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An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users

Summary

A new arXiv paper presents an $88, fully offline AI-integrated smart cane built on a Raspberry Pi Zero 2W that fuses RGB vision with Time-of-Flight ranging and delivers distance-aware vibrotactile and audio alerts. Indoor tests show a macro-averaged F1 of 0.82, roughly 330 ms end-to-end latency and 2.8 W peak power, with a 12-participant usability study reporting a SUS score of 78.5.

SourcearXiv AIAuthor: Ali Akarma, Adeel Ahmad, Toqeer Ali Syed
An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users
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[Submitted on 12 Sep 2026]

Title:An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users

View a PDF of the paper titled An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users, by Ali Akarma and 1 other authors

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Abstract:Visual impairment affects over 2.2 billion people worldwide, yet conventional white canes cannot detect elevated hazards or provide semantic environmental context. Existing AI-assisted navigation systems typically rely on expensive hardware or cloud connectivity, limiting accessibility in resource-constrained settings. This paper presents an affordable (\$88 USD), fully offline AI-integrated smart cane designed for multimodal mobility assistance on an ultra-low-power Raspberry Pi Zero 2W. The system fuses RGB vision sensing with Time-of-Flight (ToF) distance estimation, pairing an INT8-quantized SSD MobileNet V1 model with distance-aware vibrotactile feedback and real-time audio alerts. To ensure operational robustness on constrained hardware, a multiprocessing architecture isolates sensor acquisition, neural inference, and haptic feedback into independent processes with fail-safe sensing support. Experimental evaluation across indoor mobility scenarios demonstrates a macro-averaged F1-score of 0.82 (precision: 0.85, recall: 0.81), a mean end-to-end latency of 330\,ms, and a peak power draw of 2.8\,W. A preliminary usability study with 12 participants (SUS: 78.5, NASA-TLX) demonstrated positive user perception and enhanced obstacle awareness. The proposed prototype validates the feasibility of deploying privacy-preserving, edge-native assistive intelligence for cost-sensitive mobility assistance.

Subjects:

Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Robotics (cs.RO)

Cite as: arXiv:2609.22277 [cs.AI]

(or arXiv:2609.22277v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.22277

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Ali Akarma [view email] [v1] Sat, 12 Sep 2026 08:45:09 UTC (1,786 KB)

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Key points

  • The device costs about $88 and runs entirely offline, avoiding cloud dependency for resource-constrained settings
  • It fuses RGB vision with Time-of-Flight distance estimation and runs an INT8-quantized SSD MobileNet V1 model on an ultra-low-power Raspberry Pi Zero 2W
  • A multiprocessing architecture isolates sensor acquisition, neural inference and haptic feedback, with fail-safe sensing support
  • Evaluation reports a macro-averaged F1 of 0.82 (precision 0.85, recall 0.81), 330 ms mean latency, 2.8 W peak power and a SUS score of 78.5 from 12 participants

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