A wearable obstacle avoidance device for visually impaired individuals with cross-modal learning

Yun Gao, Dan Wu, Jie Song, Xueyi Zhang, Bangbang Hou, Hengfa Liu, Junqi Liao and Liang Zhou ()
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Yun Gao: Nanjing University of Posts and Telecommunications
Dan Wu: Army Engineering University of PLA
Jie Song: Nanjing University of Posts and Telecommunications
Xueyi Zhang: Nanjing University of Posts and Telecommunications
Bangbang Hou: Nanjing University of Posts and Telecommunications
Hengfa Liu: Nanjing University of Posts and Telecommunications
Junqi Liao: Nanjing University of Posts and Telecommunications
Liang Zhou: Nanjing University of Posts and Telecommunications

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58085-x

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