# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Visually Impaired
#### 2.1.1 Navigation Systems  
A _walking guide_[10] enhances the mobility of the visually impaired. It consists of two parts: an _obstacle and a pothole detection system_. The presence of obstacles can be detected in the front, left, and right directions using ultrasonic sensors. Audio messages are transmitted  
Figure 2: Proposed taxonomy of machine learning accessibility tools. in the presence of obstacles. Images captured by a camera are sent to convolutional neural networks (CNNs) to detect the presence of potholes and alert users with audio signals. A smartphone-based solution [11] helps the visually impaired _navigate along predefined paths in indoor and outdoor scenarios_. It does so by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback. Augmented reality technology is used to eliminate the need for any physical support, whereas CNNs are trained to recognize objects or buildings and make it possible to access the digital contents associated with them. The _location of a visually impaired_ is obtained with enough precision _in indoor navigation_[12]. The indoor environment is divided into microcells. Two-dimensional local coordinates are assigned to the vertices of each microcell. Data from inertial sensors (accelerometer, gyroscope, and magnetometer) of a smartphone is used to train a multilayer perceptron (MLP). The neural network returns the 2D local coordinates of the microcell, related to the position of the smartphone and its owner, with an accuracy of more than 94% and a positioning error of 0.65 m.  
Furthermore, an _artificial intelligence edge computing-based assistive system_ helps the visually impaired to safely _use zebra crossings_[13]. When a visually impaired pedestrian reaches a zebra crossing next to a traffic signal, an intelligent walking cane receives the current pedestrian traffic signal status and transmits it to a waist-mounted device. This device guides the visually impaired with audio messages. In addition, a video of the zebra crossing is recorded via the front camera (module) of the blind person's smart sunglasses, and the video is transmitted in real-time to the proposed mounted device. Based on the video images, it is possible to detect zebra crossings and determine if the pedestrian is deviating from the crossing when he/she is passing through. If this is the case, warnings are transmitted to the earphones of the visually impaired so that he/she corrects his/her route to avoid an accident. The Inception V2 based convolutional neural network is used for real-time zebra crossing recognition. A mobile vision-based _staircase detection system in indoor environments_ can also be added [14]. The database of staircases candidates (upstairs, downstairs, and negatives (i.e., corridors)) is collected using an RGB-D camera. The Support Vector Machine (SVM) multi-classifier is used for training and testing during staircase recognition. Furthermore, an _assistance system to support the navigation of the visually impaired in low structured environments_ has also been proposed [15]. A safe walking direction is computed based on the images from a 3D stereo camera mounted at the front of a backpack. Three different convolutional neural network (CNN) architectures (U-Net, PSPNet, and LinkNet) that include several backbones (DenseNet, EfficientNet, Inception, Mobilenet v1 and v2, ResNet, and VGG16) are used to segment the traversable path. Vibrotactile feedback is sent to the visually impaired once the local collision free trajectory is computed. In addition, a _navigation system_ that helps the visually impaired _run_ is also developed [16]. The 3D camera captures information from its environment; an Artificial Neural Network (ANN) analyzes the images to detect the path; finally, vibration motors generate the navigation feedback. Also, a computer vision-based system [17]_recognizes bus information_ based on images captured by a camera at a bus stop.