## 2 Method  
The survey was conducted with the following search methodology (see Figure 1): the first step was determining the scope of the survey. The selected articles should be:  
1. on assistive devices;
2. preferably using deep-learning technologies;
3. aiming at fulfilling navigation, obstacle detection and/or object recognition tasks;
4. designed for BVIP.  
A decision was also made to favor recent articles, ideally ranging from 2017 until the present, to provide only the latest technological advances in AI methods. Article quality was particularly important. All publications were assessed using the following tools:  
Figure 1: PRISMA selection diagram that explains the main steps of our survey methodology (see text for explanation).  
* Qualis from the Computing Institute of the Federal University of Mato Grosso in Brazil ([[https://qualis.ic.ufm.br/](https://qualis.ic.ufm.br/)]([https://qualis.ic.ufm.br/](https://qualis.ic.ufm.br/)), visited 30 September 2021), with venues ranked A1 or A2;
* Core Conference Portal ([[http://portal.core.edu.au/conf-ranks/](http://portal.core.edu.au/conf-ranks/)]([http://portal.core.edu.au/conf-ranks/](http://portal.core.edu.au/conf-ranks/)), visited 30 September 2021), with conferences ranked A or B;
* Scimago Journal and Country Ranks ([[https://www.scimagojr.com/journalrank.php](https://www.scimagojr.com/journalrank.php)]([https://www.scimagojr.com/journalrank.php](https://www.scimagojr.com/journalrank.php)), visited 30 September 2021), with journals or conferences ranked Q1 or Q2.  
The articles were found using Google Scholar and by scanning through scientific databases such as IEEE Xplore, Elsevier ScienceDirect, ACM Digital Library, and PubMed. The search was made using a combination of the following keywords:  
* \"assistive technologies\" or \"assistive devices\";
* \"navigation\";
* \"obstacle detection\" and/or \"object recognition\";
* \"artificial intelligence\", \"deep learning\" or \"computer vision\";
* \"blind\" and/or \"visually impaired people\".  
A total of 78 identified articles were first filtered by reading their abstract to estimate the correspondence with the chosen topics, thus excluding unsuitable ones (33). A few more articles (12) removed from the list after quick reading, mostly due to being technologically inadequate regarding the subject of this survey. In this paper, it was decided to concentrate on external assistive devices and to set aside research based on vision replacement using medical bionic prosthetic systems such as the project described by [PERSON] et al. in [15]. A total of 30 case studies, from 33 research papers, were finally incorporated in the analysis: papers [16; 17; 18; 19; 20] were considered to be sole entities, as they were parts of the same research study. Some more generalist articles were included to highlight important steps and challenges when building assistive tools for BVIP.  
Analysis was done following three main themes, divided in the following sections:  
* First, human-machine interface (data acquisition and processing, feedback transmissions);
* Second, artificial intelligence techniques (scope, algorithms, datasets, training techniques);
* Third, testing methods (context, end-user participation).  
For each part, the results summarized in tables with their percentage of occurrence.