# An Overview of Machine Learning and 5G for People with Disabilities
## 8 Conclusions  
In this paper, an overview of machine learning and 5G, for people with disabilities, is provided. Machine learning offers new ways to help people with disabilities. The most important machine learning accessibility tools for people with disabilities have been introduced. The components designed for the visually impaired are: (1) _navigation systems_, (2) _object recognition systems_, and (3) _face recognition systems_. The one designed for the hearing impaired is _sound recognition systems_. Finally, the components designed for the physically impaired are: (1) _human-machine interfaces_ and (2) _human activity recognition systems_.  
Machine learning is particularly well-suited to operate with 5G networks since it requires massive amounts of data to predict activity accurately and 5G can transmit higher volumes of data faster. The key 5G capabilities that will have significant importance in improving assistive technology for the disabled are low latency, high scalability, low energy consumption, improved connectivity and reliability, improved security, and high data rates.  
The relevant application scenarios and main benefits have been described. Machine learning and 5G help people with disabilities remain autonomous in four basic scenarios: shopping, travelling, work, and smart homes.  
A 5G network slicing architecture has been proposed, and its main benefits have been discussed. Specific slices have been proposed based on the main KPIs and functional requirements of the different use cases for people with disabilities. The research challenges have also been surveyed, with particular reference to the improvement of training data, for people with disabilities. In this area, significant efforts are required. The traditional mobile communication networks that provide services based on the \"one-size-fits-all\" design are usually built on specialized equipment and dedicated platforms with weak flexibility and scalability. Therefore, they cannot address the wider range of performance and cost requirements of the new use cases for people with disabilities. For this reason, we propose a 5G network slicing architecture that improves the Quality of Service (QoS) of the different applications for people, with disabilities allocating dedicated slices for each use case. The 5G network slices, which are represented by logically isolated and self-contained networks, are flexible enough and highly customizable to accommodate diverse use cases simultaneously over the same network infrastructure. As future work we propose to conduct an empirical evaluation of the 5G architecture for people with disabilities to verify its effectiveness.  
This work has been supported by the Agencia Estatal de Investigacion of Ministerio de Ciencia e Innovacion of Spain under project PID2019-108713 RB-C51 MCIN/ AEI /10.13039/501100011033.  
Not applicable.  
This work has been supported by the Agencia Estatal de Investigacion of Ministerio de Ciencia e Innovacion of Spain under project PID2019-108713 RB-C51 MCIN/ AEI /10.13039/501100011033.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.