ArticleNature communications2025
A wearable obstacle avoidance device for visually impaired individuals with cross-modal learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A review of full-stack autonomous obstacle avoidance for assistive robots for the disabled.Frontiers in robotics and AI · 2026Review
- Wearable technologies for assisted mobility in the real world.Nature communications · 2025Review
- Independent pathfinding with collision avoidance for visually impaired individuals.Scientific reports · 2025Article
- A wearable obstacle avoidance device for visually impaired individuals with cross-modal learning.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
It is challenging for wearable obstacle avoidance devices to simultaneously meet practical demands of high reliability, rapid response, long-lasting duration, and usable design. Here we report a wearable obstacle avoidance device, comprising a set of self-developed glasses (weighing ~400 grams, including an ~80 grams battery) and a common smartphone. Specifically, the glasses collect the multi-modal data for comprehensive environmental perception, including video and depth modalities, and implement a depth-aided video compression module. This module not only adaptively compresses video data to reduce transmission delay to the smartphone, but also operates on a customized FPGA board featuring a multi float-point vector unit streaming processing architecture, thereby facilitating responsive and energy-efficient obstacle detection. Additionally, we design a cross-modal obstacle detection module on the smartphone, which ensures reliable detection and provides user-friendly auditory and tactile alerts by utilizing cross-modal learning based on modal correlations. Multiple indoor and outdoor experimental results demonstrate 100% collision avoidance rates, delay of less than 320 ms, and duration of approximately 11 hours.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.