Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Authors

DOI:

https://doi.org/10.18196/jrc.v5i1.20057

Keywords:

Convolutional Neural Network (CNN), Deep Learning (Dl), Global Positioning System (GPS), Smart Home Management, Cutting-Edge Technologies, Voice Control, Remote Monitoring, Accessibility, Inclusivity, Assistive Technology, Disability Support

Abstract

This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

Author Biographies

Ninura Jayasekara, Sri Lanka Institute of Information Technology

Ninura Jayasekara currently enrolled as a undergraduate and  pursuing a Bachelor of Science Software Engineering degree at the Computer Science and the software Engineering Department, Faculty of computing, Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka.
Currently doing academic studies and learning new technologies by applying them to real-world applications. Interested in learning further on Full Stack Developing and looking forward to furthering contributions to the field of Software Engineering.

Binali Kulathunge, Sri Lanka Institute of Information Technology

Binali Kulathunge is a dedicated undergraduate student following a Bachelor of Science degree in Computer Systems and Network Engineering at the Department of Computer Systems, Faculty of Computing, Sri Lanka Institute of Information technology (SLIIT), Sri Lanka.
With a profound interest in the fields of computer systems, robotics, and networking, aim to contribute innovative solutions to enhance these domains.  Looking forward to furthering contributions to the field of computer systems and network engineering, with a strong commitment to developing innovative solutions.

Hirudika Premaratne, Sri Lanka Institute of Information Technology

Hirudika Premaratne is  a committed undergraduate student pursuing a Bachelor of Science degree in Computer Systems and Network Engineering at the Sri Lanka Institute of Information Technology. His deep passion lies in the realms of computer systems, robotics, and networking, and aspires to bring forth groundbreaking solutions to advance these areas.

Insaf Nilam, Sri Lanka Institute of Information Technology

With an unwavering passion for technology and innovation, Insaf Nilam wholeheartedly dedicated to coding, programming, and improving software development skills with the aim of crafting cutting-edge solutions for real-world challenges. Throughout the academic pursuit, not solely focused on acquiring technical expertise; but, equally committed to developing a profound understanding of technology's profound societal impact.  Firmly believe in the transformative power of technology to address some of the most pressing global issues.

Samantha Rajapaksha, Sri Lanka Institute of Information Technology

Samantha Rajapaksha received B.Sc.(Eng.)(Hons) in Computer Science and Engineering from University of Moratuwa in Sri Lanka and completed the Ph.D. and M.Sc. in Information Technology from Sri Lanka Institute of Information Technology.

In 2002 joined to Sri Lanka Institute of Information Technology (SLIIT) as lecturer and currently working as Senior lecturer in the SLIIT. Currently working on research work in relation to robot programming in robot automation, semantic web and ontology-based development.

Jenny Krishara, Sri Lanka Institute of Information Technology

Jenny Krishara received B.Sc.(Hons) in Information Technology and M.Sc. in Information Technology from Sri Lanka Institute of Information Technology.
In 2016, joined Sri Lanka Institute of Information Technology (SLIIT) as an Academic Instructor and currently working as a lecturer at SLIIT. Currently working on research work in relation to Natural Language Processing, Artificial Intelligence, Semantic web and ontology-based development.

References

H. H. Fore, “Research and Evidence on Children with Disabilities,” UNICEF for Every Child, https://www.unicef-irc.org/ children-with-disabilities, (accessed Oct. 26, 2023).

T. A. Ghebreyesus, “Almost one billion children and adults with disabilities and older persons in need of assistive technology denied access, according to new report,” WHO, https://bit.ly/4ahLoo0, (accessed Oct. 26, 2023).

J. J. Zhang, J. Y. Li, J. M. Liu, and Y. M. Wu, “A hybrid deep learning method for software defect prediction,” IEEE Access, vol. 7, pp. 23551- 23562, 2019.

P. Shafiabady and F. Kiani, “Investigating the effect of the geometrical parameters on the thermal performance of nanofluid-filled microchannel heat sink,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 11, pp. 4509-4513, 2019, doi: 10.3390/su11164509.

T. A. Alharthi, Y. M. Sami, and H. B. Ahmed, “Smart irrigation system using internet of things,” IEEE Access, vol. 8, pp. 171931-171942, 2020.

H. Zhang, X. Shen, Y. Zhao, and Y. Sun, “A smart city system based on Internet of Things and cloud computing,” IEEE Access, vol. 9, pp. 481-489, 2021.

Y. Zhou, X. Li, and M. Yin, “A machine learning approach for predicting bank customer churn,” IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 3344-3352, 2021.

M. M. Khan, S. N. Safa, M. H. Ashik, M. Masud, and M. A. AlZain, “Research and development of a brain-controlled wheelchair for paralyzed patients,” in Proceedings of the 2019 2nd International Conference on Intelligent Autonomous Systems, pp. 47-52, 2019, doi: 10.32604/iasc.2021.016077.

S. S. Raza, S. Ahmad, M. A. Khan, and A. Hussain, “Design and development of a low-cost wireless sensor network for landslide monitoring,” IEEE Access, vol. 8, pp. 41304-41313, 2020.

S. Wang, Z. Wu, and Y. Liu, “A novel privacy-preserving scheme for vehicular clouds,” in Advances in Intelligent Systems and Computing, vol. 858, pp. 63-70, 2019, doi: 10.1016/j.repl.2019.02.010.

X. Zhang, Z. Li, and W. Wang, “Design and implementation of a smart home security system based on IoT and cloud computing,” IEEE Access, vol. 8, pp. 2394-2404, 2020.

E. M. -Martin, and F. M. -Espejo, “Deep Learning Techniques for Spanish Sign Language Interpretation,” Computational Intelligence and Neuroscience, vol. 2021, pp. 1-10, 2021, doi: 10.1155/2021/5532580.

N. Yigzaw, M. Meshesha, and C. Diriba, “A Generic Approach towards Amharic Sign Language Recognition,” Advances in Human-Computer Interaction, vol. 2022, pp. 1-11, 2022, doi: 10.1155/2022/1112169.

J. Zheng, Z. Zhao, M. Chen, J. Chen, C. Wu, Y. Chen, X. Shi, and Y. Tong, “An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences,” Computational Intelligence and Neuroscience, vol. 2020, pp. 1-11, 2020, doi: 10.1155/2020/8816125.

Z. Gao, C. -C. Lee, L. Zheng, R. Zhang, and X. Xu, “A Multitask Sign Language Recognition System Using Commodity Wi-Fi,” Mobile Information Systems, vol. 2023, pp. 11-11, 2023, doi: 10.1155/2023/ 7959916.

A. Mannan, A. Abbasi, A. R. Javed, A. Ahsan, T. R. Gadekallu, and Q. Xin, “Hypertuned Deep Convolutional Neural Network for Sign Language Recognition,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1-10, 2022, doi: 10.1155/2022/1450822.

A. M. J. AL Moustafa, M. S. M. Rahim, B. Bouallegue, M. M. Khattab, A. M. Soliman, G. Tharwat, and A. M. Ahmed, “Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition,” Journal of Electrical and Computer Engineering, vol. 2023, pp. 1-15, 2023, doi: 10.1155/2023/8870750.

G. Li, Q. Li, C. Yang, Y. Su, Z. Yuan and X. Wu, “The Classification and New Trends of Shared Control Strategies in Telerobotic Systems: A Survey,” in IEEE Transactions on Haptics, vol. 16, no. 2, pp. 118-133, 2023, doi: 10.1109/TOH.2023.3253856.

K. T. Alhamazani, J. Alshudukhi, T. S. Alharbi, S. Aljaloud, and Z. Meraf, “Using Depth Cameras for Recognition and Segmentation of Hand Gestures,” Advances in Materials Science and Engineering, vol. 2021, pp. 1-6, 2021, doi: 10.1155/2021/7100727.

J. Mouchoux, M. A. B. -Cabrera, S. Dosen, A. F. Schilling, and M. Markovic, “Impact of Shared Control Modalities on Performance and Usability of Semi-autonomous Prostheses,” Frontiers in Neurorobotics, vol. 15, 2021, doi: 10.3389/fnbot.2021.768619.

R. Alkhatib, A. Swaidan, J. Marzouk, M. Sabbah, S. Berjaoui and M. O. Diab, “Smart Autonomous Wheelchair,” 2019 3rd International Conference on Bio-engineering for Smart Technologies, 2019, pp. 1-5, doi: 10.1109/BIOSMART.2019.8734264.

N. Thongpance, and P. Chotikunnan, “Design and Construction of Electric Wheelchair with Mecanum Wheel,” Journal of Robotics and Control, vol. 4, no. 1, pp. 71-82, 2023, doi: 10.18196/jrc.v4i1.17095.

L. Yang, N. Guo, R. Sakamoto, N. Kato, and K. Yano, “Electric Wheelchair Hybrid Operating System Coordinated with Working Range of a Robotic Arm,” Journal of Robotics and Control, vol. 3, no. 5, pp. 679-689, 2022, doi: 10.18196/jrc.v3i5.15944.

J. P. A. Ogenga, W. Njeri, M. Joseph, “Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities,” Journal of Robotics and Control, vol. 4, no. 2, pp. 165-178, 2023, doi: 10.18196/jrc.v4i2.17165.

S. Perera, S. Gamage, C. Weerasinghe, C. Jayawardena, K. Pathinayake and S. Rajapaksha, “Intelligent Wheelchair with Emotion Analysis and Voice Recognition,” 2022 7th International Conference on Information Technology Research, 2022, pp. 1-6, doi: 10.1109/ICITR57877.2022. 9992532.

Y. Jung, Y. Kim, W. H. Lee, M. S. Bang, Y. Kim and S. Kim, “Path Planning Algorithm for an Autonomous Electric Wheelchair in Hospitals,” in IEEE Access, vol. 8, pp. 208199-208213, 2020, doi: 10.1109/ACCESS.2020.3038452.

Qing Lv, Suzhen Zhang, Yuechun Wang, “Deep Learning Model of Image Classification Using Machine Learning,” Advances in Multimedia, vol. 2022, pp. 1-12, 2022, doi: 10.1155/2022/3351256.

M. J. Khan, I. Awan, and Z. A. Rana, “Deep Learning in Mobile Information Systems,” Mobile Information Systems, vol. 2021, pp. 1- 2, 2021, doi: 10.1155/2021/1296849.

M. J. Iqbal, M. M. Iqbal, I. Ahmad, M. O. Alassafi, A. S. Alfakeeh, and A. Alhomoud, “Real-Time Surveillance Using Deep Learning,” Security and Communication Networks, vol. 2021, pp 1-17, 2021, doi: 10.1155/ 2021/6184756.

W. Cao, H. Yu, X. Wu, S. Li, Q. Meng, and C. Chen, “Development and Evaluation of a Rehabilitation Wheelchair with Multiposture Transformation and Smart Control,” Complexity, vol. 2021, pp. 1-14, 2021, doi: 10.1155/2021/6628802.

N. Aktar, I. Jaharr and B. Lala, “Voice Recognition based intelligent Wheelchair and GPS Tracking System,” 2019 International Conference on Electrical, Computer and Communication Engineering, pp. 1-6, 2019, doi: 10.1109/ECACE.2019.8679163.

V. Vedanarayanan, C. Vibhakar, A. Sujaatha, J. K. Chavda, M. Karthik, P. V. Pramila, and I. K. Raghavan, “Utilization of Sustainable Resources for Promoting Energy Efficiency in Environment Using Smart Technologies,” International Journal of Photoenergy, vol. 2022, pp. 1-9, 2022, doi: 10.1155/2022/6711300.

J. Wang, L. Zhang, Y. Huang, and J. Zhao, “Safety of Autonomous Vehicles”, Journal of Advanced Transportation, vol. 2020, pp. 1-13, 2020. doi: 10.1155/2020/8867757.

S. K. Sahoo, and B. B. Choudhury, “A Review on Human-Robot Interaction and User Experience in Smart Robotic Wheelchairs,” Journal of Technology Innovations and Energy, vol. 2, no. 2, pp. 39–55, 2023, doi: 10.56556/jtie.v2i2.576.

Security and Communication Networks, “Retracted: Artistic Design Method of User Interaction Experience of Mobile System Based on Context Awareness,” Security and Communication Networks, vol. 2022, p. 1, 2022, doi: 10.1155/2022/9897426.

Z. Syahputra, “Website Based Sales Information System With The Concept Of Mvc (Model View Controller): Website Based Sales InformationSystem With The Concept Of Mvc (Model View Controller),” Mantik, vol. 4, no. 2, pp. 1133–1137, 2020, doi: 10.35335/mantik.Vol4. 2020.915.pp1133-1137.

M. Patel, S. Gupta, and A. Jain, “E-commerce Application Development Using Flutter and Firebase: A Case Study,” in IEEE Internet Computing, 2022.

P. Peng, Y. Ju, Y. Zhang, K. Wang, S. Jiang and Y. Wang, “Sparse Representation and Dictionary Learning Model Incorporating Group Sparsity and Incoherence to Extract Abnormal Brain Regions Associated With Schizophrenia,” in IEEE Access, vol. 8, pp. 104396-104406, 2020, doi: 10.1109/ACCESS.2020.2999513.

R. W. Attar, A. Almusharraf, A. Alfawaz, and N. Hajli, “New Trends in E-Commerce Research: Linking Social Commerce and Sharing Commerce: A Systematic Literature Review,” Sustainability, vol. 14, no. 23, 2022, doi: 10.3390/su142316024.

N. T. Reddy, P. V. Sai, B. M. Chaitanya, G. A. S. Thomas and P. S. Rao, “cloud based e-commerce application with flutter and firebase,” in International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 6, pp. 1917–1919, 2022.

Y. Kudo, S. Kino, and Y. Matsuura, “Vacuum Ultraviolet Absorption Spectroscopy Analysis of Breath Acetone Using a Hollow Optical Fiber Gas Cell,” Sensors, vol. 21, no. 2, 2021, doi: 10.3390/s21020478.

Y. Touati, A. Ali-Cherif and B. Achili, “Smart Wheelchair design and ´ monitoring via wired and wireless networks,” 2009 IEEE Symposium on Industrial Electronics & Applications, pp. 920-925, 2009 doi: 10.1109/ ISIEA.2009.5356324.

Y. Rabhi, M. Mrabet, and F. Fnaiech, “Intelligent Control Wheelchair Using a New Visual Joystick,” Journal of Healthcare Engineering, vol. 2018, pp. 1-20, 2018, doi: 10.1155/2018/6083565.

M. Z. U. Rahman, I. Ali, A. Ishfaque, M. T. Riaz, N. Ahmad, M. M. S. A. Mahmud, and R. Tanveer, “Design, Analysis, and Control of Biomedical Healthcare Modular Wheelchair with Posture Transformation,” Complexity, vol. 2023, pp. 1-13, 2023, doi: 10.1155/2023/7310265.

R. J. Oskouei, Z. MousaviLou, Z. Bakhtiari, and K. B. Jalbani, “IoTBased Healthcare Support System for Alzheimer’s Patients,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1-15, 2020, doi: 10.1155/2020/8822598.

S. Desai, S. S. Mantha and V. M. Phalle, “Advances in smart wheelchair technology,” 2017 International Conference on Nascent Technologies in Engineering, pp. 1-7, 2017, doi: 10.1109/ICNTE.2017.7947914.

H. Huang, H. Tao, Y. Chen, and Y. Zhang, “Research on intelligent wheelchair system based on multisensory perception,” in 2019 IEEE 5th International Conference on Computer and Communications, pp. 496-501, 2019.

D. Lee and D. Lee, “Development of an intelligent wheelchair system based on a multimodal interface,” in 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, pp. 568-573, 2019.

J. Kim, J. Hwang, Y. Lee, C. Lee, and S. Lee, “A Smart Wheelchair with User-Friendly Touchscreen Interface,” in 2020 IEEE 17th International Conference on Ubiquitous Robots, pp. 578-583, 2020.

J. Liu, Z. Li and X. Li, “Design of an Intelligent Wheelchair Navigation System Based on Vision and Multi-Sensors,” 2020 IEEE 4th International Conference on Control Science and Systems Engineering, pp. 758-762, 2020.

Y. Wu, X. Wu, Y. Shang, H. Liu, J. Yu and W. Zhang, “A Smart Wheelchair System for People with Severe Disabilities,” 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification, pp. 184-189, 2019.

C. Yang, X. Li, X. Li and C. Li, “Design and Implementation of Smart Wheelchair System Based on Joystick,” 2019 IEEE 6th International Conference on Industrial Engineering and Applications, pp. 562-566, 2019.

S. Zhao, J. Duan, J. Hu and J. Xu, “A Speech Recognition-Based Hands-Free Operation System for a Smart Wheelchair,” 2018 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics, pp. 329-333, 2018.

S. Ghosh, N. Das, I. Das, and U. Maulik, “Understanding Deep Learning Techniques for Image Segmentation,” ACM Computing Surveys, vol. 52, no. 4, pp. 1-35, doi: 10.1145/3329784.

G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis and R. Willett, “Deep Learning Techniques for Inverse Problems in Imaging,” in IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 1, pp. 39-56, 2020, doi: 10.1109/JSAIT.2020.2991563.

B. Selcuk, and T. Serif, “A Comparison of YOLOv5 and YOLOv8 in the Context of Mobile UI Detection,” International Conference on Mobile Web and Intelligent Information Systems Lecture Notes in Computer Science, vol. 13977, pp. 161-174, 2023, doi: 10.1007/978-3-031-39764-6_11.

Z. Khan, Y. Shen, and H. Liu, and X. Zeng, “Improved Yolov8 Deep Learning Instance Segmentation Algorithm with Dilated Convolution: Real Time Precise Segmentation of Orchard Canopies in Natural Environment,” pp. 1-13, 2023, doi: 10.2139/ssrn.4604445.

M. Alloghani, D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf, “A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Scienc,” Supervised and Unsupervised Learning for Data Science, pp. 3-21, 2019, doi: 10.1007/978-3-030-22475-2_1.

M. A. M. El-Samak, M. A. Kishk, M. A. Zidan, “Environmental Data Analysis and Prediction Using Machine Learning Techniques,” in IEEE Access, 2021.

S. Lersilp, S. Putthinoi, and T. Lersilp, “Facilitators and Barriers of Assistive Technology and Learning Environment for Children with Special Needs,” Occupational Therapy International, vol. 2018, pp. 1-9, 2018, doi: 10.1155/2018/3705946.

S. S. Alomari, M. H. H. A. Shukor, M. F. A. Rasid, and R. Mahmud, “Sign Language Recognition using Deep Learning: A Survey and Research Directions,” in IEEE Access, 2020.

J. Yao, L. Zhang, and Y. Dong, “A Comprehensive Survey of Recent Advances in Convolutional Neural Networks,” in IEEE Transactions on Neural Networks and Learning Systems, 2021.

J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen, “Recent advances in convolutional neural networks,” Pattern Recognition, vol. 77, pp. 354-377, 2018, doi: 10.1016/j.patcog.2017.10.013.

W. Rawat and Z. Wang, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” in Neural Computation, vol. 29, no. 9, pp. 2352-2449, 2017, doi: 10.1162/neco_a_00990.

J. Zhang, Y. Wang, and H. Li, “Advancements in Convolutional Neural Networks for Image Classification: A Comprehensive Survey,” in IEEE Transactions on Neural Networks and Learning Systems, 2021.

C. Lim, K. K. Au, T. H. Y. Kwong, “Assistive Technology Outcomes: The Importance of Stakeholder Collaboration and User Engagement,” in IEEE Transactions on Neural and Rehabilitation Engineering, 2020.

S. Mendelsohn, D. L. Edyburn, K. L. Rust, T. D. Schwanke, and R. O. Smith, “Using Assistive Technology Outcomes Research to Inform Policy Related to the Employment of Individuals With Disabilities,” Assistive Technology, vol. 20, no. 3, pp. 139-148, 2010, doi: 10.1080/ 10400435.2008.10131941.

X. Zhou, L. Zhang, D. Wang, “A Review of Environmental Data Science Applications: Challenges, Approaches, and Opportunities,” in IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3081440.

B. AlKhlidi, A. T. Abdulsadda, and A. A. Bakri, “Optimal Robotic Path Planning Using Intelligents Search Algorithms,” Journal of Robotics and Control, vol. 2, no. 6, pp. 519-526, 2021, doi: 10.18196/jrc.26132.

N. S. Abu, W. M. Bukhari, M. H. Adli, S. N. Omar, and S. A. Sohaimeh, “A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots,” Journal of Robotics and Control, vol. 3, no. 5, pp. 666-678, 2022, doi: 10.18196/jrc.v3i5.14683.

L. D. Hanh, and V. D. Cong, “Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning,” Journal of Robotics and Control, vol. 4, no. 2, pp. 157-164, 2023, doi: 10.18196/jrc.v4i2.17368.

A. Ubaidillah, and H. Sukri, “Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot,” Journal of Robotics and Control, vol. 4, no. 3, pp. 413-423, 2023, doi: 10.18196/jrc.v4i3.18489.

Y. Deng, T. Li, M. Xie and W. Chen, “Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles,” in IEEE Access, vol. 11, pp. 27893-27918, 2023, doi: 10.1109/ACCESS.2023.3257570.

T. P. Tunggal, S. A. Juliani, H. A. Widodo, R. A. Atmoko, and P. T. Nguyen, “The Design of Digital Heart Rate Meter Using Microcontroller,” Journal of Robotics and Control, vol. 1, no. 5, pp. 141-144, 2020, doi: 10.18196/jrc.1529.

H. Yu and Z. Zhou, “Optimization of IoT-Based Artificial Intelligence Assisted Telemedicine Health Analysis System,” in IEEE Access, vol. 9, pp. 85034-85048, 2021, doi: 10.1109/ACCESS.2021.3088262.

E. M. -Martin and M. Cazorla, “A Socially Assistive Robot for Elderly Exercise Promotion,” in IEEE Access, vol. 7, pp. 75515-75529, 2019, doi: 10.1109/ACCESS.2019.2921257.

V. Raghunathan, S. S. Dhillon, and J. Ghosh, “Speech Recognition for Mobile Health Applications: Promises and Challenges,” 2021.

S. Aggarwal and S. Sharma, “Voice Based Deep Learning Enabled User Interface Design For Smart Home Application System,” 2021 2nd International Conference on Communication, Computing and Industry 4.0, pp. 1-6, 2021, doi: 10.1109/C2I454156.2021.9689435.

A. Smith and B. Johnson, “Enhancing Mobile Accessibility with Speech Recognition: Challenges and Solutions,” 2019.

S. Yin et al., “Low-power neuromorphic speech recognition engine with coarse-grain sparsity,” 2017 22nd Asia and South Pacific Design Automation Conference, pp. 111-114, 2017, doi: 10.1109/ASPDAC. 2017.7858305.

T. A. Khoa et al., “Designing Efficient Smart Home Management with IoT Smart Lighting: A Case Study,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1-18, 2020, doi: 10.1155/2020/8896637.

S. K. A. Shah, and W. Mahmood, “Smart home automation using IoT and its low cost implementation,” International Journal of Engineering and Manufacturing, vol. 10, no. 5, pp.28-36, 2020, doi: 10.5815/ijem.2020.05.03.

Q. Luo, “The Improving Effect of Intelligent Speech Recognition System on English Learning,” Advances in Multimedia, vol. 2022, pp. 1-13, 2022, doi: 10.1155/2022/2910859.

Z. Shen, H. V. Krimpen, and M. Spruit, “A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems,” Journal of Healthcare Engineering, vol. 2019, pp. 1-11, 2019, doi: 10.1155/2019/3435609.

J. Muslehiddinoglu et al., “Technical Considerations for Use of Oligonucleotide Solution API”, Nucleic Acid Therapeutics, vol. 30, no. 4, pp. 189-197, 2020, doi: 10.1089/nat.2020.0846.

J. -Y. Fourniols, N. Nasreddine, C. Escriba, P. Acco, J. Roux, and G. Romero, “An Overview of Basics Speech Recognition and Autonomous Approach for Smart Home IoT Low Power Devices,” Journal of Signal and Information Processing, vol. 9, pp. 239-257, 2018, doi: 10.4236/jsip.2018.94015.

C. Kato, T. Nagashima, S. Komatsu, S. Matsumoto, H. Shibutani, K. Aoki, and K. Tsuchida, “Evaluation of Layouts for Braille Guidance Blocks under Different Situations in Sports Facilities,” Psychology, vol. 10, no. 4, pp. 595-607, 2019, doi: 10.4236/psych.2019.104038.

J. -X. Zhang, H. -F. Chen, B. Chen, B. -Q. Chen, J. -H. Zhong, X. -Q. Zeng, “Design and Implementation of Chinese Common Braille Translation System Integrating Braille Word Segmentation and Concatenation Rules,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1- 14, 2022, doi: 10.1155/2022/8934241.

O. Taiwo, and A. E. Ezugwu, “Internet of Things-Based Intelligent Smart Home Control System”, Security and Communication Networks, vol. 2021, pp. 1-17, 2021, doi: 10.1155/2021/9928254.

S. Shokat, R. Riaz, S. S. Rizvi, K. Khan, F. Riaz, and S. J. Kwon, “Analysis and Evaluation of Braille to Text Conversion Methods,” Mobile Information Systems, vol. 2020, pp. 1-14, 2020, doi: 10.1155/2020/3461651.

X. Yu, Z. Xie, Y. Yu et al, “Skin-integrated wireless haptic interfaces for virtual and augmented reality,” Nature, pp. 473–479, 2019, doi: 10. 1038/s41586-019-1687-0.

T. -H. Yang, J. R. Kim, H. Jin, H. Gil, J. -H. Koo, and H. J. Kim, “Recent Advances and Opportunities of Active Materials for Haptic Technologies in Virtual and Augmented Reality,” Advance Functional Materials, vol. 31, no. 39, 2021, doi: 10.1002/adfm.202008831.

S. K. Nataraj, F. Al-Turjman, A. H. B. Adom, S. R, R. M and K. R, “Intelligent Robotic Chair With Thought Control and Communication Aid Using Higher Order Spectra Band Features,” in IEEE Sensors Journal, vol. 22, no. 18, pp. 17362-17369, 2022, doi: 10.1109/JSEN.2020.3020971.

N. Ng, P. Gaston, P. M. Simpson, G. J. Macpherson, J. T. Patton, and N. D. Clement, “Robotic arm-assisted versus manual total hip arthroplasty,” The Bone and Joint Journal, vol. 103-B, no. 6, pp. 1009-1020, doi: 10.1302/0301-620X.103B6.BJJ-2020-1856.R1.

L. M. John, N. Mistry, R. Patel, D. Nagvekar, “Over-The-Top (OTT) Media Application using Flutter,” Proceedings of the 4th International Conference on Advances in Science & Technology, pp. 1-6, 2021, doi: 10.2139/ssrn.3867569.

Z. Daizhi, L. Zeqi, Z. Tianxiang, W. Zhiyuan and S. Yaochen, “Design and Research of Braille Machine Control System Supported by Human-Computer Interaction,” 2021 IEEE Conference on Telecommunications, Optics and Computer Science, pp. 802-805, 2021, doi: 10.1109/TOCS53301.2021.9688819.

V. Shah and N. Sajnani, “Multi-Class Image Classification using CNN and Tflite,” International Journal of Research in Engineering, Science and Management, vol. 3, no. 11, pp. 65–68, 2020, doi: 10.47607/ijresm.2020.375.

V. Kocaman, and D. Talby, “Spark NLP: Natural Language Understanding at Scale,” Software Impacts, vol. 8, pp. 1-5, 2021, doi: 10.1016/j.simpa.2021.100058.

L. Lin, H. He, Z. Xu, and D. Wu, “Realtime Vehicle Tracking Method Based on YOLOv5+DeepSORT,” Computational Intelligence and Neuroscience, vol. 2023, pp 1-11, 2023, doi: 10.1155/2023/7974201.

A. Dixit et al., “Audio to Indian and American Sign Language Converter using Machine Translation and NLP Technique,” 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies, pp. 874-879, 2022, doi: 10.1109/ICICICT54557.2022.9917614.

R. P, K. Suresh, B. M, M. Gokul, G. D. Kumar and A. R, “IoT Based Voice Assistant using Raspberry Pi and Natural Language Processing,” 2022 International Conference on Power, Energy, Control and Transmission Systems, pp. 1-4, 2022, doi: 10.1109/ICPECTS56089.2022.10046890.

T. N. Rincy and R. Gupta, “Ensemble Learning Techniques and its Efficiency in Machine Learning: A Survey,” 2nd International Conference on Data, Engineering and Applications, pp. 1-6, 2020, doi: 10.1109/IDEA49133.2020.9170675.

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Published

2023-12-27

How to Cite

[1]
N. Jayasekara, B. Kulathunge, H. Premaratne, I. Nilam, S. Rajapaksha, and J. Krishara, “Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management”, J Robot Control (JRC), vol. 5, no. 1, pp. 27–53, Dec. 2023.

Issue

Vol. 5 No. 1 (2024)

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Articles

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