An Ambulatory Biomechanical Data Collection System for Use in Space: Design and Validation
Bibliographic record
Abstract
INTRODUCTION: Loss in bone mineral density and muscle strength in astronauts following long-duration spaceflight have been well documented, but the altered force and movement environments in microgravity which may contribute to these changes have not been well characterized. This paper describes the instrumentation, software, and data collection procedures developed for the "Foot" experiment that was conducted on the International Space Station (ISS) to provide insight into the biomechanics of daily activity in a microgravity environment. METHODS: The instrumentation used for data collection included the Ambulatory Data Acquisition System (ADAS), ADAS electromyography (EMG) modules, the Joint Excursion System, and the Total Force-Foot Ground interface system, which were all integrated into a specially designed Lower Extremity Monitoring Suit. There were 14 total channels of data that were collected at sampling rates between 8 Hz and 1024 Hz, including 7 channels of EMG, 4 channels of joint angle data, 2 channels of in-shoe ground reaction force, and a marker channel for event recording. Data were typically collected for between 6.5 and 11.8 h of activity during 4 d on Earth and 4-7 d in flight. RESULTS: Exemplar data sets collected preflight on astronauts in 1 g to validate the instrumentation are presented. DISCUSSION: We conclude that the system provides valid and useful biomechanical information on long-term activity. The analysis of data collected on-orbit using the system described here will be presented in a series of future papers characterizing the biomechanics of astronaut activity during complete working days on the Earth and on the ISS.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".