Federated Learning-Driven IoT and Edge Cloud Networks for Smart Wheelchair Systems in Assistive Robotics

These days, assistive robotics and their applications for people with disabilities have become a revolutionary field in medical care. It combines edge-cutting technologies such as the Internet of Things (IoT), edge computing networks, and federated learning, offering the best services to disabled people for mobility and navigation in the environment. However, in the state of the art, many conceptual models are presented, and less effort is put into the practical implementation of assistive robots for disabled people in the environment. With this motivation, we propose an intelligent assistive robotics wheelchair system that enhances disabled care in federated learning-enabled IoT and edge cloud networks. In the proposed system, we connect IoT-enabled robots with various sensors and devices, facilitating real-time data gathering and processing, which is vital for tracking the health conditions and immediate requirements of disabled patients. The proposed system enables the robots to continuously learn and adapt to the specific needs trained on different clinicals based on federated learning securely. For example, a user who uses a wheelchair might benefit from a robot that learns to navigate various settings based on previous interactions, optimize paths, and adjust assistance based on real-time conditions based on multi-modal trained data. We exploited the deep convolutional neural network to train, validate, and test data. These navigation operations are performed with higher accuracy and secure data. The system's goal is to offer wheelchairs to disabled people for performing pilgrimages and monitoring their health, as well as autonomous help to search locations and objects. Simulation outcomes show that we achieved optimal results for patients with disabilities, a time and deadline missing ratio of 50%, data training accuracy of 98%, and minimized power consumption of wheelchairs during pilgrimages and Umrah in Saudia Arabia. The overall paper focused on the disability services during their pilgrimage in Saudia Arabia.