Recent Developments in the Artificial Intelligence of Things (AIoT) in Assistive Technology: A Systematic Literature Review (2020–2025)
Main Article Content
Abstract
This systematic literature review explores the application of Artificial Intelligence of Things (AIoT) in Assistive Technology designed to support individuals with disabilities. Out of an initial 267 articles, 38 studies were selected based on inclusion criteria and quality assessment. The review identifies the dominant machine learning models used in AIoT-based assistive technology solutions. Most research focuses on visual impairments, revealing a significant gap in addressing cognitive, psychological, and degenerative disabilities. Various IoT devices such as wearables, sensors, exoskeletons, and smart wheelchairs are employed to provide adaptive, real-time, and personalized assistance. Key methodological limitations include reliance on simulated data, small sample sizes, and lack of field validation. Technical challenges such as device interoperability and accessibility also hinder implementation. These findings highlight the need for more inclusive research involving direct participation of end-users to develop effective, accessible, and scalable AIoT-based assistive technologies that enhance the quality of life for people with disabilities.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to journal Tech-E, Universitas Buddhi Dharma as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from journal Tech-E.
journal Tech-E, the Editors and the Advisory Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the journal Tech-E, Universitas Buddhi Dharma are sole and exclusive responsibility of their respective authors and advertisers.
References
WHO, “World Report on Disability,” WHO. [Online]. Available: https://apps.who.int/iris/handle/10665/44575
M. P. de Freitas, V. A. Piai, R. H. Farias, A. M. R. Fernandes, A. G. de Moraes Rossetto, and V. R. Q. Leithardt, “Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review,” Sensors, vol. 22, no. 21, pp. 1–20, 2022, doi: 10.3390/s22218531.
P. King and E. Guevara Martinez, “Robotic Assistive Technologies: Principles and Practice,” IEEE Pulse, vol. 11, no. 1, pp. 27–28, 2020, doi: 10.1109/mpuls.2020.2972726.
J. Zhang and D. Tao, “Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things,” IEEE Internet Things J., vol. 8, no. 10, pp. 7789–7817, 2021, doi: 10.1109/JIOT.2020.3039359.
T. W. Sung, P. W. Tsai, T. Gaber, and C. Y. Lee, “Artificial Intelligence of Things (AIoT) Technologies and Applications,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/9781271.
N. Tyagi, D. Sharma, J. Singh, B. Sharma, and S. Narang, “Assistive Navigation System for Visually Impaired and Blind People: A Review,” in 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV), 2021, pp. 1–5. doi: 10.1109/AIMV53313.2021.9670951.
E. V Polyakov, M. S. Mazhanov, A. Y. Rolich, L. S. Voskov, M. V Kachalova, and S. V Polyakov, “Investigation and development of the intelligent voice assistant for the Internet of Things using machine learning,” in 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT), 2018, pp. 1–5. doi: 10.1109/MWENT.2018.8337236.
M. A. Ahmed, B. B. Zaidan, A. A. Zaidan, M. M. Salih, Z. T. Al-qaysi, and A. H. Alamoodi, “Based on wearable sensory device in 3D-printed humanoid: A new real-time sign language recognition system,” Measurement, vol. 168, p. 108431, 2021, doi: https://doi.org/10.1016/j.measurement.2020.108431.
Neeraj, V. Singhal, J. Mathew, and R. K. Behera, “Detection of alcoholism using EEG signals and a CNN-LSTM-ATTN network,” Comput. Biol. Med., vol. 138, p. 104940, 2021, doi: https://doi.org/10.1016/j.compbiomed.2021.104940.
B. Kitchenham and P. Brereton, “A systematic review of systematic review process research in software engineering,” Inf. Softw. Technol., vol. 55, no. 12, pp. 2049–2075, 2013, doi: https://doi.org/10.1016/j.infsof.2013.07.010.
V. Austin et al., “Assistive Technology Changes Lives: an assessment of AT need and capacity in England,” pp. 1-155, 2023, [Online]. Available: https://cdn.disabilityinnovation.com/uploads/documents/publications/England-CCA-Latest.pdf?v=1686583690
S. J. Hussain Shah, A. A. Albishri, and Y. Lee, “Deep Learning Framework For Internet Of Things For People With Disabilities,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 3609-3614. doi: 10.1109/BigData52589.2021.9671475.
P. Srinivas, M. Arulprakash, M. Vadivel, N. Anusha, G. Rajasekar, and C. Srinivasan, “Support Vector Machines Based Predictive Seizure Care using IoT-Wearable EEG Devices for Proactive Intervention in Epilepsy,” in 2024 2nd International Conference on Computer, Communication and Control (IC4), 2024, pp. 1-5. doi: 10.1109/IC457434.2024.10486581.
A. M. TURING, “I.-COMPUTING MACHINERY AND INTELLIGENCE,” Mind, vol. LIX, no. 236, pp. 433-460, 1950, doi: 10.1093/mind/LIX.236.433.
M. Samad, A. Baig, S. Anshrah, and S. Munir, “AI-based Wearable Vision Assistance System for the Visually Impaired: Integrating Real-Time Object Recognition and Contextual Understanding Using Large Vision-Language Models,” pp. 1-18.
S. Frizzo Stefenon, C. Kasburg, A. Nied, A. C. Rodrigues Klaar, F. C. Silva Ferreira, and N. Waldrigues Branco, “Hybrid deep learning for power generation forecasting in active solar trackers,” IET Gener. Transm. Distrib., vol. 14, no. 23, pp. 5667-5674, 2020, doi: https://doi.org/10.1049/iet-gtd.2020.0814.
S. Chandra and P. Gaur, “Radial Basis Function Neural Network Technique for Efficient Maximum Power Point Tracking in Solar Photo-Voltaic System,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 2354–2363, 2020, doi: 10.1016/j.procs.2020.03.288.
A. KASAPBASI, A. E. A. ELBUSHRA, O. AL-HARDANEE, and A. YILMAZ, “DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals,” Comput. Methods Programs Biomed. Update., vol. 2, no. October 2021, 2022, doi: 10.1016/j.cmpbup.2021.100048.
M. S. Rajan et al., “Diagnosis of fault node in wireless sensor networks using adaptive neuro-fuzzy inference system,” Appl. Nanosci., vol. 13, no. 2, pp. 1007–1015, 2023, doi: 10.1007/s13204-021-01934-0.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
L. Golightly, V. Chang, Q. A. Xu, X. Gao, and B. S. C. Liu, “Adoption of cloud computing as innovation in the organization,” Int. J. Eng. Bus. Manag., vol. 14, pp. 1–17, 2022, doi: 10.1177/18479790221093992.
K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” Inf. Softw. Technol., vol. 64, pp. 1–18, 2015, doi: https://doi.org/10.1016/j.infsof.2015.03.007.
M. Junior, O. Maia, H. Oliveira, E. Souto, and R. Barreto, Assistive Technology through Internet of Things and Edge Computing. 2019. doi: 10.1109/ICCE-Berlin47944.2019.8966148.
W.-J. Chang et al., “A Deep Learning Based Wearable Medicines Recognition System for Visually Impaired People,” in 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2019, pp. 207–208. doi: 10.1109/AICAS.2019.8771559.
D. Kumar, S. Iyer, E. Raja, R. Kumar, and V. P. Kafle, “Enhancing User Experience in Pedestrian Navigation Based on Augmented Reality and Landmark Recognition,” 2022 ITU Kaleidosc. - Ext. Real. - How to Boost Qual. Exp. Interoperability, ITU K 2022 - Proc., no. March, 2022, doi: 10.23919/ITUK56368.2022.10003059.
B. G. Lee, T. W. Chong, and W. Y. Chung, “Sensor fusion of motion-based sign language interpretation with deep learning,” Sensors (Switzerland), vol. 20, no. 21, pp. 1–17, 2020, doi: 10.3390/s20216256.
M. Ghazal, T. Basmaji, M. Qasymeh, R. Salim, and A. Khalil, “Localized Assistive Scene Understanding using Deep Learning and the IoT,” in 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2019, pp. 53–58. doi: 10.1109/FiCloudW.2019.00023.
W.-J. Chang, L.-B. Chen, C.-H. Hsu, J.-H. Chen, T.-C. Yang, and C.-P. Lin, “MedGlasses: A Wearable Smart-Glasses-Based Drug Pill Recognition System Using Deep Learning for Visually Impaired Chronic Patients,” IEEE Access, vol. 8, pp. 17013–17024, 2020, doi: 10.1109/ACCESS.2020.2967400.
M. Sreeraj, J. Joy, A. Kuriakose, M. B. Bhameesh, A. K. Babu, and M. Kunjumon, “VIZIYON: Assistive handheld device for visually challenged,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 2486–2492, 2020, doi: 10.1016/j.procs.2020.04.269.
A. Hengle, A. Kulkarni, N. Bavadekar, N. Kulkarni, and R. Udyawar, “Smart Cap: A Deep Learning and IoT Based Assistant for the Visually Impaired,” 2020, pp. 1109–1116. doi: 10.1109/ICSSIT48917.2020.9214140.
S. Rao and V. M. Singh, “Computer Vision and Iot Based Smart System for Visually Impaired People,” in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021, pp. 552–556. doi: 10.1109/Confluence51648.2021.9377120.
W.-J. Chang, L.-B. Chen, C.-Y. Sie, and C.-H. Yang, “An Artificial Intelligence Edge Computing-Based Assistive System for Visually Impaired Pedestrian Safety at Zebra Crossings,” IEEE Trans. Consum. Electron., vol. 67, pp. 3–11, 2021, doi: 10.1109/TCE.2020.3037065.
Y. Akbari, H. Hassen, N. Subramanian, J. Kunhoth, S. Al-ma’adeed, and W. Alhajyaseen, “A vision-based zebra crossing detection method for people with visual impairments,” 2020, pp. 118–123. doi: 10.1109/ICIoT48696.2020.9089622.
A. Karkar, J. Kunhoth, and S. Al-ma’adeed, “A Scene-to-Speech Mobile based Application: Multiple Trained Models Approach,” 2020, pp. 490–497. doi: 10.1109/ICIoT48696.2020.9089557.
D. Bal, A. Arfi, and S. Dey, “Dynamic Hand Gesture Pattern Recognition Using Probabilistic Neural Network,” 2021, pp. 1–4. doi: 10.1109/IEMTRONICS52119.2021.9422496.
Y. S. Su, C. H. Chou, Y. L. Chu, and Z. Y. Yang, “A Finger-Worn Device for Exploring Chinese Printed Text with Using CNN Algorithm on a Micro IoT Processor,” IEEE Access, vol. 7, pp. 116529–116541, 2019, doi: 10.1109/ACCESS.2019.2936143.
M. Madahana, K. Khoza-Shangase, N. Moroe, D. Mayombo, O. Nyandoro, and J. Ekoru, “A proposed artificial intelligence-based real-time speech-to-text to sign language translator for South African official languages for the COVID-19 era and beyond: In pursuit of solutions for the hearing impaired,” South African J. Commun. Disord., vol. 69, no. 2, 2022, [Online]. Available: https://sajcd.org.za/index.php/sajcd/rt/printerFriendly/915/1814
D. Yadav, S. Mookherji, J. Gomes, and S. Patil, “Intelligent Navigation System for the Visually Impaired - A Deep Learning Approach,” 2020, pp. 652–659. doi: 10.1109/ICCMC48092.2020.ICCMC-000121.
S. Jacob et al., “AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities,” IEEE Access, vol. 9, pp. 80340–80350, 2021, doi: 10.1109/ACCESS.2021.3083093.
X. Zhang, X. Huang, Y. Ding, L. Long, W. Li, and X. Xu, “Advancements in Smart Wearable Mobility Aids for Visual Impairments: A Bibliometric Narrative Review,” Sensors, vol. 24, no. 24, 2024, doi: 10.3390/s24247986.
M.-C. Chen, C. Chu, and C.-C. Ko, The Literacy of Integrating Assistive Technology into Classroom Instruction for Special Education Teachers in Taiwan, vol. 8548. 2014. doi: 10.1007/978-3-319-08599-9_53.
B. Jiang, J. Yang, Z. Lyu, and H. Song, “Wearable Vision Assistance System Based on Binocular Sensors for Visually Impaired Users,” IEEE Internet Things J., vol. PP, p. 1, May 2018, doi: 10.1109/JIOT.2018.2842229.
J. Li et al., “Sign Language Recognition and Translation: A Multi-Modal Approach using Computer Vision and Natural Language Processing,” Int. Conf. Recent Adv. Nat. Lang. Process. RANLP, pp. 658–665, 2023, doi: 10.26615/978-954-452-092-2_071.
I.-C. Huang, D. Sugden, and S. Beveridge, “Assistive devices and cerebral palsy: The use of assistive devices at school by children with cerebral palsy,” Child. Care. Health Dev., vol. 35, pp. 698–708, Apr. 2009, doi: 10.1111/j.1365-2214.2009.00968.x.
S. Wang, M. Mei, Y. Xie, Y. Zhao, and F. Yang, “Proactive Personality as a Predictor of Career Adaptability and Career Growth Potential: A View From Conservation of Resources Theory,” Front. Psychol., vol. 12, no. September, pp. 1–11, 2021, doi: 10.3389/fpsyg.2021.699461.
K. K. T. Punsara, H. H. R. C. Premachandra, A. W. A. D. Chanaka, R. V. Wijayawickrama, A. Nimsiri, and R. De Silva, “IoT based sign language recognition system,” ICAC 2020 - 2nd Int. Conf. Adv. Comput. Proc., no. October 2022, pp. 162–167, 2020, doi: 10.1109/ICAC51239.2020.9357267.
L. Boppana, R. Ahamed, H. Rane, and R. K. Kodali, “Assistive Sign Language Converter for Deaf and Dumb,” in 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2019, pp. 302–307. doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00071.
M. A. K. Al Shabibi and S. M. Kesavan, “IoT Based Smart Wheelchair for Disabled People,” in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 2021, pp. 1–6. doi: 10.1109/ICSCAN53069.2021.9526427.
A. R. Javed, M. U. Sarwar, S. ur Rehman, H. U. Khan, Y. D. Al-Otaibi, and W. S. Alnumay, “PP-SPA: Privacy Preserved Smartphone-Based Personal Assistant to Improve Routine Life Functioning of Cognitive Impaired Individuals,” Neural Process. Lett., vol. 55, no. 1, pp. 35–52, 2023, doi: 10.1007/s11063-020-10414-5.
K.-J. Wang, H.-W. Tung, Z. Huang, P. Thakur, Z.-H. Mao, and M.-X. You, “EXGbuds: Universal Wearable Assistive Device for Disabled People to Interact with the Environment Seamlessly,” in Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, in HRI ’18. New York, NY, USA: Association for Computing Machinery, 2018, pp. 369–370. doi: 10.1145/3173386.3177836.
A. Kandoth, N. R. Arya, P. R. Mohan, T. V Priya, and M. Geetha, “Dhrishti: A Visual Aiding System for Outdoor Environment,” in 2020 5th International Conference on Communication and Electronics Systems (ICCES), 2020, pp. 305–310. doi: 10.1109/ICCES48766.2020.9137967.
C. J. Baby, A. Mazumdar, H. Sood, Y. Gupta, A. Panda, and R. Poonkuzhali, “Parkinson’s Disease Assist Device Using Machine Learning and Internet of Things,” in 2018 International Conference on Communication and Signal Processing (ICCSP), 2018, pp. 922–927. doi: 10.1109/ICCSP.2018.8523831.
Abstract views: 779
/
PDF downloads: 562