Cognitive functioning that affects user behaviors is an important factor to consider when designing interactive systems for the elderly, including emerging voice-based dialogue systems such as smart speakers and voice assistants. Previous studies have investigated the interaction behaviors of dementia patients with voice-based dialogue systems, but the extent to which age-related cognitive decline in the non-demented elderly influences the user experiences of modern voice-based dialogue systems remains uninvestigated. In this work, we conducted an empirical study in which 40 healthy elderly participants performed tasks on a voice-based dialogue system. Analysis showed that cognitive scores assessed by neuropsychological tests were significantly related to vocal characteristics, such as pauses and hesitations, as well as to behavioral differences in error-handing situations, such as when the system failed to recognize the user's intent. On the basis of the results, we discuss design implications towards the tailored design of voice-based dialogue systems for ordinary older adults with age-related cognitive decline.

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Abstract. Micro-tasking (e.g., crowdsourcing) has the potential to help "longtail" senior workers utilize their knowledge and experience to contribute to their communities. However, their limited ICT skills and their concerns about new technologies can prevent them from participating in emerging work scenarios. We have devised a question-answer card interface to allow the elderly to participate in micro-tasks with minimal ICT skills and learning efforts. Our survey identified a need for skill-based task recommendations, so we also added a probabilistic skill assessment model based on the results of the micro-tasks. We also discuss some scenarios to exploit the question-answer card framework to create new work opportunities for senior citizens. Our experiments showed that untrained seniors performed the micro-tasks effectively with our interface in both controlled and realistic conditions, and the differences in their skills were reliably assessed.

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Background The rising number of patients with dementia has become a serious social problem worldwide. To help detect dementia at an early stage, many studies have been conducted to detect signs of cognitive decline by prosodic and acoustic features. However, many of these methods are not suitable for everyday use as they focus on cognitive function or conversational speech during the examinations. In contrast, conversational humanoid robots are expected to be used in the care of older people to help reduce the work of care and monitoring through interaction. Objective This study focuses on early detection of mild cognitive impairment (MCI) through conversations between patients and humanoid robots without a specific examination, such as neuropsychological examination. Methods This was an exploratory study involving patients with MCI and cognitively normal (CN) older people. We collected the conversation data during neuropsychological examination (Mini-Mental State Examination [MMSE]) and everyday conversation between a humanoid robot and 94 participants (n=47, 50%, patients with MCI and n=47, 50%, CN older people). We extracted 17 types of prosodic and acoustic features, such as the duration of response time and jitter, from these conversations. We conducted a statistical significance test for each feature to clarify the speech features that are useful when classifying people into CN people and patients with MCI. Furthermore, we conducted an automatic classification experiment using a support vector machine (SVM) to verify whether it is possible to automatically classify these 2 groups by the features identified in the statistical significance test. Results We obtained significant differences in 5 (29%) of 17 types of features obtained from the MMSE conversational speech. The duration of response time, the duration of silent periods, and the proportion of silent periods showed a significant difference (P<.001) and met the reference value r=0.1 (small) of the effect size. Additionally, filler periods (P<.01) and the proportion of fillers (P=.02) showed a significant difference; however, these did not meet the reference value of the effect size. In contrast, we obtained significant differences in 16 (94%) of 17 types of features obtained from the everyday conversations with the humanoid robot. The duration of response time, the duration of speech periods, jitter (local, relative average perturbation [rap], 5-point period perturbation quotient [ppq5], difference of difference of periods [ddp]), shimmer (local, amplitude perturbation quotient [apq]3, apq5, apq11, average absolute differences between the amplitudes of consecutive periods [dda]), and F0cov (coefficient of variation of the fundamental frequency) showed a significant difference (P<.001). In addition, the duration of response time, the duration of silent periods, the filler period, and the proportion of fillers showed significant differences (P<.05). However, only jitter (local) met the reference value r=0.1 (small) of the effect size. In the automatic classification experiment for the classification of participants into CN and MCI groups, the results showed 66.0% accuracy in the MMSE conversational speech and 68.1% accuracy in everyday conversations with the humanoid robot. Conclusions This study shows the possibility of early and simple screening for patients with MCI using prosodic and acoustic features from everyday conversations with a humanoid robot with the same level of accuracy as the MMSE.

Information technologies (IT) have great potential to improve the everyday lives of senior citizens, but their lack of skills prevents them from exploiting the possibilities. Skilled seniors can effectively teach unskilled seniors based on their deep understanding of the barriers and needs of their generation, but skilled seniors are scarce. Distant learning methods could be a solution, but their unfamiliarity with IT and strong preference for face-to-face learning are challenges. To make the distant course closer to the face-to-face experience, we developed a remote education system with real-time gesture visualization integrated with multiple audio and video streams between the teachers and learners. In this paper, we will introduce our remote course system and the course design, and then report results of the trial remote course approach.