Tracking astronaut physical activity and cardiorespiratory responses with the Bio‐Monitor sensor shirt
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».