# Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
## 1 Introduction  
People with visual impairment must overcome various difficulties in daily life; tasks such as moving around spaces might be challenging for these people. Numerous tools and devices have been created to help blind people increase their independence. The designs of these tools and devices are typically sensor-based, computer-vision-based, or smartphone-based. Sensor-based approaches [1, 2, 3, 4, 5] use data collected from a variety of sensors, including ultrasonic, infrared (IR), laser, and distance sensors, to identify obstructions. Computer-vision-based techniques [6, 7, 8, 9, 10] use cameras to capture the environment in front of the user and then apply computer vision algorithms to identify obstacles. Smartphone-based solutions [11, 12, 13, 14] leverage smartphone cameras or sensors to obtain information from the outside world. These data are then processed to detect obstructions [15]. Recent deep learning developments have greatly improved picture recognition system performance and application in practical assisting environments. Deep learning models, especially convolutional neural networks (CNNs), can learn multi-level feature representations from rawinput data on their own, unlike conventional image processing techniques. This skill helps these models to excel in challenging tasks such as object identification, facial recognition, and emotional categorization. Such characteristics are particularly crucial in dynamic and erratic environments; such urban navigation systems are meant to help visually challenged individuals. For instance, [PERSON] et al. [16] developed methods incorporating deep learning with data augmentation methods to increase architectural feature detection in complex street-view imagery. Their research is a prime example of how deep learning models are adaptable and robust. The designs of most devices developed for people with visual impairment are focused primarily on navigation and obstacle avoidance instead of context awareness and object recognition [17; 18; 19; 20]. In addition, these designs often use servers or computers to implement their method, which limits possible navigation areas. Object detection schemes are typically employed to perform object identification in a current scene, and the produced descriptions of the environment are simplistic. To resolve the aforementioned problems, this study developed a deep-learning-based multifunctional embedded mobile system that performs face recognition, object detection, and gender, age, and emotion classifications to assist people with visual impairment in perceiving their environment. These tasks are briefly described as follows. (1) Face recognition  
Face recognition has become a popular research topic in computer vision because this task is critical for numerous applications, including monitoring and security, video surveillance, and human-computer interaction (HCI) systems [21]. Deep learning techniques can successfully perform face recognition; for example, CNN architectures have been used to develop cutting-edge face recognition technologies, such as VGGFace [22], FaceNet [23], and ArcFace [24]. Internet of Things (IoT) technology has also recently been widely applied; however, the potential of IoT-based health-care systems requires further exploration. Some studies have developed cloud- or computer-based facial recognition software for people with visual impairment. Cloud-based systems typically comprise a server and a local unit [25; 26]. The local unit collects data such as photos and sends and receives the data to and from a server. The server processes the received data and communicates its findings to the local unit. Cloud-based systems can be portable but typically have long processing times and are only usable with Internet access. Computer-based approaches often involve using a laptop [27; 28] to handle all of the operations of a cloud-based system. However, carrying a laptop during movement and navigation may be tiring for people with visual impairment.