AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities

Abstract

In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities.

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

“…The paper of Jacob et al [ 13 ] presents an artificial intelligence-powered smart and light weight exoskeleton system (AI-IoT-SES), which receives data from various sensors, classifies them intelligently, and generates the desired commands via IoT for rendering rehabilitation and support, with the help of caretakers, for paralyzed patients in smart and connected communities. The navigation module uses AI-and IoT-enabled simultaneous localization and mapping.…”

“…When applied to AT, AIoT allows the conception of an array of disruptive solutions to address the disability issue. Some examples of such solutions are navigation systems for blind people, voice assistants for people with disabilities [ 8 ], the remote monitoring of health conditions [ 9 ], telemedicine and telehealth [ 10 ], communication systems based on sign language [ 11 ], auxiliary memory for people with cognitive disabilities, and a series of smart objects such as medicine dispensers, wheelchairs [ 12 ], exoskeletons [ 13 ], etc. These are some of the numerous applications of great value for those in need, quoting Mary Pat Radabaugh (the former director of IBM’s National Support Center for People with Disabilities in 1988) “For people without disabilities, technology makes life easier.…”

Are skepticism and moderation dominating attitudes toward AI‐based technologies?

“…AI‐powered exoskeletons are revolutionizing the experiences of individuals with mobility impairments and those in physically demanding occupations. These wearable robotic systems enhance human strength and endurance, enabling people with disabilities or injuries to perform activities that were once challenging or impossible (Jacob et al., 2021; Vélez‐guerrero et al., 2021). In industrial settings, they mitigate the risk of injury and fatigue, thereby enhancing safety and productivity.…”

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

“…In addition, this technology can also usually be applied to various fields such as; Sentiment Analysis [41]- [43], Big Data [44]- [46], Blockchain [47]- [51], and the Internet of Things (IoT) [52]- [54] AI drop is divided into categories like Machine Learning or ML [55]- [57] ML can be exploited like a workhorse of AI, and the intensive use of ML uses methods that can be found everywhere, almost in a large number of sciences, businesses, and engineering, which are more based on actual decision making [58]. Machine learning can be shared into several types: unsupervised, supervised, semi-supervised, and reinforcing learning (RL).…”