A multimodal smartphone interface for active perception by visually impaired

The diffuse availability of mobile devices, such as smartphones and tablets, has the potential to bring substantial benefits to the people with sensory impairments. The solution proposed in this paper is part of an ongoing effort to create an accurate obstacle and hazard detector for the visually impaired, which is embedded in a hand-held device. In particular, it presents a proof of concept for a multimodal interface to control the orientation of a smartphone's camera, while being held by a person, using a combination of vocal messages, 3D sounds and vibrations. The solution, which is to be evaluated experimentally by users, will enable further research in the area of active vision with human-in-the-loop, with potential application to mobile assistive devices for indoor navigation of visually impaired people

Assistive Systems for the Visually Impaired Based on Image Processing

In this chapter, we proposed three assistive systems for visually impaired individuals based on image processing: Kinect cane system, Kinect goggle system, and light checking system. The Kinect cane system can detect obstacles of various sizes and also recognize objects such as seats. A visually impaired user is notified of the results of detection and recognition by means of vibration feedback. The Kinect goggle system is another type of wearable system, and can make user’s hands free. The light checking system is implemented as an application for a smartphone, and can tell a visually impaired user the ON/OFF states of room lights and elevator button lights. The experimental results demonstrate that the proposed systems are effective in helping visually impaired individuals in everyday environments

An assistive model of obstacle detection based on deep learning: YOLOv3 for visually impaired people

The World Health Organization (WHO) reported in 2019 that at least 2.2 billion people were visual-impairment or blindness. The main problem of living for visually impaired people have been facing difficulties in moving even indoor or outdoor situations. Therefore, their lives are not safe and harmful. In this paper, we proposed an assistive application model based on deep learning: YOLOv3 with a Darknet-53 base network for visually impaired people on a smartphone. The Pascal VOC2007 and Pascal VOC2012 were used for the training set and used Pascal VOC2007 test set for validation. The assistive model was installed on a smartphone with an eSpeak synthesizer which generates the audio output to the user. The experimental result showed a high speed and also high detection accuracy. The proposed application with the help of technology will be an effective way to assist visually impaired people to interact with the surrounding environment in their daily life