Gait Phase Detection in Walking and Stairs Using Machine Learning
Bibliographic record
Abstract
Machine learning-based activity and gait phase recognition algorithms are used in powered motion assistive devices to inform control of motorized components. The objective of this study was to develop a supervised multiclass classifier to simultaneously detect activity and gait phase (stance, swing) in real-world walking, stair ascent, and stair descent using inertial measurement data from the thigh and shank. The intended use of this algorithm was for control of a motion assistive device local to the knee. Using data from 80 participants, two decision trees and five long short-term memory (LSTM) models that each used different feature sets were initially tested and evaluated using a novel performance metric: proportion of perfectly classified strides (PPCS). Based on the PPCS of these initial models, five additional posthoc LSTM models were tested. Separate models were developed to classify (i) both activity and gait phase simultaneously (one model predicting six states), and (ii) activity-specific models (three individual binary classifiers predicting stance/swing phases). The superior activity-specific model had an accuracy of 98.0% and PPCS of 55.7%. The superior six-phase model used filtered inertial measurement data as its features and a median filter on its predictions and had an accuracy of 92.1% and PPCS of 22.9%. Pooling stance and swing phases from all activities and treating this model as a binary classifier, this model had an accuracy of 97.1%, which may be acceptable for real-world lower limb exoskeleton control if only stance and swing gait phases must be detected. Keywords: machine learning, deep learning, inertial measurement unit, activity recognition, gait.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".