Sources of error during inertial sensing of human movement: a critical review of the fundamentals
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
Inertial assessments of human movement have potential to support diagnosis and treatment of neuromuscular disorders in healthcare settings. Despite the potential advantages, uptake and acceptance by healthcare professionals are still a challenge, as inertial measurement units are prone to measurement errors due to inherent limitations with the technology. As such, full exploitation is limited to a small group of highly qualified personnel. For usage to be more ubiquitous, standard practices for acquiring high-quality data are required and should include methods for error avoidance, detection, identification, quantification, and mitigation. In this paper, a critical review of sources of error was conducted, from which a taxonomic error classification framework was developed. From this review, it has become apparent which sources of error carry the highest risk for impacting data quality. Methods for error mitigation have been identified, along with limitations and areas for improvement. This framework is intended to serve as a useful reference for both proficient and non-proficient users to ensure all sources of error are considered when developing and interpreting IMU-based assessments. It also provides a foundation for developing standard practices to help users efficiently and reliably acquire high-quality data, which is imperative for uptake and acceptance in healthcare settings.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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