Classifying Mobility Aid Use from LiDAR Data
Bibliographic record
Abstract
With the global population aging, enabling older adults to live safely and independently in their own homes has become a priority. Automatically detecting the usage of prescribed mobility aids could generate safety reminders for users with declining mental acuity. Previous research has successfully employed RGB cameras and computer vision techniques to detect mobility aids. Nevertheless, using RGB cameras to monitor individuals can raise privacy concerns that might deter system adoption. Here, we propose a privacy-preserving and noninvasive monitoring system that leverages LiDAR technology and deep learning to automatically detect and classify the use of mobility aids. A custom dataset was collected using a high-resolution LiDAR sensor in a controlled indoor environment, capturing pointcloud data from participants employing seven distinct mobility aid classes-including crutches, canes, walkers, rollators, and self-propelled wheelchairs-from multiple viewing angles. The Point Transformer v3 (PTv3) semantic segmentation model was adapted and fine-tuned for pointcloud classification tasks. The model trained for 5-class classification achieved an accuracy of 98.3% in a proof-of-concept experiment involving 30 participants. These results demonstrate promising performance in accurately classifying mobility aid usage, suggesting that the proposed approach can form the basis for a system providing timely intervention prompts and alerts in real-world settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".