Does elderly enjoy playing Bingo with a robot? A case study with the humanoid robot Nadine

There are considerable advancements in medical health care in recent years, resulting in rising older population. As the workforce for such a population is not keeping pace, there is an urgent need to address this problem. Having robots to stimulating recreational activities for older adults can reduce the workload for caretakers and give them time to address the emotional needs of the elderly. In this paper, we investigate the effects of the humanoid social robot Nadine as an activity host for the elderly. This study aims to analyse if the elderly feels comfortable and enjoy playing game/activity with the humanoid robot Nadine. We propose to evaluate this by placing Nadine humanoid social robot in a nursing home as a caretaker where she hosts bingo game. We record sessions with and without Nadine to understand the difference and acceptance of these two scenarios. We use computer vision methods to analyse the activities of the elderly to detect emotions and their involvement in the game. We envision that such humanoid robots will make recreational activities more readily available for the elderly. Our results present positive enforcement during recreational activity, Bingo, in the presence of Nadine.

Can a Humanoid Robot be part of the Organizational Workforce? A User Study Leveraging Sentiment Analysis

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.