguish the emotional states of others. As a remedy, several healthcare providers across the world are leveraging robots for autism therapy, empowering autistic children to recognize emotions and respond to them appropriately.
The therapy requires robots with robust capabilities, which enable them to interpret the behavior of patients seamlessly. Recently, researchers at the MIT Media Lab developed a personalized machine learning (ML) technology which enables clinical robots to determine the engagement and interest of the children during their interactions. The perception drawn by the robots equipped with the personalized deep learning network showed a correlation of up to 60 percent with the assessments by human experts.
Human observers often find it challenging to reach a higher agreement regarding the engagement and behavior of children. However, the use of trained robots will deliver more consistent behavioral estimates in autistic children. The purpose of the personalized robots is to augment the capabilities of therapists by providing them with vital information which can enhance the treatment. The deep learning models will enable a more natural and engaging interaction between robots and children.
One of the challenges hindering the use of AI in autism therapy is the vast requirement of data. Since autism is a heterogenic field, with each patient having unique behavioral patterns, the conventional AI approaches often fail. Personalized deep learning will also find potential use cases in pain monitoring and the forecasting of Alzheimer's disease progression.
The robot-assisted autism therapy approach comprises a human therapist who shows photos and flashcards with different facial expressions to the child, each representing various emotions, including fear, sadness, and joy. The robot is programmed to show the same feelings and engage with the child, whose behavior provides vital feedback to the therapist to augment their treatment.
The concept of deep learning has been around for several decades, but the evolution in robust computing technologies has enabled its development. The technology is already being used in automatic speech and object-recognition programs and is ideal for identifying facial features and recognizing behaviors in children. If any technology can take autism therapy and treatment to the next level, it is personalized deep learning.