一种面向视障用户多模态移动辅助的经济型AI集成智能手杖
An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users
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中文总结 AI 辅助
针对传统白手杖无法检测高处危险且现有AI导航系统昂贵的问题，本文提出一种基于Raspberry Pi Zero 2W的88美元离线智能手杖，融合视觉与ToF感知，实现高精度低延迟的障碍物检测与反馈，验证了边缘AI辅助的可行性。
中文摘要 AI 辅助
视觉障碍影响全球超过22亿人，然而传统白手杖无法检测高处的危险或提供语义环境信息。现有的AI辅助导航系统通常依赖昂贵的硬件或云连接，限制了在资源受限环境中的可及性。本文提出了一种经济实惠（88美元）、完全离线的AI集成智能手杖，设计用于在超低功耗的Raspberry Pi Zero 2W上提供多模态移动辅助。该系统融合了RGB视觉感知与飞行时间（ToF）距离估计，将INT8量化的SSD MobileNet V1模型与距离感知的振动触觉反馈和实时音频警报相结合。为确保在受限硬件上的运行稳健性，多进程架构将传感器采集、神经推理和触觉反馈隔离为独立进程，并支持故障安全感知。在室内移动场景中的实验评估表明，宏平均F1分数为0.82（精确率：0.85，召回率：0.81），平均端到端延迟为330毫秒，峰值功耗为2.8瓦。一项包含12名参与者的初步可用性研究（SUS：78.5，NASA-TLX）显示了积极的用户感知和增强的障碍物意识。所提出的原型验证了在成本敏感的移动辅助中部署隐私保护、边缘原生辅助智能的可行性。
英文摘要
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.
发表机构
- Islamic University of Madinah（麦地那伊斯兰大学）
- King Fahd University of Petroleum and Minerals（法赫德国王石油矿产大学）
机构由 AI 辅助整理，请以论文原文为准。
