Gait phase detection from thigh kinematics using machine learning techniques
Bibliographic record
Abstract
Intelligent orthotic devices require accurate detection of gait events for real-time control. For orthoses that control the knee, an ideal system would only locate sensors at the thigh and knee, thereby facilitating sensor and electronics integration with the assistive device. To determine potential gait phase identification approaches, classification was implemented using J-48 Decision Tree, Random Forest, Multi-layer Perceptrons, and Support Vector Machine classifiers, along with 5-fold (5-FCV) and 10-fold cross validation (10-FCV). Knee angle, thigh angular velocity, and thigh acceleration were obtained from 31 able-bodied participants during walking (10 strides each). Strides were segmented into Loading Response, Push-Off, Swing, and Terminal Swing and features were extracted using a 0.1 second sliding window. Gait phase classification was performed with and without the knee angle parameter. J-48 Decision Tree with the knee angle parameter was ranked the best classifier due to its second highest classification accuracy of 97.5% and lowest mean absolute error of 0.014. Results without the knee angle parameter differed by only 0.5% and 0.003. Therefore, an inertial sensor with accelerometer and gyroscope output, located at the thigh, is a viable approach for classifying gait phases for intelligent orthosis control.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".