A Convolutional Neural Network Approach to Classifying Activities Using Knee Instrumented Wearable Sensors
Bibliographic record
Abstract
Wearable sensors permit convenient human activity data collection in diverse environments and collected data can be used to evaluate functional impairment or analyze recovery following surgical interventions such as knee replacement. Automated activity classification can be used for adding context to unscripted sessions for comparing identical tasks across subjects. In this study, twenty participants were instrumented with wearable inertial sensors placed above and below both knees while performing activities of daily living. Collected multivariate time series data were encoded as colour images and three convolutional neural networks were developed to classify activities into eleven classes. Performance was evaluated using twenty iterations of a leave-one-subject-out scheme. A first-stage classification model was able to differentiate static vs. dynamic activities almost perfectly and a second-stage model was able to further classify specific static activities performed with 99% accuracy. A separate second-stage model was developed to classify dynamic activities with 91% accuracy. Cycling and ascending/descending stairs were the most commonly confused activities. The current work has demonstrated that both static and dynamic activities of daily living can be classified using only leg instrumentation which is beneficial for applications studying knee performance in varying environments.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".