Human pose recovery using wireless inertial measurement units
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many applications in rehabilitation and sports training require the assessment of the patient's status based on observation of their movement. Small wireless sensors, such as accelerometers and gyroscopes, can be utilized to provide a quantitative measure of the human movement for assessment. In this paper, a kinematics-based approach is developed to estimate human leg posture and velocity from wearable sensors during the performance of typical physiotherapy and training exercises. The proposed approach uses an extended Kalman filter to estimate joint angles from accelerometer and gyroscopic data and is capable of recovering joint angles from arbitrary 3D motion. Additional joint limit constraints are implemented to reduce drift, and an automated approach is developed for estimating and adapting the process noise during online estimation. The approach is validated through a user study consisting of 20 subjects performing knee and hip rehabilitation exercises. When compared to motion capture, the approach achieves an average root-mean-square error of 4.27 cm for unconstrained motion, with an average joint error of 6.5°. The average root-mean-square error is 3.31 cm for sagittal planar motion, with an average joint error of 4.3°.
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.
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 | 0.001 | 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 it