AI-Powered Smart Glasses for Sensing and Recognition of Human-Robot Walking Environments
Bibliographic record
Abstract
Environment sensing and recognition can allow hu-mans and/or robotic systems to dynamically adapt to different walking terrains. However., fast yet accurate visual perception is challenging., especially on embedded systems with limited computational resources. The purpose of this study was to develop and prototype a new pair of integrated AI-powered smart glasses for onboard sensing and recognition of human-robot walking en-vironments with high accuracy and low latency. Our system in-cludes a Raspberry Pi Pico micro controller and an ArduCam low-power camera., both of which interface with commercial eye-glass frames via 3D-printed mounts that we custom-designed. We trained and optimized a lightweight and efficient convolutional neural network using a MobileN etVI backbone to classify real-world walking terrains as either indoor surfaces., outdoor surfaces (grass and dirt)., or outdoor surfaces (paved) using over 62,500 egocentric images that we adapted and manually labelled from the Meta Eg04D dataset. We compiled and deployed our deep learning model using TensorFlow Lite micro and post-training quantization to create a minimized byte array model of size 0.31MB. Our system was able to accurately classify complex walking environments with 93.6% accuracy and an embedded inference speed of 1.5 seconds during online experiments. These AI-powered smart glasses open new opportunities for visual per-ception of human-robot walking environments where real-time embedded computing is desired. Future research will focus on improving the onboard inference speed and further miniaturization of the mechatronic components.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".