Object Recognition with Machine Learning: Case Study of DemandResponsive Service To assist the disabled, a system was developed and designed to provide passenger transport services for disabled persons in wheelchairs. Machinelearning imagerecognition technology was applied to provide accessible bus rides for the disabled. Based on the video footage at the bus stop, this system can judge whether there are wheelchair riders in the waiting area. YOLOv3, a realtime imagerecognition model, was used for object recognition and notification. By combining LINE with the Flask Web application framework, it helps the disabled transmit messages and actively notifies bus drivers. The API server was established using the Flask framework. When the system at the bus stop detects the disabled, the images of the disabled waiting for the bus are transmitted to the server, and the images and related messages are transmitted in real time to the users through the LINE Message API. Passengers can also use the LINE chatbot to enter keywords to confirm the realtime location of the bus. The dynamic bus information comes from the Open Data of the Bureau of Transportation. The LINE BOT API program was developed using the PyCharm tool and then transmitted to the Heroku cloud platform for deployment. The LINE BOT API provides realtime wheelchair image recognition, and receives notifications and bus information queries through the LINE chatbot. The main contributions of this study are the use of artificial intelligence (AI) technology to provide solutions to facilitate bus rides for the disabled and to improve the quality of barrierfree transport services in modern society.

Assistive Technology through Internet of Things and Edge Computing This paper proposes a framework that applies computer vision and machine learning techniques, through an Internet of Things (IoT) network, with the use of cloud computing for capacity increase. Images are captured by an IoT device and sent for an edge element (IoT node), which processes them, identifies objects, computes distances, and, ultimately, converts that information into audible commands, in order to provide guidance for visuallyimpaired people. Unrecognized objects are sent to a cloud service, in order to provide network retraining and, consequently, system evolution. With the goal of validation, the proposed framework was implemented, using known and proven technologies, such as Raspberry Pi, Esp32CAM, OpenCv, Tensorflow, GoogleTTS and Python. Finally, experimental results demonstrated its feasibility and effectiveness.