Wing-Yue Geoffrey Louie

Acceptance and Attitudes Toward a Human-like Socially Assistive Robot by Older Adults

Recent studies have shown that cognitive and social interventions are crucial to the overall health of older adults including their psychological, cognitive, and physical well-being. However, due to the rapidly growing elderly population of the world, the resources and people to provide these interventions is lacking. Our work focuses on the use of social robotic technologies to provide person-centered cognitive interventions. In this article, we investigate the acceptance and attitudes of older adults toward the human-like expressive socially assistive robot Brian 2.1 in order to determine if the robot’s human-like assistive and social characteristics would promote the use of the robot as a cognitive and social interaction tool to aid with activities of daily living. The results of a robot acceptance questionnaire administered during a robot demonstration session with a group of 46 elderly adults showed that the majority of the individuals had positive attitudes toward the socially assistive robot and its intended applications.

Brian 2.1: A socially assistive robot for the elderly and cognitively impaired

As the worlds elderly population continues to grow, so does the number of individuals diagnosed with cognitive impairments. It is estimated that 115 million people will have age-related memory loss by 2050 [1]. The number of older adults who have difficulties performing self-care and independent-living activities increases significantly with the prevalence of cognitive impairment. This is especially true for the population over 70 years of age [2]. Cognitive impairment, as a result of dementia, severely affects a persons ability to independently initiate and perform daily activities, as cognitive abilities can be diminished [3]. If a person is incapable of performing these activities, continuous assistance from others is necessary. In 2010, the total worldwide cost of dementia (including medical, social, and informal care costs) was estimated to be US

A focus group study on the design considerations and impressions of a socially assistive robot for long-term care

604 billion [1].

Playing a memory game with a socially assistive robot: A case study at a long-term care facility

As older adults age, they are more likely to reside in long-term care facilities due to the decline in cognitive and/or physical abilities that prevent them from living independently. With a rapidly aging population there is an increasing demand on long-term care facilities to care for older adults. Such facilities need to provide medical services, assistance in activities of daily living, and scheduled leisure activities to improve health and quality of life. However, as the need for long-term care is increasing, the care workforce is faced with decreasing numbers of healthcare staff and high turnover rates. Our research focuses on the design of socially assistive robots to plan, schedule, and facilitate social and cognitive interventions for residents in long-term care facilities. In this paper, we investigate the specific design considerations and the impressions of long-term care residents, healthcare professionals, and family members on a socially assistive robot designed to autonomously facilitate cognitively and socially stimulating leisure activities. Thematic analysis of focus group sessions conducted at a long-term care facility with the aforementioned individuals revealed important design considerations for the development and integration of a socially assistive robot in long-term care facilities.

A victim identification methodology for rescue robots operating in cluttered USAR environments

Studies have shown that cognitive and social stimulation is crucial to the overall health of older adults including psychological, cognitive and physical well-being. However, activities to promote such stimulation are often lacking in long-term care facilities. Our work focuses on the use of social robotic technologies to provide person-centered cognitive interventions. Namely, this paper presents an HRI study with the unique human-like socially assistive robot Brian 2.1, in order to investigate the use and acceptability of the expressive human-like robot by older adults living in a longterm care center. Current studies with social robots for the elderly have been mainly directed towards collecting data on the acceptance and use of animal-like robots. Herein, we aim to determine if the robots human-like assistive and social characteristics result in the elderly having positive attitudes towards the robot as well as accepting it as an interactive cognitive training tool.

A user-study with Tangy the Bingo facilitating robot and long-term care residents

Our research focuses on developing an automated victim identification methodology for rescue robots in order to aid robot operators with the complex and stressful task of searching for victims in cluttered urban search and rescue (USAR) environments. In this paper, we present an approach that utilizes 2D and 3D sensory information from a real-time 3D sensory system for robust victim identification using both human geometric and skin region features. Our technique, uniquely, allows for the identification of partially occluded victims and single body parts that may be visible in cluttered USAR scenes using a Support Vector Machine-based classifier based on the aforementioned features. Unlike other approaches that focus on the recognition of one specific body part (such as the head) or the recognition of a small set of fixed body poses, we aim to identify multiple different body parts in a number of varying configurations to increase recognition rate. Experimental results illustrate the robustness of our methodology to find human victims in a variety of different poses in a rubble-filled USAR-like scene and its ability to potentially reduce operator workload.

A learning from demonstration system architecture for robots learning social group recreational activities

Cognitive decline among the elderly decreases their independence and quality of life. Promoting engagement in recreational activities can help reduce this decline as such activities can provide both social and cognitive stimulation. For example, Bingo is a popular recreational activity in long-term care (LTC) facilities. However, activities such as Bingo have significant time and personnel requirements, and are becoming increasingly difficult to facilitate due to the current LTC staff shortages and an increasing demand for other LTC services. To address this problem, our research focuses on the development of the autonomous socially assistive robot Tangy which is being designed to facilitate needed multi-user recreational activities. In this paper, we present a pilot study conducted with Tangy facilitating multiple Bingo sessions with groups of elderly residents at a LTC facility. The study results showed that Tangy was able to autonomously and effectively facilitate Bingo games in real interaction settings by determining its appropriate assistive behaviors. Residents also had high compliance and engagement rates with respect to Tangy and the Bingo games. A post-interaction questionnaire showed that they enjoyed playing Bingo with Tangy, liked Tangys socially interactive attributes, and would interact with it again in the future.

Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor

Group-based recreational activities have shown to have a number of health benefits for people of all ages. The handful of social robots designed to facilitate such activities are currently only able to implement a priori known recreational activities that have been pre-programmed by human experts. Once deployed in their intended facility, these robots are not able to learn new activities from non-expert humans. In this paper, we present the development of a novel learning from demonstration (LfD) system architecture for a social robot in order for it to learn from non-expert teachers the structure of an activity and monitor the execution of the new activity. In order to obtain user compliance, personalized persuasive strategies are also learned by the robot to use while implementing the activity during human-robot interactions (HRI) with the intended users. The architecture has been integrated into our socially assistive robot Tangy to learn the group-based activity Bingo. System performance experiments were conducted with Tangy to first learn to facilitate Bingo from non-expert teachers and then use the learned activity to physically facilitate Bingo with multiple users. The results showed Tangy was able to effectively and efficiently learn the new Bingo activity structure as well as personalize its persuasive strategies to individual users in order to obtain activity compliance.

An autonomous assistive robot for planning, scheduling and facilitating multi-user activities

To effectively interact with people, social robots need to perceive human behaviors and in turn display their own behaviors using social communication modes such as gestures. The modeling of gestures can be difficult due to the high dimensionality of the robot configuration space. Imitation learning can be used to teach a robot to implement multi-jointed arm gestures by directly observing a human teacher’s arm movements (for example, using a non-contact 3D sensor) and then mapping these movements onto the robot arms. In this paper, we present a novel imitation learning system with robot self-collision awareness and avoidance. The proposed method uses a kinematical approach with bounding volumes to detect and avoid collisions with the robot itself while performing gesticulations. We conducted experiments with a dual arm social robot and a 3D sensor to determine the effectiveness of our imitation system in being able to mimic gestures while avoiding self-collisions.