As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Emerging opportunities to measure individual and population-level health data with activity monitors during recreational running activities may set the stage for new research possibilities in mass participation running events and marathon medicine. This study explores the applicability of consumer activity monitor data in a preliminary study for future marathon health research with a cohort of 12 (n = 12) participants completing a 3.379 km walking or running course. This study explored the feasibility of collecting pace and distance data from Fitbit brand consumer activity monitors, from access to user data to reporting of data characteristics and data analysis. We show that a large percentage of participant data can be successfully retrieved from Fitbit consumer activity monitor devices for analysis in marathon health research, and that identifying variations in pace across participants is a practical possibility. We note a mean absolute percentage error of 13% over the true distance of 3.379 km, a higher error than that reported by other studies. We also observe a Pearson correlation coefficient between participant variation in pace and absolute distance error of 0.61. This study provides preliminary evidence to support the applicability of consumer activity monitor data in marathon health research.

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Introduction:Large-scale mass-sporting events are increasingly requiring greater prehospital event planning and preparation to address inherent event-associated medical conditions in addition to incidents that may be unexpected. The Bank of America Chicago Marathon (Chicago, Illinois USA) is one of the largest marathons in the world, and with the improvement of technology, the use of historical patient and event data, in conjunction with environmental conditions, can provide organizers and public safety officials a way to plan based on injury patterns and patient demands for care by predicting the placement and timing of needed medical support and resources.Problem:During large-scale events, disaster planning and preparedness between event organizers, Emergency Medical Services (EMS), and local, state, and federal agencies is critical to ensure participant and public safety.Methods:This study looked at the Bank of America Chicago Marathon, a significant endurance event, and took a unique approach of reviewing digital runner data retrospectively over a five-year period to establish patterns of medical demand geographically, temporally, and by the presenting diagnoses. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, coordination, and communication to ensure a safe and secure event.Conclusions:The Chicago Marathon is one of the largest marathons in the world, and this study identified an equal number of participants requiring care on-course and at the finish line. Most medical complaints were musculoskeletal in nature; however, there were life-threatening conditions such as hyperthermia and cardiac incidents that highlight the need for detailed planning, multi-disciplined coordination, and communication to ensure a safe and secure event. As technology has evolved, the use, analysis, and implementation of historical digital data with various environmental conditions can provide organizers and public safety officials a map to plan injury patterns and patient demands by predicting the placement and timing of needed medical support, personnel, and resources.

problems, and chest pain. In 9,686 (62.1 %) calls, patients were transported to a hospital. Conclusion: This study showed that frequent users constitute a significant proportion of all EMS calls; they may explain the substantial increase seen in EMS call volumes in the last few years. Individually tailored interventions to users with an annual call volume of ≥10 should be considered. Results: During 2012 to 2015, in total 4,963 people encountered pre-hospital care at the marathon. Of these, 175 people (3.53%) were transferred to a hospital for further care (Figure 1). One-hundred-thirty (2.62%) people were treated in the on-site ICU facilities, suggesting that the triage system imperatively connects patients to appropriate care without overburdening particular personnel and resources. Conclusion: The majority of individuals seeking care were successfully triaged and treated by the personnel and resources on site, addressing the needs of an average of over 1,200 individuals per event. The small fraction transferred to hospitals minimized the public health burden, while identifying and mobilizing those who required transfer. This event can be likened to mass events both planned and unexpected, and it demonstrates strategic pre-hospital medical preparedness.