summary

Early detection of people with a tendency toward loneliness and guide them to early support by predicting their mental and physical conditions using AI for estimating mental states such as loneliness and environmental factor analysis technology. Provide a place to stay and show the way to hope through volunteer activities such as maintenance of Pecan Forest and renovation of vacant houses. Aim to solve social isolation and loneliness and create sustainable local communities through the use of data.

Technical Strengths

Natural language processing AI estimates and visualizes psychological states such as individual loneliness non-invasively and in real-time from everyday text and voice data. Environmental factor analysis technology predicts and visualizes mental and physical states up to a week into the future using weather, air pollutants, space weather, etc. This is an innovative technology for early awareness and early intervention in the unaffected stage.

Case Studies (Including Demonstration Results)

Municipalities served

Yamaguchi Prefectural Police (demonstration experiment)

Case Studies

Aiming to reduce accidents by visualizing the risk of traffic accidents using AI and environmental factor analysis.

scale of implementation

Using five years of publicly available traffic accident data (2019-2023) in Yamaguchi Prefecture, the project automates the process of analyzing traffic accidents and preparing reports. In addition, factors contributing to traffic accidents were analyzed and a model for predicting the occurrence of traffic accidents was developed.

Time to delivery

Development projects: 3-12 months

Price (if paid)

Prediction of mental and physical condition using loneliness estimation AI and environmental factor analysis technology: ¥500/month/person (estimated).
Development projects: Individual quotation

Other Case Studies

Joint research: Prediction of disease onset using environmental factor analysis technology

How to use in peacetime

Prediction of the risk of mental and physical disorders among employees: Estimation and prediction of the mental and physical conditions of employees working in companies and departments, and guiding them to counseling and volunteer activities to improve labor productivity and to prevent leave and turnover.