Dynamic Soft Tissue Artifacts during Impulsive Loads: Measurement Errors Vary With Wearable Inertial Measurement Unit Sensor Design
Bibliographic record
Abstract
OBJECTIVE: Characterize and model Inertial Measurement Unit (IMU) errors due to transient dynamic soft tissue artifacts excited by impulsive loads, such as foot strikes during running and jumping. METHODS: We instrumented 10 participants (5 female, 5 male) with IMUs on the dominant leg. An ankle IMU measured reference vertical accelerations during impulsive loads and was cross-validated against vertical force measures. Two IMUs on the posterior shank and anterior shank were used to characterize errors caused by dynamic soft tissue artifacts with respect to the reference. Shank sensors' masses were varied to explore their effect on dynamic soft tissue artifacts. RESULTS: Both the posterior IMU and anterior IMU overestimated peak vertical accelerations during the impulsive load (gain of 2.18 ± 0.63 and 1.55 ± 0.35 respectively). The post- impulsive load oscillation duration and natural frequency varied with sensor mass according to an underdamped second-order system, with posterior IMU and anterior IMU durations of 326 ± 75 ms and 151 ± 50 ms respectively and natural frequencies of 9.79 ± 2.68 Hz and 18.22 ± 12.10 Hz respectively. Low-pass filtering reduced overestimation of peak vertical accelerations, but also attenuated the reference measure. CONCLUSION: Our study suggests dynamic soft tissue artifacts result in transient, but substantial measurement errors that may not be appropriately mitigated through low-pass filtering. However, these dynamic soft tissue artifacts can be modeled using an underdamped second-order system and used to estimate material properties of underlying soft tissue. SIGNIFICANCE: We demonstrate that dynamic soft tissue artifacts can be modeled and potentially mitigated to improve accuracy in applications necessitating measurement of impulsive loads such as foot strikes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".