The image shows AI counting the number of pedestrians walking along the street, using footage from a security camera in front of a store. By utilizing AI to automatically recognize age and gender and count people 24 hours a day, 365 days a year, this data could serve as valuable market information for the AI era.
In addition, we are also developing medical systems that utilize AI for applications in laparoscopic surgery, among others. Currently, we are conducting joint research with School of Medicine, College of Science and Engineering, College of Engineering, College of Humanities and Sciences, and other institutions.
In our laboratory, we are conducting joint research with professors from the School of Medicine to develop next-generation educational systems.
We are collaborating with muujuu.Inc, a company engaged in VTuber production and motion capture technology development, to conduct product development marketing focused on how to utilize new technologies. By presenting fresh ideas unique to students and receiving feedback from the company, we gain a realistic understanding of business perspectives, on-site challenges, and potential issues.
Since VTubers operate in virtual spaces, they cannot physically hold products. However, with Mujuryoku Inc.'s new technology, V-Diver, VTubers can interact with objects in a room, such as holding products, shaking hands with people, or riding vehicles, just as shown in photos. This innovation opens up new possibilities as a marketing tool.
Each store is equipped with security cameras, and by integrating AI into these systems, it is possible to automatically detect criminal activities such as shoplifting. The diagram illustrates AI-powered image recognition, where the green circular points indicate locations such as hands, elbows, and shoulders, and their coordinates can be measured in real time. Using these coordinates for motion analysis and modeling makes automatic crime detection achievable.
Monitoring camera footage 24/7 by human eyes is a challenging task. However, with AI automatically detecting incidents, triggering warning sounds, and sending notifications, the workload of store managers and security centers can be significantly reduced and automated. Additionally, AI can detect situations beyond crime, such as someone suddenly feeling unwell, crouching down, or collapsing.
In the future, we aim to integrate AI into security cameras on roads, buildings, and train stations to detect emergencies requiring medical assistance or to prevent crimes before they occur. This would involve playing a siren to deter individuals and simultaneously notifying the police or disaster prevention centers. The goal is to utilize AI to protect and safeguard human lives.
In facility and risk management, rust evaluation is extremely important, and it has traditionally been conducted through visual inspection by skilled technicians. However, due to issues like retirement, the transfer of expertise from veteran technicians to younger ones has not kept pace. This is an example of a joint research project where AI-driven automatic evaluation was made possible through machine learning on a vast amount of past evaluation results and photos.
This photo was created based on the image on the left, and the area to be evaluated is only the metal mounting bracket. The image on the right uses Grad-CAM to highlight the areas the AI was looking at in red. Although there are many irrelevant parts, such as the concrete wall in the background and other devices, it is confirmed that the AI is only referencing the metal bracket for the evaluation.
This is the scene when Toyotani Seminar received an award for contributing to the revitalization of the local area through the Ramen Stamp Rally at the special stage of Narashino Kiratto, Narashino City's largest festival. The mayor of Narashino, the president of the Chamber of Commerce and Industry, the chairman of the executive committee of the Ramen Stamp Rally, President Tokishige, and the seminar leader and vice-seminar leader of the Toyotani Seminar, Toyotani, are lined up.
Nihon University Industrial Engineering students cooperate with local event in Narashino, utilizing SNS and AI (2023/11/08)
Toyotani Seminar, which supported the Ramen Stamp Rally Received a letter of appreciation and commemorative gift from the committee (2022/10/9)
Okubo Mimo Ramen Stamp Rally 2022 Eat, apply, and support the city! (2022/9/3)
Toyotani Laboratory will be hosting the "Okubo/Mitsumi Ramen Stamp Rally" Technical support for operation (2022/06/03)
Ramen restaurant stamp rally in Narashino 14 participating stores (2023/10/27)
At the Ramen Carnival, we utilized the homepage and Twitter (X) to disseminate information and conducted access analysis to investigate the effectiveness. The upper part shows the transition of access numbers for Twitter (X), while the lower part shows the access numbers for the homepage. Initially, the access numbers to the homepage increased for registering in the Ramen Stamp Rally app. However, afterwards, as there was no need to refer to the homepage, the access numbers did not increase. However, through access analysis, it was found that from the posts on Twitter (X), people were not accessing the homepage but directly referring to the SNS of each store.
We are currently collaborating with two traditional Japanese confectionery companies, Oedo Co., Ltd. and Watato Co., Ltd., on SNS marketing activities. Leveraging creative ideas unique to students, we support activities such as photo shoots for X (formerly Twitter) and Instagram, as well as promotional video production.
Both company presidents are personally involved to help communicate the charm of traditional Japanese sweets to younger generations. Videos created by our seminar students are posted by the companies on their platforms, and we receive feedback from the presidents, including insights on viewership and evaluation, which provides valuable exposure to real business perspectives.
In collaboration with [MELON LAB. × DANISH LAB. Mimomi Store (Melon Bread Shop)] located at Mimomi Station, we have started initiatives to utilize AI in management, such as predicting sales and preparation quantities based on past visitor numbers, sales figures, temperature, humidity, weather, and seasons.