In this study, we aim to realize an assistive technology that can present necessary information to elderly people with cognitive concerns or dementia in a way that is adaptable to their home life. To achieve this goal, we propose ALPS (Assisted Living by Personalized Speaker), a system that presents appropriate information according to various locations and times in the home. We installs IoT speakers with motion sensors at key locations in the home, and by linking them to ECA(Event-Condition-Action) rules in the cloud, ALPS provides information based on location and time in voice. We implemented a prototype of the proposed ALPS and conducted a case study of two elderly people. As a result, it was found that by defining ECA rules for each problem, the system can present information according to the individual’s lifestyle.

Currently, Japan is entering a super-aged society. It is difficult for the elderly at home to deal with their problems in their lives by self aid and mutual aid. So this study aims to propose and implement Compass4SL, a problem and solution sharing service for the elderly at home. We design and implement the system according to the following steps. First, we find the use cases and entities. Then, we design the overall system structure with a layered architecture. Finally, we implement the system as a Web application based on the obtained design using Java and Spring Boot framework.

To support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based monitoring, the daily recording and externalization of mental states. In the previous research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, a chatbot asks a user specific questions to acquire his/her mental state. Based on the answers, the service assesses the mental state. In this research, we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice to improve the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmed that the weekly feedback encouraged self-care consciousness and increased motivation of subjects.

To support in-home long-term care, we are studying techniques of Mind Sensing, which externalizes internal states of elderly people as words through conversations with agents or robots. We have previously developed a prototype system of Mind Sensing, integrated with an activity recognition system and an LINE chatbot. However, the system was tightly coupled with the fixed systems, it was difficult to add or change the setting of questions from the chatbot to individual elderly people.

In order to support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, as well as to encourage their ability of self-care. However, the technical challenges include the limitations on human interventions and sensor-based monitoring, as well as daily recording and externalization of mental states. In this research, we propose Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, an agent asks a user specific questions to acquire his/her mental state. Based on the answers, the service then assesses the mental state and sends feedback. We implement a prototype service, and evaluate the feasibility of the service through a preliminary experiment. The results show that data characterizing mental states of individual subjects was obtained successfully, and that some subjects externalized their minds by feedback from the service.

In recently years, many care robots have received a lot of attention to help elderly people. However existing care robots have difficult to adopt personalization. For instance, some programmers have to customize robot program to meet needs of each elderly. Even if a care robot which has a feature of machine learning, it takes a long time to learn a preference for each elderly. In this paper, our goal is to deploy a smart care service integration agent that provides a personalization and integration for each elderly people. Our proposed service consists of three essential components, Virtual Care Giver (VCG), Virtual Care Personalizer (VCP) and Care Template. VCG is a robot agent, where executes care tasks in each home. The VCG is offered care tasks based on care template which Virtual Care Personalizer (VCP) generates. Virtual Care Personalizer (VCP) manages and generates personalization of care tasks the on cloud. Moreover, we deploy Care Template on the cloud which enables to provide the basic care tasks. To demonstrate the feasibility, we consider three kinds of usecase scenarios for two persona people.

A number of reminder systems have been developed to help elderly people with dementia. However, the existing reminder systems lack the awareness of human context, the sympathetic human-machine interaction, and the flexibility of personal adaptation. To cope with the limitations, we are currently studying a new reminder service for people with dementia. Specifically, we exploit a BLE-based indoor positioning system to capture the current location and context of the patient. We then use a virtual agent system for rich interactions. Finally, we develop a schedule management system for personalized reminders. To integrate these heterogeneous systems, we re-design and deploy the systems as three services with Web-API: Location Service, Agent Service, and Schedule Service. These services are loosely integrated by Coordinator Service, based on the service-oriented architecture, In this paper, we first present the system architecture, and then discuss the key idea to implement the services. We also demonstrate “reminder at the entrance” as a practical scenario of the proposed services. In order to evaluate the Agent Service, which is a key component of proposed service, we have conducted the a preliminary experiment with 17 people with dementia.

With the goal of developing a conversational humanoid that can serve as a companion for people with dementia, we propose an autonomous virtual agent that can generate backchannel feedback, such as head nods and verbal acknowledgement, on the basis of acoustic information in the user's speech. The system is also capable of speech recognition and language understanding functionalities, which are potentially useful for evaluating the cognitive status of elderly people on a daily basis.

Reminiscence therapy, which is effective for increasing the selfesteem of and for reducing behavioral disturbances in individuals with dementia, is usually conducted in a group led by experienced staff. However, due to the shortage of care attendants, only a limited number of patients at home can receive the benefits of this therapy. To provide this therapy for patients anytime or anywhere, we have developed a networked reminiscence therapy system that combines IP videophones with a photo- and video-sharing mechanism based on Web technology. First, we prepared the experimental setup in a hospital and examined whether dementia patients could communicate with therapists by videophone. Then we conducted a field trial of networked reminiscence therapy with a more realistic situation where remote volunteers communicated with dementia sufferers in the care home by IP videophones connected by broadband network. In this paper, we describe our developed system. Then, we present experimental results showing that dementia sufferers could communicate with therapists by videophone and that networked reminiscence sessions were generally as successful for individuals with dementia as face-to-face reminiscence sessions.