Application of Interpretive Structural Modeling on Verbal and Non-Verbal Communication Models Between Learning Companion Humanoids and Elderly Humans

Abstract

As the number of elderly people in Malaysia increases, they might feel lonely as caregivers struggle to give care and provide companionship to the elders. Loneliness can lead to mental diseases like depression and dementia and that can have a negative impact on the quality of life of both elderly people and their caregivers. Therefore, a solution to overcome this problem is by using humanoids as companions for the elders and to encourage them to gain knowledge at a later age. This study aimed to develop a model on verbal and non-verbal communication between learning companion humanoids and elderly humans.The model was produced by using Interpretive Structural Modeling (ISM) with the aid of Concept Star software that utilizes expert opinions. This method resulted in a total of 32 elements for the model and shows the relationships between them. Each element was further classified into four clusters which are Autonomous elements, linkage elements, dependent elements and independent elements, in terms of their driving and dependence powers through MICMAC analysis. This paper contributes to the Education and Artificial Intelligence fields by identifying the elements for effective verbal and non-verbal communication between humanoids and elderly humans and prioritizing them. The model will act as a guideline for successful communications between lonely elders, healthy elders and humanoids.