Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors
Bibliographic record
Abstract
Objective: To develop a deep learning framework to predict lower-limb joint kinematics from inertial measurement unit (IMU) data across multiple gait tasks (walking, jogging, and running) and evaluate the impact of dynamic time warping (DTW) on reducing prediction errors. Patients and Methods: Data were collected from 18 participants fitted with IMUs and an optical motion capture system between May 25, 2023, and May 30, 2023. A long short-term memory autoencoder supervised regression model was developed. The model consisted of multiple long short-term memory and convolution layers. Acceleration and gyroscope data from the IMUs in 3 axes and their magnitude for the proximal and distal sensors of each joint (hip, knee, and ankle) were inputs to the model. Optical motion capture kinematics were considered ground truth and used as an output to train the prediction model. Results: The deep learning models achieved a root-mean-square error of less than 6° for hip, knee, and ankle joint sagittal plane angles, with the ankle showing the lowest error (5.1°). Task-specific models reported enhanced performance during certain gait phases, such as knee flexion during running. The application of DTW significantly reduced root-mean-square error across all tasks by at least 3° to 4°. External validation of independent data confirmed the model's generalizability. Conclusion: Our findings underscore the potential of IMU-based deep learning models for joint kinematic predictions, offering a practical solution for remote and continuous biomechanical assessments in health care and sports science.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".