Understanding human intention is critical in healthcare, where direct communication might be limited. We developed a dynamic intention recognition system for elderly care, integrating visual data and conversational interactions. Building upon in-home fixed-point cameras and lightweight image recognition systems, facial, skeletal, and hand features were detected to monitor behavior changes. Image-based analysis alone is often insufficient to fully grasp the intentions behind observed actions. Therefore, we developed a multimodal system that combines (1) image-derived contextual cues and (2) dialogue with an interactive agent, analyzed through locally executable large language models (LLMs). Specifically, we utilized locally deployable models such as Meta-Llama and Llama 3.1 within a web-based interface built on AnythingLLM, enabling real-time conversational intention estimation without compromising data privacy. The developed system performs image-based context extraction, conversational prompting with embedded intentions, and time-windowed log analysis using local LLMs for chronological intention estimation and confirmation. Five participants were recruited to compare their self-reported intentions with those inferred by the system using document similarity metrics. The results demonstrated the feasibility of the system and its real-time, privacy-conscious performance in elderly home care. The system contributes to the advancement of human-agent collaboration and lays a foundation for adaptive, intention-aware support systems. © 2025 IEEE.

In recent years, percentage of the elderly living alone are also on the rise. Elderly living alone are likely to be socially isolated, and if the detection of illness or injury is delayed, it is likely to lead to dying alone. It is important to understand the daily health condition of such elderly people, but existing safety confirmation and elderly care services have the problem that elderly people have no choice but to report their health condition by themselves. In this study, we use an existing emotion recognition model to acquire and analyze emotion estimation results from images of facial expressions of people acting according to their health condition. Then, we conduct an experiment to see if machine learning can classify the video clips using the emotion estimation results as feature values and the health condition of the person as labeled data. Based on the experiments, it is possible to classify facial expressions by creating a model that is adapted to the individual. © 2025 IEEE.

In Japan, the prevalence of Mild Cognitive Impairment (MCI), a prodromal stage of dementia, has been increasing with population aging, becoming a significant social issue. MCI impairs memory, particularly episodic memory, imposing psychological and time-related burdens on both individuals and caregivers. Although several methods have been proposed to support memory by collecting and presenting daily life data, many rely on third-person perspectives or require complex device operations. Therefore, it is difficult for such individuals to operate existing systems independently. The goal of this paper is to support older adults with MCI in independently collecting and storing first-person perspective data, thereby facilitating self-initiated recall of episodic memories. Our key idea is to integrate a wearable device built with a Raspberry Pi and cloud storage. We propose a system that automatically captures first-person view images, stores them in the cloud, and enables search based on tags assigned through object recognition. This system aims to support self-initiated recall of episodic memories by allowing older adults to independently collect and retrieve contextual data from their own perspective. © 2025 IEEE.

Japan's aging population and declining birthrate have had a significant impact on emergency medical services(EMS). The rising number of emergency transports has led to longer response times. Some less urgent patient transfers additionally delay the provision of critical emergency care. The introduction of private EMS has been considered as a potential solution, but its effectiveness remains unverified. This study proposes a method to evaluate the impact of delegating less urgent patient transfers to private EMS on EMS quality, using data from Kobe City. Results show that over a year, the cumulative response time was reduced by approximately 170 million seconds, indicating the delegation of less urgent patient transfers to private EMS improves the quality of EMS. © 2025 IEEE.

In recent years, climate change, particularly global warming, has become a major concern, with global average temperatures continuing to rise. In response, the Japanese government has declared its commitment to achieving a zero-carbon society where the difference between CO2 emissions and absorption becomes zero. While energy conservation is crucial for achieving this goal, this research specifically focuses on energy-saving behaviors in general households. Previous research has proposed methods to promote energy-saving behaviors through dialogue between systems and users. However, these methods have several issues: they can only provide predetermined responses, they use costly 3D models, and they cannot determine whether users are at home. Considering these issues, this research proposes EcoWiz, a system utilizing large language models and Live2D. Additionally, the system implements presence detection and periodic notifications using motion sensors and Pub/Sub architecture. Based on the proposed method, we implemented the system and verified its operation through case studies. © 2025 IEEE.

In Japan, the number of elderly people and dementia patients is on the rise. They often face the issue of forgetting where they placed their belongings. When they are unable to remember the location of an item, not only do they themselves have to search for it, but their family members and caregivers are also involved in the search, increasing the burden on everyone involved. Therefore, reducing this burden has become a significant challenge. To address this issue, this study proposes a "Grasped Object Recognition and Recording Service"that utilizes cameras installed in the room and image recognition technology to identify and record objects grasped by individuals. The implementation focused on the recognition and recording of the grasped objects. As a result, the system successfully achieved a practical level of accuracy in recognizing and recording the objects held by people in the room. © 2025 IEEE.

Japan is experiencing rapid population aging, and the shortage of healthcare workers has become a serious issue. In parallel, the number of people with dementia is increasing, raising the importance of early detection and robust screening methods. To address these challenges, we developed the tablet-based drawing test app EVIDENT. In this study, we propose an early screening method for cognitive decline using drawing-process data from the Clock Drawing Test (CDT) and the Cube Copying Test (CCT) collected with these apps. Specifically, we build machine learning models that use process features as explanatory variables and scoring results as target variables. We also perform clustering of the drawing-process data to reveal drawing-process patterns in participants with cognitive decline and to summarize their characteristics. Through evaluation experiments, we demonstrated the effectiveness of the proposed models and extract process features that contribute to early screening. These results suggest that our approach can serve as a basis for supporting early detection in real-world operations, including group testing, while reducing burden at the point of care. In future work, we will expand the dataset, further improve screening accuracy, and pursue applications to diagnostic support integrated with EVIDENT. © 2025 IEEE.

In Japan, the prevalence of mild cognitive impairment (MCI) is rising with population aging, and episodic-memory deficits burden individuals and caregivers. Various memory support methods have been proposed that collect and present daily life data, but many rely on third-person perspective data or require complex operations and prior knowledge. Given this background, there is a growing need for systems that enable individuals with MCI to independently collect, store, and retrieve data that facilitates episodic memory recall. Previous studies have proposed systems that capture and store egocentric images and enable tag-based search, but some issues remain. To address these challenges, this study proposes a new memory support system called Anotoki Camera, which enhances a previous prototype by automatically filtering images and utilizing LLM for tagging. We implemented the system and conducted a case study, confirming that it can automatically eliminate unnecessary images and assign tags useful for search. © 2025 IEEE.

As Japan's population continues to age, the rise in dementia cases has become a major societal issue. For early diagnosis and rehabilitation of dementia, research has focused on techniques that measure hand-motor skills to evaluate cognitive function. Previous systems faced challenges such as the need for improved hand movement evaluation methods, difficulty in system operation and implementation by end-users, and inadequate methods for data provision and visualization. This paper aims to enhance a web service to assess manual dexterity through tapping tasks. The proposed system has revised the evaluation method for scenarios where multiple tapping actions are performed under the same instruction, enabling more accurate assessments of motor function. Additionally, by employing Docker container technology to simplify system startup and converting recorded data into the more user-friendly XLSX format for use in applications like Excel, the system has improved data handling, making it more accessible for end-users. © 2024 IEEE.

Recently, there has been a surge in investigations indicating an association between cognitive functionality and dexterity in hand movements. Nevertheless, research encom-passing the entirety of human fingertip motions has been scarce, and there is an absence of a well-established analyt-ical approach. Our research team has been in the process of devising a system for measuring finger movements that combines image identification with touch panel manipulation. Thus, the objective of this study is to suggest a method for analyzing dexterity in hand movements utilizing the data on fingertip motions procured through our innovative system. Our principal concept revolves around examining irregularly spaced time-based data of fingertip motions and subjecting it to scrutiny using a state-space model. The proposed methodology progresses through a series of steps: (Step 1) Loading and organizing the data. (Step 2) Converting coordinate data. (Step 3) Individually comparing data features. Furthermore, in a case study, we derive data from two types of tapping tasks (specifically, standard tasks and n-back tasks) and offer practical instances of examining four parameters: response time and speed of hand movement, differential distance, and angles. Subsequently, we engage in discussions, delineating differences from pertinent studies and areas in which this methodology could be enhanced. This strategy is anticipated to establish innovative benchmarks for dexterity in hand movements, potentially assisting in the prompt identification of indications of cognitive decline in the elderly. © 2024 IEEE.