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Tracking astronaut physical activity and cardiorespiratory responses with the Bio‐Monitor sensor shirt

2021· article· en· W3166016148 on OpenAlexafffundabout
Carmelo Mastrandrea, Danielle K. Greaves, Richard L. Hughson

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsResearch Institute for Aging
FundersCanadian Space Agency
KeywordsSpaceflightInternational Space StationWearable computerCardiorespiratory fitnessDeconditioningWeightlessnessAccelerometerTelemetryMedicineAeronauticsSimulationPhysical medicine and rehabilitationComputer sciencePhysical therapyEngineeringEmbedded systemAerospace engineeringTelecommunicationsPhysics

Abstract

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Astronauts develop insulin resistance, and are at risk for cardiovascular deconditioning, during long‐duration missions to the International Space Station (ISS) despite their daily exercise sessions (Hughson et al. Am J Physiol Heart Circ Physiol 310: H628–H638, 2016). Chronic unloading of the musculoskeletal and cardiovascular systems in microgravity dramatically reduces the challenge of daily activities, and the astronauts’ schedules limit them to approximately 30‐min/day aerobic exercise. To understand the physical demands of spaceflight and how these change from daily life on Earth, the Vascular Aging experiment is equipping astronauts for 48‐72h continuous recordings with the Canadian Space Agency's Bio‐Monitor wearable sensor shirt. The Bio‐Monitor (Bio‐M), developed from the commercial Hexoskin® device, consists of 3‐lead ECG, thoracic and abdominal respiratory bands, 3‐axis accelerometer, skin temperature and SpO2 sensor placed on the forehead. Our utilisation of this equipment necessitated the development of novel processing and visualisation techniques, to better interpret and guide subsequent data analyses. Here we present initial data from astronauts wearing the BioM prior to launch and aboard the ISS, demonstrating the ability to extract useful data from BioM, using software developed ‘in‐house’ . Astronauts wore the Bio‐M continually for 72‐h except for periods of water immersion or when the device conflicted with another activity. After physical exercise, astronauts changed to a dry shirt. First, we assessed the key data‐quality metrics to provide initial appraisals of acceptable recordings. Mean total recording length pre‐flight (60.5 hours) was similar to that in‐flight (66.5 hours), with a consistent distribution of recorded day (44% vs 45%, 6am‐6pm) and night (56% vs 55%, 6pm‐6am) hours (pre‐flight vs in‐flight respectively). For each recording, quality assessment of ECG signals was performed for individual leads, before combining signals and cross‐correlating R‐waves to produce reliable heart‐rate timings. Mean ECG quality for individual leads, represented here as the percentage of usable signal to total recording duration, was somewhat lower in‐flight (92%) when compared to pre‐flight (96%), likely caused by poor skin contact or dry shirt electrodes; combining lead signals as mentioned above improved the proportion of usable data to 97% and 98% respectively. Accelerometer recordings identified a significant reduction in high‐force movements over the 72‐hour recordings, with just over 2.5 hours/day of high‐force activity in astronauts pre‐flight vs 50 minutes/day in‐flight. It should be noted however that accelerometer measurements in zero‐gravity are likely to be reduced, and future refinement of activity data continues. Average heart rates in‐flight showed little difference when compared to pre‐flight, although future analyses will compare periods of sleep, rest, and activity to further refine this comparison. We conclude that utilisation of the BioM hardware with our own analysis techniques produces high‐quality data allowing for future interpretation and investigation of spaceflight‐induced physiological adaptations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.022
GPT teacher head0.292
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes3
Has abstractyes

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