AI ADVANCES IN WHEELCHAIR NAVIGATION AND CONTROL: A COMPREHENSIVE REVIEW

Sushil Kumar Sahoo; Bibhuti Bhusan  CHOUDHURY

doi:10.5937/jpmnt11-45181

Sushil Kumar Sahoo Biju Patnaik University of Technology (BPUT), Rourkela, Odisha, India
Bibhuti Bhusan CHOUDHURY Indira Gandhi Institute of Technology (IGIT), Sarang, Odisha, India

DOI: https://doi.org/10.5937/jpmnt11-45181

Sažetak

This paper presents a systematic review of the literature on integrating artificial intelligence (AI) to improve wheelchair navigation and control for people with mobility impairments. The review covers a range of AI-based approaches including computer vision, machine learning, and path planning algorithms. The paper highlights the potential benefits of integrating AI into wheelchair technology, including increased safety, autonomy, and personalized control. The review discusses the limitations and challenges of current wheelchair navigation and control systems, and how AI can address these limitations. The paper identifies common themes and trends in the literature and summarizes the strengths and weaknesses of existing AI-based wheelchair navigation and control systems. Finally, the paper concludes by discussing the potential future directions for research and development of AI-based wheelchair navigation and control systems. This review paper provides a valuable resource for researchers and engineers interested in developing and improving AI-based wheelchair technology.

Reference

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.

Arshad, J., Ashraf, M. A., Asim, H. M., Rasool, N., Jaffery, M. H., & Bhatti, S. I. (2023). Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques. Electronics, 12(5), 1132.

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.

Bolturk, E., & Kahraman, C. (2021). Humanoid robots and fuzzy sets. Toward Humanoid Robots: The Role of Fuzzy Sets: A Handbook on Theory and Applications, 3-25.

Bourahmoune, K., & Amagasa, T. (2019, August). AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the Life Chair Smart Cushion. In IJCAI (pp. 5808-5814).

Bourbakis, N. G. (2022). Challenges in Assistive Living Based on Tech Synergies: The Cooperation of a Wheelchair and A Wearable Device. Advances in Assistive Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis, 3, 237-262.

Castro, D., & New, J. (2016). The promise of artificial intelligence. Center for Data Innovation, 115(10), 32-35.

Chandra, E. (2022). A Novel N-Average Wavelet Algorithm for a Voice-Based Wheel Chair. In Empowering Artificial Intelligence Through Machine Learning (pp. 217-226). Apple Academic Press.

Daza, J. F., Cuthbertson, B. H., Myles, P. S., Shulman, M. A., & Wijeysundera, D. N. (2022). Measurement properties of the WHO Disability Assessment Schedule 2.0 for evaluating functional status after inpatient surgery. British Journal of Surgery, 109(10), 968-976.

Desai, S., Mantha, S. S., & Phalle, V. M. (2017, January). Advances in smart wheelchair technology. In 2017 International Conference on Nascent Technologies in Engineering (ICNTE) (pp. 1-7). IEEE.

Dey, P., Hasan, M. M., Mostofa, S., & Rana, A. I. (2019, January). Smart wheelchair integrating head gesture navigation. In 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 329-334). IEEE.

Fosch-Villaronga, E., & Drukarch, H. (2022). AI for healthcare robotics. CRC Press.

Ghosh, A. (2021). Artificial intelligence in bringing about a revolution in the healthcare industry. BODHI International Journal of Research in Humanities, Arts and Science, 5(1), 98.

Goswami, S. S., Behera, D. K., & Mitra, S. (2020). A comprehensive study of weighted product model for selecting the best product in our daily life. Brazilian Journal of Operations & Production Management, 17(2), 1-18.

Guo, K., Orban, M., Lu, J., Al-Quraishi, M. S., Yang, H., & Elsamanty, M. (2023). Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System. Bioengineering, 10(5), 557.

Hemmati, A., & Rahmani, A. M. (2022). The Internet of Autonomous Things applications: A taxonomy, technologies, and future directions. Internet of Things, 20, 100635.

Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics. Sensors, 21(19), 6340.

Ienca, M., & Ignatiadis, K. (2020). Artificial intelligence in clinical neuroscience: methodological and ethical challenges. AJOB neuroscience, 11(2), 77-87.

Jameel, H. F., Gharghan, S. K., & Mohammed, S. L. (2022). Wheelchair control system for the disabled based on EMOTIV sensor gyroscope. Microprocessors and Microsystems, 94, 104686.

Kambhamettu, S., Vimal Cruz, M., Anitha, S., Sibi Chakkaravarthy, S., & Nandeesh Kumar, K. (2023). Brain–Computer Interface‐Assisted Automated Wheelchair Control Management–Cerebro: A BCI Application. Brain‐Computer Interface: Using Deep Learning Applications, 205-229.

Kane, S. K., Guo, A., & Morris, M. R. (2020, October). Sense and accessibility: Understanding people with physical disabilities’ experiences with sensing systems. In Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-14).

Kumar, A., Jain, R., Gupta, M., & Islam, S. M. (Eds.). (2023). 6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis. CRC Press.

Lott, J. S., Young, S. E., Duvall, A., & Henao, A. (2022). Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles. In Automated People Movers and Automated Transit Systems 2022 (pp. 48-60).

Mathur, S., Gupta, D., & Rana, A. (2021, September). Solar Power based Hybrid Wheelchair. In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1-3). IEEE.

McCoy, K. F., Arnott, J. L., Ferres, L., Fried-Oken, M., & Roark, B. (2013). Speech and language processing as assistive technologies. Computer Speech & Language, 27(6), 1143-1146.

McLean, G., Osei-Frimpong, K., & Barhorst, J. (2021). Alexa, do voice assistants influence consumer brand engagement?–Examining the role of AI powered voice assistants in influencing consumer brand engagement. Journal of Business Research, 124, 312-328.

Niijima, S., Sasaki, Y., & Mizoguchi, H. (2019). Real-time autonomous navigation of an electric wheelchair in large-scale urban area with 3D map. Advanced Robotics, 33(19), 1006-1018.

Ouallane, A. A., Bakali, A., Bahnasse, A., Broumi, S., & Talea, M. (2022). Fusion of engineering insights and emerging trends: Intelligent urban traffic management system. Information Fusion.

Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human factors, 39(2), 230-253.

Rajapaksha, S., Illankoon, V., Halloluwa, N. D., Satharana, M., & Umayanganie, D. (2019, December). Responsive drone autopilot system for uncertain natural language commands. In 2019 International Conference on Advancements in Computing (ICAC) (pp. 232-237). IEEE.

Ruffing, J. J., Schmeler, M. R., Schein, R. M., & Mhatre, A. (2022). A cross-sectional descriptive analysis of complex rehabilitation technology (CRT) supplier opinions on the current state of wheelchair repair services. Disability and Rehabilitation: Assistive Technology, 1-6.

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 Intelligent Systems: Proceedings of ICMIB 2020 (pp. 451-464). Singapore: Springer Singapore.

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. (2023). 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.

Singh, A. K., & Gaharwar, M. (2018). Artificial intelligence based wireless smart wheelchair. International Journal of Emerging Technologies in Engineering Research (IJETER), 6(3).

Subashini, P., & Krishnaveni, M. (2021). Artificial Intelligence-Based Assistive Technology. In Artificial Intelligence Theory, Models, and Applications (pp. 217-240). Auerbach Publications.

Subramanian, M., & Faisal, A. A. (2022). Natural Gaze Informatics: Toward Intelligence Assisted Wheelchair Mobility. In Eye Tracking: Background, Methods, and Applications (pp. 97-112). New York, NY: Springer US.

Sun, X., Ding, J., Dong, Y., Ma, X., Wang, R., Jin, K, & Zhang, Y. (2023). A survey of technologies facilitating home and community-based stroke rehabilitation. International Journal of Human–Computer Interaction, 39(5), 1016-1042.

Tatano, V., & Revellini, R. (2023). An alternative system to improve accessibility for wheelchair users: the stepped ramp. Applied ergonomics, 108, 103938.

Tonin, L., Menegatti, E., & Coyle, D. (2021). Advances in the Integration of Brain-Machine Interfaces and Robotic Devices. Frontiers in Robotics and AI, 8, 653615.

Uma, S. (2023). Potential Integration of Artificial Intelligence and Biomedical Research Applications: Inevitable Disruptive Technologies for Prospective Healthcare. In Handbook of Research on AI Methods and Applications in Computer Engineering (pp. 363-386). IGI Global.

Wang, Y., & Chen, W. (2011, May). Hybrid map-based navigation for intelligent wheelchair. In 2011 IEEE International Conference on Robotics and Automation (pp. 637-642). IEEE.

Wangmo, T., Lipps, M., Kressig, R. W., & Ienca, M. (2019). Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders. BMC medical ethics, 20(1), 1-11.

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.

Yüksel, N., Börklü, H. R., Sezer, H. K., & Canyurt, O. E. (2023). Review of artificial intelligence applications in engineering design perspective. Engineering Applications of Artificial Intelligence, 118, 105697.

Zdravkova, K. (2022). The Potential of Artificial Intelligence for Assistive Technology in Education. In Handbook on Intelligent Techniques in the Educational Process: Vol 1 Recent Advances and Case Studies (pp. 61-85). Cham: Springer International Publishing.