Author:
Frédéric Rayar
Affiliation:
LIFAT, University of Tours, Tours, France
Keyword(s):
Object Detection, YOLOv7 Model, Task Generation, Assistive Technology, Intellectual Disability.
Abstract:
People suffering from Intellectual Disability (ID) face challenges to perform sequential tasks in their daily life, impacting their education and employability. To help them, the usage of Assistive Technology (AT) on mobile devices is a promising direction. However, most of them do not take advantage of the important recent advances in Artificial Intelligence. In this paper, we address this lack by presenting a prototype of an embed AT system that leverage computer vision advances to assist a user suffering from ID to perform sequential tasks in a guesthouse rooms’ tiding-up activity. It first relies on a state-of-the-art object detector, namely YOLOv7, that we have adapted for a real-time usage on mobile devices. Then, using a ”spot the difference” approach, it identifies objects of interest that are either present, absent or displaced, compared to a template image. A list of tasks to be achieved is automatically generated and conveyed to the user using an accessible and ergonomic i
nterface. Early qualitative experiments of this ongoing work lead us to believe that our contribution could improve the life of people suffering from ID, allowing them to improve both their functioning and independence.
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