- Title
- An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Creator
- Jacob, Sunil; Alagirisamy, Mukil; Menon, Varun; Kumar, B. Manoj; Balasubramanian, Venki
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177515
- Identifier
- vital:15298
- Identifier
-
https://doi.org/10.1109/ACCESS.2020.2997727
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2013 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License
- Rights
- Open Access
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Artificial intelligence; Assistive technologies; Brain-computer interface; Edge computing; Internet of Things (IoT); Rehabilitation
- Full Text
- Reviewed
- Funder
- This work was supported in part by the Institute of Electrical and Electronics Engineers (IEEE) EPICS, USA, under Grant 2016-12.
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