# An Overview of Machine Learning and 5G for People with Disabilities
## 7 Research Challenges  
Next, the research challenges for machine learning and 5G for people with disabilities are introduced. _Customization for people with disabilities is a key challenge._ Since people with disabilities have special needs, machine learning assistive technologies should be adapted to their different vision of the world. The development of applications for people with disabilities is usually hindered by the scarcity of training data for a specific population (e.g., there are very few sign language videos for the deaf). A novel approach is that users train their own assistive devices with their own data; this way, they can personalize their own data-driven assistive technology and improve its accuracy. For example, a mobile sound detector is trained by people with hearing impairments with their own personally relevant sounds (filtering out unimportant ones), or the visually impaired can train an object recognizer with their own objects. New innovative ways should be designed to provide feedback about the quality of their collected training data. _Collecting big data sets is also challenging. Training algorithms used by applications for people with disabilities is time and effort-intensive_ because it involves acquiring images/audio, and other types of data, under different conditions. For instance, the number of objects that a system developer uses to train an object recognition system is going to be a small subset compared to the thousands of objects that the system will be able to recognize. Although blind people have the option to train the system with their own objects, they may abandon it due to the large amount of time and effort it takes. This issue can be solved by sharing resources through crowdsourcing, although a special effort must be made to combine datasets from different sources. A possible solution to this problem would be the maintenance of a database of training data by systems developers that would incorporate additional data to train objects sent by current users. Another challenge is how to optimize the recognition accuracy of the machine learning algorithms in real-time applications for the disabled._ There is a tradeoff between the obtained classification accuracy and the maximum delay that the application can tolerate to maintain its quality of service. For example, the recognition accuracy for moving a wheelchair based on EEG eye movement recognition may be improved computing more spectral and statistical features, but the addition of more features from 32 channels would require a feature selection method and would increase the computational burden so much that the proposed method would not work as a real-time application [32]. Sometimes latency is increased due to the congestion and the distance of the network systems. In these cases, instead of offloading computation to the remote cloud, edge computing can be leveraged in 5G networks to store and process content near mobile users [73]. The decentralization of cloud computing infrastructure to the edge introduces new challenges to 5G evolution [73], such as resource management to support only a constrained number of applications with moderate needs because of the limited computing and storage resources in an individual Mobile Edge Computing (MEC) platform or fair resource sharing and load balancing. Slice security is also required. Dynamic resource sharing among tenants enables more efficient network resource usage but also introduces new problems such as how to guarantee a secure isolation of the slices. Security requirements of specific tenant applications, such as remote vehicular control (for people with disabilities), could restrict how the slices are partitioned, or even prevent the coexistence of network slices that would share the same hardware [56]. Since network slices exchange important information about the network state and the network resources, the higher the number of virtual network functions share between slices, the higher the security risks might be [74].