# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Visually Impaired
#### 2.1.2 Object Recognition Systems  
A vision-based system to _differentiate objects with a similar tactile sensation_ is developed [18]. It consists of three modules:* _An input module_: the visually impaired specifies to which class an object belongs; touch is used to estimate the object's size and shape, and/or sound is used to obtain a feedback on the object content.
* _A visual recognition module_: it analyzes images from a video stream, as well as the prior information provided by the visually impaired, and tries to identify particular visual features from the object. For this task, different image descriptors (descriptions of the visual features of the contents in images or videos) have been compared; bag-of-features (BoF) provides a higher recognition performance than other image descriptors.
* _An output module_: it provides feedback to the user.  
Deep learning techniques for object detection have improved significantly in recent years [19; 20]. The _wearable smart glasses recognize drug pills_ using deep learning [21], so that safety in medication-use for the visually impaired with chronic diseases is improved. The proposed system consists of a pair of wearable smart glasses, an artificial intelligence (AI)-based intelligent drug pill recognition box, a mobile device app, and a cloud-based information management platform. The Quick Response (QR) code on a drug package is scanned using the mobile device app to obtain the associated medication information, which is uploaded in the cloud-based management platform. The proposed AI-based intelligent drug pill recognition box consists of an AI-based embedded edge computing module and a power amplifier with a micro speaker. This box receives the medication information and reminds the patient to take his/her medicine, when the time comes, using voice messages. When the patient picks up the selected drug pills, the camera embedded on the proposed smart glasses takes and sends an image to the recognition box to analyze if this is the right medicine. This box also transmits the medication-use records to the cloud-based management platform, so the patient's family members or caregivers are able to check the medication status using a mobile app. Two deep learning modules are used for object detection: (1) Single Shot Detector (SSD) as an object position detection module and (2) ResNet as an image classification module.  
In addition, a _deep learning-based approach able to detect and recognize bills and coins_, from images captured by smartphone cameras in complicated backgrounds, is introduced [22]. The suggested three-stage detection technology for new banknotes and coins applies faster region-based CNN, geometric constraints, and Residual Network (ResNet). A portable system allows visually impaired people to _detect and recognize Euro banknotes_[23]. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The Haar features and AdaBoost algorithms, proposed by [PERSON] and [PERSON], are the core of the algorithm for the Euro banknote detection, while the Fast-Hessian matrix is the core of the SURF descriptor that is used for the detection of points of interest between images as well as to match them [23]. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.  
A _deep learning object detector_[24] recognizes static and moving objects in real time. After detection, a Kalman Filter multi-object tracker is used to track these objects over time to determine the motion model. Motion features are extracted to quantify the level of hazard of a given object using five predefined classes with a neural network-based classifier.