To assist personalized healthcare of elderly people, our interest is to develop a virtual caregiver system that retrieves the expression of mental and physical health states through human–computer interaction in the form of dialogue. The purpose of this paper is to implement and evaluate a virtual caregiver system using mobile chatbot. Unlike the conventional health monitoring approach, our key idea is to integrate a rule-based virtual caregiver system (called “Mind Monitoring” service) with the physical, mental, and social questionnaires into the mobile chat application. The elderly person receives one question from the mobile chatbot per day, and answers it by pushing the optional button or using a speech recognition technique. Furthermore, a novel method is implemented to quantify the answers, generate visual graphs, and send the corresponding summaries or advice to the specific elder. In the experimental evaluation, we applied it to eight elderly subjects and 19 younger subjects within 14 months. As main results, its effects were significantly improved by the proposed method, including the above 80% in the response rate, the accurate reflection of their real lives from the responses, and high usefulness of the feedback messages with software quality requirements and evaluation. We also conducted interviews with subjects for health analysis and improvement.

To capture scientific evidence in elderly care, a user-defined facial expression sensing service was proposed in our previous study. Since the time-series data of feature values have been growing at a high rate as the measurement time increases, it may be difficult to find points of interest, especially for detecting changes from the elderly facial expression, such as many elderly people can only be shown in a micro facial expression due to facial wrinkles and aging. The purpose of this paper is to implement a method to efficiently find points of interest (PoI) from the facial feature time-series data of the elderly. In the proposed method, the concept of changing point detection into the analysis of feature values is incorporated by us, to automatically detect big fluctuations or changes in the trend in feature values and detect the moment when the subject’s facial expression changed significantly. Our key idea is to introduce the novel concept of composite feature value to achieve higher accuracy and apply change-point detection to it as well as to single feature values. Furthermore, the PoI finding results from the facial feature time-series data of young volunteers and the elderly are analyzed and evaluated. By the experiments, it is found that the proposed method is able to capture the moment of large facial movements even for people with micro facial expressions and obtain information that can be used as a clue to investigate their response to care.

In today’s world, services are essential in daily life, and identifying each person’s unique needs is key to creating a human-centered society. Traditional research has used machine learning to recommend services based on user behavior logs without directly detecting individual needs. This study introduces a system called Needs Companion, which automatically detects individual service needs, laying the groundwork for accurate needs sensing. The system defines a needs data model based on the 6W1H framework, uses virtual agents for needs elicitation, and applies large language models (LLMs) to analyze and automatically extract needs. Experiments showed that the system could detect needs accurately and quickly. This research provides interpretable data for personalized services and contributes to fields like machine learning, human-centered design, and requirements engineering.

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development.

In this study, aimed at achieving Society 5.0, we explore efficient development methods for services tailored to individual user needs. The diversification of user demographics has led to swiftly changing service demands, increasing the necessity for personalized adaptation. However, developing services tailored to individual users requires specialized knowledge and incurs high costs. This research focuses on developing technologies that enable service development through a concrete understanding of user needs. Utilizing an interactive needs extraction system with a virtual agent (VA) developed in prior studies, along with the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system, we aim to automate the extraction of user needs and provide developers with relevant insights. Prior research has found it challenging to grasp the outline of services to be developed from user statements. However, a new method using large language models (LLM) has enabled the automatic extraction of novel service needs. Experiments have shown the potential to automatically provide development insights for 67% of ideal needs.