Internet of things in assistive technology for people with visual impairment: A scoping review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable (scoping review; no pooled effect size calculated). Device accuracy ranges reported: 87.8% for navigation tasks to 93% for voice control and 100% for object identification in individual studies

• Primary Outcome: Mapping of IoT-based assistive technology types for visual impairment, with smart canes/sticks comprising the largest category (38% of included studies)

• Nnt Or Sensitivity: Sensitivity/specificity not formally reported. Object identification accuracy up to 100% and navigation accuracy as low as 87.8% are point estimates from individual device evaluations without statistical precision measures. Clinical sensitivity and specificity in real-world conditions are unknown.

• Confidence Interval: Not reported for any metric

Clinical Application

Feasibility in routine clinical practice remains limited. Most reviewed devices are prototype or proof-of-concept systems built on Raspberry Pi and Arduino platforms — not commercially available, TGA-approved, or clinically validated products. Barriers include hardware durability, battery life, environmental adaptability (lighting, weather), dataset limitations for diverse populations, and absence of formal usability testing with end-users. Cost-effectiveness data are absent. In Australia, assistive technology for people with visual impairment is primarily funded through the National Disability Insurance Scheme (NDIS) and Vision Australia programs. The TGA regulates medical devices, and IoT-based AT devices would require appropriate classification and registration before clinical deployment. The Royal Australian and New Zealand College of Ophthalmologists (RANZCO) and Optometry Australia have not issued specific guidelines on IoT-AT prescription. Guide Dogs Australia and Vision Australia provide low vision rehabilitation services where such technologies could theoretically be integrated. The NDIS Assistive Technology framework (Tier 3 complex AT) would apply to sophisticated IoT navigation devices, requiring occupational therapist or orthoptist assessment. No PBS listing is relevant. The review's findings from an Indian institutional context have limited direct transferability given differences in infrastructure, regulatory environment, and rehabilitation service models. However, the technology landscape mapping is globally relevant for informing future Australian research and procurement decisions. Adults and potentially children with any degree of visual impairment or blindness seeking assistive technology solutions for navigation, object identification, and daily living activities. Applicability is currently limited to technology-literate users in settings with reliable internet connectivity and access to technical support.

Abstract

Assistive technology (AT) can significantly enhance functional abilities among persons with disabilities. Recently, a rapid advance in computer and Internet of Things (IoT)-based AT has been observed with the integration of Artificial Intelligence. The present scoping review explores a range of IoT-based AT for vision impairments, focusing on their functioning, reliability, and identifying the existing gaps and opportunities for future enhancement. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted for the articles published between 2020 and March 2025. After the initial screening of both titles and abstracts of 705 articles, 18 full-text articles were selected for the review. It was found that the majority of the papers (38%) are related to smart canes/sticks. The findings indicate that several studies use Raspberry Pi and Arduino platform to make products cost-effective and flexible options for real-time processing. The accuracy of these devices ranges from 87.8% in navigation to 93% in voice control and 100% in object identification. Further, challenges such as usability testing, hardware limitations, dataset constraints, and environmental adaptability remain a matter of concern which need to be addressed in future innovation. Incorporating IoT technologies into vision-assistive devices has substantially improved accessibility. Future research should focus on enhancing real-time performance, AI-driven decision-making, and user-centric designs.

References

1. 1.Chauhan, P., Senjam, S. S., Rana, S., Singh, R., & Singh, S. (2026). Internet of things in assistive technology for people with visual impairment: A scoping review. Indian Journal of Ophthalmology. Advance online publication. https://doi.org/10.4103/IJO.IJO_1486_25