A review on the use of socially assistive robots in education and elderly care

2020 ◽  
Author(s):  
L.B. Thamil Amudhu
Keyword(s):  
Elderly Care ◽  
Assistive Robots ◽  
Socially Assistive Robots

Socially Assistive Robots in Elderly Care: A Systematic Review into Effects and Effectiveness

2012 ◽  
Vol 13 (2) ◽  
pp. 114-120.e1 ◽  
Author(s):  
Roger Bemelmans ◽  
Gert Jan Gelderblom ◽  
Pieter Jonker ◽  
Luc de Witte
Keyword(s):  
Systematic Review ◽  
Elderly Care ◽  
Assistive Robots ◽  
Socially Assistive Robots

scholarly journals Service robots: value co-creation and co-destruction in elderly care networks

2018 ◽  
Vol 29 (2) ◽  
pp. 178-205 ◽  
Author(s):  
Martina Čaić ◽  
Gaby Odekerken-Schröder ◽  
Dominik Mahr
Keyword(s):  
Elderly People ◽  
Elderly Care ◽  
Social Contact ◽  
The Elderly ◽  
Service Robots ◽  
Value Networks ◽  
Content Type ◽  
Value Network ◽  
Assistive Robots ◽  
Socially Assistive Robots

Purpose The purpose of this paper is to investigate the potential roles for service robots (i.e. socially assistive robots) in value networks of elderly care. Taking an elderly person’s perspective, it defines robot roles according to their value co-creating/destroying potential for the elderly user (i.e. focal actor), while acknowledging consequences for a network of users around the elderly (i.e. network actors). Design/methodology/approach This qualitative, interpretative study employs in-depth phenomenographic interviews, supported by generative cards activities (i.e. Contextual Value Network Mapping), to elicit an elderly person’s tacit knowledge and anticipate the effects of introducing an automated actor on institutionalized value co-creation practices. Findings The proposed typology identifies six roles of socially assistive robots in an elderly person’s value network (enabler, intruder, ally, replacement, extended self, and deactivator) and links them to three health-supporting functions by robots: safeguarding, social contact, and cognitive support. Research limitations/implications Elderly people have notable expectations about the inclusion of a socially assistive robot as a new actor in their value networks. The identified robot roles inform service scholars and managers about both the value co-destruction potential that needs to be avoided through careful designs and the value co-creation potential that should be leveraged. Originality/value Using network-conscious phenomenographic interviews before the introduction of a novel value proposition sheds new light on the shifting value co-creation interplay among value network actors (i.e. elderly people, formal and informal caregivers). The value co-creation/destruction potential of socially assistive robots and their corresponding roles in care-based value networks offer insights for the design of meaningful robotic technology and its introduction into the existing service networks.

scholarly journals Control Strategies and Artificial Intelligence in Rehabilitation Robotics

AI Magazine ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 23-33 ◽  
Author(s):  
Domen Novak ◽  
Robert Riener
Keyword(s):  
Artificial Intelligence ◽  
Control Strategies ◽  
Rehabilitation Robotics ◽  
Control Algorithms ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Rehabilitation Robots ◽  
Exercise Adaptation ◽  
Dominant Paradigm ◽  
Do So

Rehabilitation robots physically support and guide a patient's limb during motor therapy, but require sophisticated control algorithms and artificial intelligence to do so. This article provides an overview of the state of the art in this area. It begins with the dominant paradigm of assistive control, from impedance-based cooperative controller through electromyography and intention estimation. It then covers challenge-based algorithms, which provide more difficult and complex tasks for the patient to perform through resistive control and error augmentation. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.

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