A Platform for Real-Time Space Health Analytics as a Service Utilizing Space Data Relays
Notice bibliographique
Résumé
The health, wellness and adaptation response of astronauts during spaceflight is a key component for the success of any manned mission. Physiological and psychological responses of astronauts during spaceflight have been monitored from the first manned missions sixty years ago. However, limited communication networks to and within the spacecraft have limited methods to monitor the health, wellness and adaptation response of astronauts in real-time. This has resulted in a paradigm of astronaut monitoring as discontinuous samplings of physiological data that are captured on board the spacecraft and transported to Earth on storage devices for retrospective down sampled analysis. In 2009, as part of prior research, McGregor proposed a big data analytics framework and platform, that enables the capture and processing of physiological data and other clinical data in real-time for new approaches to real-time health monitoring. The platform, was named the Artemis platform after the Greek goddess of childbearing as the first domain it was used was neonatal intensive care. Its efficacy and reliability as a new approach for real-time health monitoring has been demonstrated in the critical care domain and specifically within the domain of neonatal intensive care. McGregor previously proposed the application of Artemis as an approach for autonomous health monitoring within the spacecraft to support missions within and beyond low Earth orbit. This would enable sophisticated realtime health, wellness and adaptation assessment that did not require the transmission of data beyond the spacecraft. Artemis Cloud has been proposed as a cloud-based approach to provide remote health monitoring. Artemis Cloud enables Health Analytics as a Service and has been demonstrated utilizing the Ontario Research and Innovation Optical Network (ORION) in Ontario and Artemis Cloud instances located at the Compute Ontario advanced research computing node within the Centre for Advanced Computing, Queen's University, Ontario providing remote health monitoring of neonatal intensive care patients at two hospitals in Ontario, Canada. There is great potential for this Health Analytics as a Service model enabled through Artemis Cloud to support the assessment of health, wellness and adaptation response of astronauts in space, however, robust and reliable networks are required. The next stage of space exploration will see unmanned missions using robots that will require prognostics and health management. This will be followed by manned lunar orbiters, Moon bases and Mars missions that will all require the support of health, wellness and adaptation assessment of astronauts engaged in those missions. Deep space hybrid radio frequency and optical networks have great potential to address the current gap in space communication networks. This paper presents a framework and infrastructure to enable real-time equipment monitoring for prognostics and health management and astronaut health monitoring through cloud-based Health Analytics as a Service utilizing space data relays. A key benefit of this approach is its ability to monitor their health and wellbeing onboard the spacecraft as well as enabling the equipment and astronaut's physiological data, and other clinical data, to be sent to an Earth based Mission Control Center within more manageable latencies of seconds or minutes. This will provide a more viable alternative to autonomous only approaches for equipment and astronaut monitoring.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,009 |
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 ».