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

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.

[1]  Amitabha Chakrabarty,et al.  An Algorithm for Mapping a Traffic Domain Into a Complex Network: A Social Internet of Things Approach , 2019, IEEE Access.

[2]  B. Manoj Kumar,et al.  An Adaptive and Flexible Brain Energized Full Body Exoskeleton With IoT Edge for Assisting the Paralyzed Patients , 2020, IEEE Access.

[3]  Ajay K. Sharma,et al.  Cost-Effective Cluster-Based Energy Efficient Routing for Green Wireless Sensor Network , 2020, Recent Advances in Computer Science and Communications.

[4]  Sunil K. Agrawal,et al.  Retraining of Human Gait - Are Lightweight Cable-Driven Leg Exoskeleton Designs Effective? , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Leonardo Mostarda,et al.  Artificial Muscle Intelligence System With Deep Learning for Post-Stroke Assistance and Rehabilitation , 2019, IEEE Access.

[6]  Xiaodong Zhang,et al.  Processing Surface EMG Signals for Exoskeleton Motion Control , 2020, Frontiers in Neurorobotics.

[7]  Ajay K. Sharma,et al.  Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network , 2019, Appl. Soft Comput..

[8]  Danda B. Rawat,et al.  Intelligent Framework Using IoT-Based WSNs for Wildfire Detection , 2021, IEEE Access.

[9]  N. Ward,et al.  Brain regions important for recovery after severe post-stroke upper limb paresis , 2017, Journal of Neurology, Neurosurgery, and Psychiatry.

[10]  Sreeja Rajesh,et al.  Secure Brain-to-Brain Communication With Edge Computing for Assisting Post-Stroke Paralyzed Patients , 2020, IEEE Internet of Things Journal.

[11]  E. Biryukova,et al.  Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial , 2017, Front. Neurosci..

[12]  Subashan Perera,et al.  Persisting Consequences of Stroke Measured by the Stroke Impact Scale , 2002, Stroke.

[13]  M. Shamim Hossain,et al.  Cloud-Supported Cyber–Physical Localization Framework for Patients Monitoring , 2017, IEEE Systems Journal.

[14]  Jinho Choi,et al.  An Efficient Clustering Framework for Massive Sensor Networking in Industrial Internet of Things , 2021, IEEE Transactions on Industrial Informatics.

[15]  M. Shamim Hossain,et al.  Cognitive IoT-Cloud Integration for Smart Healthcare: Case Study for Epileptic Seizure Detection and Monitoring , 2018, Mob. Networks Appl..

[16]  J. Liepert,et al.  Treatment-induced cortical reorganization after stroke in humans. , 2000, Stroke.

[17]  Irene Faiman,et al.  Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field , 2018, NeuroImage.

[18]  Kai-Kit Wong,et al.  A Lightweight Secure and Resilient Transmission Scheme for the Internet of Things in the Presence of a Hostile Jammer , 2020, IEEE Internet of Things Journal.

[19]  William D. Smart,et al.  Robots for humanity: using assistive robotics to empower people with disabilities , 2013, IEEE Robotics & Automation Magazine.

[20]  David A. Winter,et al.  Human balance and posture control during standing and walking , 1995 .

[21]  L. Sudarsky,et al.  Neurologic disorders of gait , 2001, Current neurology and neuroscience reports.

[22]  A. Geurts,et al.  Motor recovery after stroke: a systematic review of the literature. , 2002, Archives of physical medicine and rehabilitation.

[23]  Saurin Sheth,et al.  Exoskeleton: The Friend of Mankind in context of Rehabilitation and Enhancement , 2016 .

[24]  Hugh Herr,et al.  Exoskeletons and orthoses: classification, design challenges and future directions , 2009, Journal of NeuroEngineering and Rehabilitation.

[25]  J. Wolpaw,et al.  Restoring Walking after Spinal Cord Injury , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[26]  Hui Lin,et al.  Toward Secure Data Fusion in Industrial IoT Using Transfer Learning , 2020, IEEE Transactions on Industrial Informatics.

[27]  Sunil Jacob,et al.  IoT-powered deep learning brain network for assisting quadriplegic people , 2021, Comput. Electr. Eng..

[28]  J. Barendregt,et al.  Global burden of disease , 1997, The Lancet.