A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities

Authors

  • Sushil Kumar Sahoo Biju Patnaik University of Technology Rourkela, Odisha, India
  • Bibhuti Bhusan Choudhury Department of Mechanical Engineering, Indira Gandhi Institute of Technology Sarang, Odisha, India

DOI:

https://doi.org/10.31181/10001122023s

Keywords:

Assistive technology, Human-machine interface, Navigation systems, Obstacle avoidance, User-centered desig

Abstract

This research paper presents a comprehensive review of smart robotic wheelchairs and their impact on enhancing mobility and independence for individuals with disabilities. Traditional wheelchairs often impose limitations on users, resulting in reduced freedom of movement and limited accessibility. The emergence of smart robotic wheelchairs offers a promising solution to address these challenges. This paper provides an overview of wheelchair technology, identifies the specific challenges faced by individuals with disabilities, and explores the advantages and limitations of smart robotic wheelchairs through a review of previous research studies. The features and functionalities of smart robotic wheelchairs, including navigation and obstacle avoidance capabilities, autonomous and semi-autonomous modes, and customizable control options, are discussed. User experience and performance evaluation, along with the impact on mobility and independence, are examined. The paper concludes with future directions and recommendations to guide further research and development in this important field, aiming to empower individuals with disabilities and improve their quality of life.

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References

Ahmad, J., Sidén, J., & Andersson, H. (2021). A proposal of implementation of sitting posture monitoring system for wheelchair utilizing machine learning methods. Sensors, 21(19), 6349.

Al-Qaysi, Z. T., Zaidan, B. B., Zaidan, A. A., & Suzani, M. S. (2018). A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations. Computer methods and programs in biomedicine, 164, 221-237.

Bhatnagar, A., Pancholi, S., & Janyani, V. (2022). Smart Solar Power‐Assisted Wheelchairs For the Handicapped. In Raut, R., Pathak, P., Kautish, S., & Pradeep N. (Eds.), Intelligent Systems for Rehabilitation Engineering (pp. 175-196). Scrivener Publishing-Wiley.

Botrel, L., Holz, E. M., & Kübler, A. (2015). Brain painting V2: evaluation of P300-based brain-computer interface for creative expression by an end-user following the user-centered design. Brain-Computer Interfaces, 2(2-3), 135-149.

Carlson, T., & Demiris, Y. (2012). Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(3), 876-888.

Cojocaru, D., Manta, L. F., Pană, C. F., Dragomir, A., Mariniuc, A. M., & Vladu, I. C. (2022). The design of an intelligent robotic wheelchair supporting people with special needs, including for their visual system. Healthcare, 10(1), 13.

Cooper, R. A., & Cooper, R. (2019). Rehabilitation Engineering: A perspective on the past 40-years and thoughts for the future. Medical engineering & physics, 72, 3-12.

Cui, J., Cui, L., Huang, Z., Li, X., & Han, F. (2022). IoT wheelchair control system based on multi-mode sensing and human-machine interaction. Micromachines, 13(7), 1108.

Cui, J., Huang, Z., Li, X., Cui, L., Shang, Y., & Tong, L. (2023). Research on Intelligent Wheelchair Attitude-Based Adjustment Method Based on Action Intention Recognition. Micromachines, 14(6), 1265.

Czaja, S. J., & Ceruso, M. (2022). The promise of artificial intelligence in supporting an aging population. Journal of Cognitive Engineering and Decision Making, 16(4), 182-193.

Ding, Z., Liu, J., Chi, W., Wang, J., Chen, G., & Sun, L. (2023). PRTIRL based socially adaptive path planning for mobile robots. International Journal of Social Robotics, 15(2), 129-142.

Doush, I. A., Damaj, I., Al-Betar, M. A., Awadallah, M. A., Al-khatib, R. E. M., Alchalabi, A. E., & Bolaji, A. L. (2020). A survey on accessible context-aware systems. In Paiva, S. (ed.), Technological trends in improved mobility of the visually impaired (pp. 29-63). EAI/Springer Innovations in Communication and Computing. Cham: Springer.

Erdogan, A., & Argall, B. D. (2017). The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: an experimental assessment. Robotics and Autonomous Systems, 94, 282-297.

Esposito, D., Centracchio, J., Andreozzi, E., Gargiulo, G. D., Naik, G. R., & Bifulco, P. (2021). Biosignal-based human–machine interfaces for assistance and rehabilitation: A survey. Sensors, 21(20), 6863.

Fosch-Villaronga, E., Lutz, C., & Tamò-Larrieux, A. (2020). Gathering expert opinions for social robots’ ethical, legal, and societal concerns: Findings from four international workshops. International Journal of Social Robotics, 12(2), 441-458.

Gallagher, A., Cleary, G., Clifford, A., McKee, J., O’Farrell, K., & Gowran, R. J. (2022). “Unknown world of wheelchairs” A mixed methods study exploring experiences of wheelchair and seating assistive technology provision for people with spinal cord injury in an Irish context. Disability and Rehabilitation, 44(10), 1946-1958.

Geravand, M., Werner, C., Hauer, K., & Peer, A. (2016). An integrated decision making approach for adaptive shared control of mobility assistance robots. International Journal of Social Robotics, 8, 631-648.

Gomes, B., Torres, J., Sobral, P., Sousa, A., & Reis, L. P. (2023). Stereo Based 3D Perception for Obstacle Avoidance in Autonomous Wheelchair Navigation. In Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., & Marques, L. (eds), ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems (pp. 321-332), vol 589. Cham: Springer.

Gopichand, M., Rajeswari, K., & Deepthi, E. (2023). Human–Machine Interface for Wheelchair Control Using sEMG Signals. Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021 (pp. 395-406), Vol 2. Singapore: Springer.

Hampshire, L., Dehghani-Sanij, A., & O’Connor, R. J. (2022). Restorative rehabilitation robotics to promote function, independence and dignity: users’ perspectives on clinical applications. Journal of Medical Engineering & Technology, 46(6), 527-535.

Jamil, N., Belkacem, A. N., Ouhbi, S., & Lakas, A. (2021). Noninvasive electroencephalography equipment for assistive, adaptive, and rehabilitative brain–computer interfaces: a systematic literature review. Sensors, 21(14), 4754.

Koide, K., Miura, J., & Menegatti, E. (2019). A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement. International Journal of Advanced Robotic Systems, 16(2), 150310895.

Labbé, M., & Michaud, F. (2019). RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation. Journal of field robotics, 36(2), 416-446.

Leaman, J., La, H. M., & Nguyen, L. (2016, September). Development of a smart wheelchair for people with disabilities. Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 279-284). IEEE.

Lee, H. E., & Cho, J. (2019). Social media use and well-being in people with physical disabilities: Influence of SNS and online community uses on social support, depression, and psychological disposition. Health communication, 34(9), 1043-1052.

Liu, K., Yu, Y., Liu, Y., Tang, J., Liang, X., Chu, X., & Zhou, Z. (2022). A novel brain-controlled wheelchair combined with computer vision and augmented reality. Biomedical Engineering Online, 21(1), 1-20.

Maalouf, N., Sidaoui, A., Elhajj, I. H., & Asmar, D. (2018). Robotics in nursing: a scoping review. Journal of Nursing Scholarship, 50(6), 590-600.

Makhataeva, Z., & Varol, H. A. (2020). Augmented reality for robotics: A review. Robotics, 9(2), 21.

Misch, J., & Sprigle, S. (2022). Propulsion Cost Changes of Ultra-Lightweight Manual Wheelchairs After One Year of Simulated Use. ASME Open Journal of Engineering, 1, 011047.

Mohebbi, A. (2020). Human-robot interaction in rehabilitation and assistance: a review. Current Robotics Reports, 1, 131-144.

Padfield, N., Agius Anastasi, A., Camilleri, T., Fabri, S., Bugeja, M., & Camilleri, K. (2023). BCI-controlled wheelchairs: end-users’ perceptions, needs, and expectations, an interview-based study. Disability and Rehabilitation: Assistive Technology, 11, 1-13.

Perez, A. J., Siddiqui, F., Zeadally, S., & Lane, D. (2022). A review of IoT systems to enable independence for the elderly and disabled individuals. Internet of Things, 21, 100653.

Pingali, T. R. (2019). Development of a Human Accompanying Wheelchair using Ultrasonic Tethering (Doctoral dissertation). Ottawa: University of Ottawa.

Pu, J., Jiang, Y., Xie, X., Chen, X., Liu, M., & Xu, S. (2018). Low cost sensor network for obstacle avoidance in share-controlled smart wheelchairs under daily scenarios. Microelectronics Reliability, 83, 180-186.

Pushp, S., Saikia, A., Khan, A., & Hazarika, S. M. (2018). A cognitively enhanced collaborative control architecture for an intelligent wheelchair: Formalization, implementation and evaluation. Cognitive Systems Research, 49, 114-127.

Rojas, M., Ponce, P., & Molina, A. (2018). A fuzzy logic navigation controller implemented in hardware for an electric wheelchair. International Journal of Advanced Robotic Systems, 15(1), 1729881418755768.

Sahoo, S. K., & Choudhury, B. B. (2021). A Fuzzy AHP Approach to Evaluate the Strategic Design Criteria of a Smart Robotic Powered Wheelchair Prototype. In Udgata, S.K., Sethi, S., & Srirama, S.N. (eds.), Intelligent Systems: Proceedings of ICMIB 2020 (pp. 451-464). Lecture Notes in Networks and Systems, vol 185. Singapore: Springer.

Sahoo, S. K., & Choudhury, B. B. (2023a). A review of methodologies for path planning and optimization of mobile robots. Journal of process management and new technologies, 11(1-2), 122-140.

Sahoo, S. K., & Choudhury, B. B. (2023b). Wheelchair Accessibility: Bridging the Gap to Equality and Inclusion. Decision Making Advances, 1(1), 63–85.

Sahoo, S. K., & Goswami, S. S. (2023). A Comprehensive Review of Multiple Criteria Decision-Making (MCDM) Methods: Advancements, Applications, and Future Directions. Decision Making Advances, 1(1), 25-48.

Sahoo, S. K., Das, A. K., Samanta, S., & Goswami, S. S. (2023). Assessing the Role of Sustainable Development in Mitigating the Issue of Global Warming. Journal of process management and new technologies, 11(1-2), 1-21.

Sahoo, S., & Choudhury, B. (2022). Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique. Decision Science Letters, 11(1), 21-34.

Sahoo, S., & Choudhury, B. (2023c). Voice-activated wheelchair: An affordable solution for individuals with physical disabilities. Management Science Letters, 13(3), 175-192.

Sahoo, S., & Goswami, S. (2024). Theoretical framework for assessing the economic and environmental impact of water pollution: A detailed study on sustainable development of India. Journal of Future Sustainability, 4(1), 23-34.

Salvini, P., Paez-Granados, D., & Billard, A. (2022). Safety concerns emerging from robots navigating in crowded pedestrian areas. International Journal of Social Robotics, 14(2), 441-462.

Singh, H. P., & Kumar, P. (2021). Developments in the human machine interface technologies and their applications: a review. Journal of medical engineering & technology, 45(7), 552-573.

Sun, Z., Zhu, M., Shan, X., & Lee, C. (2022). Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions. Nature communications, 13(1), 5224.

Verma, A., Shrivastava, S., & Ramkumar, J. (2022). Mapping wheelchair functions and their associated functional elements for stair climbing accessibility: a systematic review. Disability and Rehabilitation: Assistive Technology, 25, 1-22.

Voznenko, T. I., Chepin, E. V., & Urvanov, G. A. (2018). The control system based on extended BCI for a robotic wheelchair. Procedia computer science, 123, 522-527.

Xavier Macedo de Azevedo, F., Heimgärtner, R., & Nebe, K. (2022). Development of a metric to evaluate the ergonomic principles of assistive systems, based on the DIN 92419. Ergonomics, 1-28.

Yenugula, M., Sahoo, S., & Goswami, S. (2023). Cloud computing in supply chain management: Exploring the relationship. Management Science Letters, 13(3), 193-210.

Yenugula, M., Sahoo, S., & Goswami, S. (2024). Cloud computing for sustainable development: An analysis of environmental, economic and social benefits. Journal of future sustainability, 4(1), 59-66.

Yoon, H. U., Wang, R. F., Hutchinson, S. A., & Hur, P. (2017). Customizing haptic and visual feedback for assistive human–robot interface and the effects on performance improvement. Robotics and Autonomous Systems, 91, 258-269.

Zhang, Y., & Xu, S. C. (2015). Ros based voice-control navigation of intelligent wheelchair. Applied Mechanics and Materials, 733, 740-744.

Published

01.12.2023

How to Cite

Sahoo, S. K., & Choudhury, B. B. (2023). A review on smart robotic wheelchairs with advancing mobility and independence for individuals with disabilities. Journal of Decision Analytics and Intelligent Computing, 3(1), 221–242. https://doi.org/10.31181/10001122023s