**2. Related Work**

Over the years, several categories of robot technologies have been proposed to support elderly people [9,10,20]. However, there are only two basic categories. One is robot technologies that physically support humans. These technologies include smart wheelchairs [21,22], prosthetic hands, and exoskeletons [23,24]. With these technologies, robots are considered as tools for extending the human body rather than as independent agents. The other is robot technologies that socially support humans. This category is further divided into two subcategories. One is robot technologies that support daily life tasks, such as medication and task schedule management [17,25,26], monitoring (fall detection) [27,28], household tasks [29,30], and shopping support [31]. The other subcategory is robot technologies that support health maintenance and psychological well-being improvement, such as pet-type robots (Paro) [32], conversational robots (including computer agents), AIBO [33,34], and NeCoRo [35]. Note that this categorization is formal. In fact, robot systems that belong to both categories have also been proposed. For example, there have been wheelchairs that talk to elderly people who ride them [22] and robots that chat while shopping [31].

In this study, we review past studies of robot technologies for supporting the elderly through conversations. A mobile robotic assistant, Pearl [25], provided a reminder of the elderly daily activities, such as to visit the toilet every three hours and also to take medication. Regarding Pearl, a caregiver for an elderly client had input his/her daily activities in advance, and Pearl reminded the person based on the schedule. MonAMI Reminder [26] was also a schedule management assistant that allowed users to register their own schedule. Due to the difficulty in speech recognition, the input to the agent was provided with a digital pen and paper. Reminders were output, by voice, through embodied agents on a device. These robots and the agent have not been evaluated from the viewpoint of quality of a conversation. Ryan was a conversation robot for elderly people with dementia and depression [36]. Ryan has been developed by DreamFace Technologies, LLC. This robot has a head projection system that displays an animated avatar onto a mask and can show an emotive and expressive face. The robot has a touch-screen interface on its torso, which can be used as music player, narrated photo album, and video player. Furthermore, the robot can play cognitive games with a user using the screen and remind the user of daily activities with simple chats. Abodollahi et al. [36] installed this robot in a room for elderly participants and asked them to live together for 4 to 6 weeks. As a result, the average one-turn conversation between a participant and the robot was 198 times per day, which is relatively large. However, this paper does not show specific results, such as examples of dialogues, duration of a dialogue, and accuracy of speech recognition. Therefore, the quality of a dialogue was quite unclear.

A virtual assistant, Billy ('Billie'), to accompany and guide the elderly throughout the day was developed [17,37]. Billy basically performs schedule management like the MonAMI Reminder. However, with Billy, all inputs and outputs can be done through spoken-dialogue and natural confirmation signals like nodding or non-lexical cues. Furthermore, Billy can provide suggestions for leisure activities depending on the user. In their study, they took into account that elderly people seldom speak clearly, they cannot understand high-density dialogue, and they cannot perform turn-taking well. In view of this, they developed a robust and reliable interaction design called social cooperative behavior for the schedule management system. Although field trials started, the specific results have not yet been reported. Their system was sophisticated in terms of schedule management. However, with regards to achieving a conversation for a long while, it appeared to have low conversation support.

For robots specialized in assisting physical or cognitive activities, Ifbot could provide some activity programs such as Japanese language quizzes, singing songs, mouth exercise, and arithmetic. Although they used speech recognition in the programs, they said; "Almost all participants may have been dissatisfied with the robot's speaking and voice recognition functions." [38]. Matilda is a human-like (in appearance and attributes; e.g., voice, expressions, gestures, emotions) assistive communication robot (service and companion) in nursing homes in Australia [39]. Although users' impression of Matilda was assessed through a field trial in the nursing homes (Australia), the dialogue between elderly participants and the robot was not mentioned. Minami et al. [40] developed a dialogue robot system that chats with elderly people watching TV. This robot could provide responses by extracting social media comments related to TV programs. In addition to this function, the robot improves users' dialogue experience such as backchannel and repetition. However, the study did not evaluate the system with elderly people, and it did not consider dialogue breakdown due to speech recognition failures. Otaki et al. [41] developed a robot system that supported the co-imagination method for elderly people. With this robot system, elderly people talk to each other with a specified theme while looking at photos taken in their early lives. This method is designed to train the cognitive functions, which especially decline with aging, at an early stage of dementia [42]. The robot acts as a moderator of the conversation. Because the robot was remote-controlled by an operator, it is unclear how successful robot moderation between elderly participants is under the situation of frequent speech recognition failures. Sakakibara et al. [43] proposed a system that dynamically generated dialogue scenarios for counseling patients with dementia. In the study, personalized conversations were generated using the history obtained in conversation and linked open data. However, they did not care about the

situation of speech recognition failures and the system had not yet been evaluated. Jokinen proposed constructive dialogue models [44] and an architecture based on the model [45] for socially intelligent robots. A robot with this architecture can be aware of potentially interesting topics and of the user's attention, interest, and understanding through multimodal signals. Dialogue content is managed by topic tracking and anticipating possible continuations, calculated by coherence measures using the semantic distance between possible topics. With this architecture, Jokinen et al. [45] developed an application, using a robot assistant that instructed the human user on various task procedures related to elder care support services. However, the architecture is not applied to engage in dialogues with the elderly and is not concerned with speech recognition failures.

Through the above review, past studies of social assistive robots for elderly people appear to have paid little attention to speech recognition failure. They have not evaluated for how long dialogues with elderly participants had been continued. In contrast to the past studies, this paper focuses on developing and evaluating a robot dialogue system that can sustain a dialogue even in a situation where speech recognition frequently fails.
