Authors:
Taif Anjum
;
Steven Lawrence
and
Amir Shabani
Affiliation:
School of Computing, University of the Fraser Valley, British Columbia, Canada
Keyword(s):
Affective Computing, Social Companion Robots, Socially-Assistive Robots, Augmented Reality, Edge Computing, Embedded Systems, Deep Learning, Age-Friendly Intendent Living, Smart Home Automation.
Abstract:
The global aging population is increasing rapidly along with the demand for care that is restricted by the decreasing workforce. World Health Organization (WHO) suggests the development of smart, physical, social, and age-friendly environments will improve the quality of life for older adults. Social Companion Robots (SCRs) integrated with different sensing technologies such as vision, voice, and haptic that can communicate with other smart devices in the environment can allow for the development of advanced AI solutions towards an age-friendly, assistive smart space. Such robots require the ability to recognize and respond to human affect. This can be achieved through applications of affective computing such as emotion recognition through speech and vision. Performing such smart sensing using state-of-the-art technologies (i.e., Deep Learning) at the edge can be challenging for mobile robots due to limited computational power. We propose to address this challenge by off-loading the
Deep Learning inference to edge hardware accelerators which can minimize the network latency and privacy/cybersecurity concerns of alternative cloud-based options. Additionally, to deploy SCRs in care-home facilities we require a platform for remote supervision, assistance, communication, and technical support. We propose the use of Augmented Reality (AR) smart glasses to establish such a central platform that will allow one single caregiver to assist multiple older adults remotely.
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