Toward enhanced free-living fall risk assessment: Data mining and deep learning for environment and terrain classification
Bibliographic record
Abstract
Fall risk assessment can be informed by understanding mobility/gait. Contemporary mobility analysis is being progressed by wearable inertial measurement units (IMU). Typically, IMUs gather temporal mobility-based outcomes (e.g., step time) from labs/clinics or beyond, capturing data for habitually informed fall risk. However, a thorough understanding of free-living IMU-based mobility is currently limited due to a lack of context. For example, although IMU-based length variability can be measured, no absolute clarity exists for factors relating to those variations, which could be due to an intrinsic or an extrinsic environmental factor. For a thorough understanding of habitual-based fall risk assessment through IMU-based mobility outcomes, use of wearable video cameras is suggested. However, investigating video data is laborious i.e., watching and manually labelling environments. Additionally, it raises ethical issues such as privacy. Accordingly, automated artificial intelligence (AI) approaches, that draw upon heterogenous datasets to accurately classify environments, are needed. Here, a novel dataset was created through mining online video and a deep learning-based tool was created via chained convolutional neural networks enabling automated environment (indoor or outdoor) and terrain (e.g., carpet, grass) classification. The dataset contained 146,624 video-based images (environment: 79,251, floor visible: 28,347, terrain: 39,026). Upon training each classifier, the system achieved F1-scores of ≥0.84 when tested on a manually labelled unseen validation dataset (environment: 0.98, floor visible indoor: 0.86, floor visible outdoor: 0.96, terrain indoor: 0.84, terrain outdoor: 0.95). Testing on new data resulted in accuracies from 51 to 100% for isolated networks and 45–90% for complete model. This work is ongoing with the underlying AI being refined for improved classification accuracies to aid automated contextual analysis of mobility/gait and subsequent fall risk. Ongoing work involves primary data capture from within participants free-living environments to bolster dataset heterogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".