Socially Assistive Robots for Older Adults and People with Autism: An Overview

Abstract

Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.

Can a humanoid social robot stimulate the interactivity of cognitively impaired elderly? A thorough study based on computer vision methods

“…Significant efforts have been aimed at improving the overall mental health of the elderly by providing them with robots for companionship. Robots, such as Pearl [8,9], Silbot [10], Mero [11], Hobbit [12][13][14], Lio [15], Kompaï [3], TIAGo [16], RAMCIP [17], Matilda [18], and more provide the elderly with assistance or services, like fulfilling daily activities, that enable them to live in their own homes and communities safely and independently [19][20][21][22][23][24]. The elderly in nursing homes have been observed to engage more frequently with various stimuli, including life-like dolls, plush animals, building blocks, sorting tasks, puzzles, and robots [25].…”

“…Researchers [26,27] investigating the effects of the physical appearance of social robots have reported that embodiment plays a vital role in their interactivity. Social assistive-robot-based systems that can perform activities, play cognitive games, and socialise, can stimulate people's cognitive, social, and physical conditions and constrain the deterioration of their cognitive state [19].…”

Value Sensitive Design to Achieve the UN SDGs with AI: A Case of Elderly Care Robots

“…All those tasks are accomplished with the use of machine learning techniques, such as Deep Learning (DL), which lets the system learn data features. Specifically, PHAROS 2.0 uses a combination of recurrent neural networks (RNN) and convolutional neural network (CNN) to identify and evaluate the physical exercises of users (Martinez-Martin et al, 2019 , 2020 ). We examine the design of the PHAROS 2.0 prototype using the framework described above (i.e., Fig.…”

Assistive Technology for Elderly Care: An Overview

“…There are several advancements in each domain that are interesting and relevant that try to tackle some previously mentioned issues. In terms of social assistants, recent academic works are [29], [30], [31], [32], [33], [34], and commercial ones are [35], [36], [37]. In terms of medical and rehabilitation assistants, recent works are [38], [39], [40], [41], [42], [43].…”