Sleep and activity measurement in search and rescue aircraft crews using novel sensing technologies.

Helicopter search and rescue crews (SARC) remain on 24 hour alert. This requires the SARC to remain in a state of readiness and maximise sleep opportunities. When on duty, depending on their proximity to the SAR base, crew members may either sleep on-base or at home. These factors may lead to possible variations in the level of physical activity (PA), sleep duration (Sdur) and sleep efficiency (Sef). Purpose: To investigate the levels of PA, Sdur, and Sef of members of the SARC during a 24 hour on-call shift using several novel sensing technologies. Method: Ten members of the Dublin SARC (mean ± SD: age 40 ± 5 years; height 1.76 ± 0.06m; mass 89.2 ± 14 kg; 5 on-base, 5 off-base) were instrumented with 2 tri-axial accelerometers (XL) and a Sensewear armband (SW) with an internal accelerometer (SWXL). The XL were placed on the right ankle and right hip with the SW placed on the left triceps. Data was recorded for a 26 hour period during which the subjects kept a written record of their activity. Total estimated energy expenditure (tEEE), Seff and Sdur were calculated for each sensor during the 24 hour period. Sleep periods were verified for each subject using a written activity log. Results: Group: Based on the placement location of the sensors (ankle; waist; triceps) significant differences were observed for tEEE (1093.9kcal ± 329.8kcal; 502kcal ± 211.5 kcal; 2371.1kcal ± 838.2kcal , p<0.01). Sleep indices calculated from the SW were seen to be significantly different to the XL data, but not between the XL units themselves (triceps vs. ankle; waist): Sef (72.8% ± 18.5% vs. 96.3% ± 2.6%; 97.3% ± 1.9%, p<0.01) and Sdur ( 257.9mins ± 80.1mins vs. 371.3mins ± 49.0mins; 379.6mins ± 53.9mins, p<0.01). Home vs Base: Significant differences were seen for tEEE for the SW (1907.0kcal ± 397.3kcal vs. 2835.2kcal ± 940.4kcal, p<0.01) and SWXL (193.8kcal ± 63.2kcal vs. 893.2kcal ± 564.2kcal, p<0.01). Similarly a significant difference was observed for Seff (231.4mins ± 82.1mins; vs. 284.4mins ± 77mins, p<0.01) on the SW. Conclusion: The location of the sensor utilised to measure PA and sleep indices in SARC members appears to play a vital role in determining the accuracy of measurement. The SW recorded significant differences in PA levels and Sdur between SARC on-base and off-base. Further research is required to determine if this holds true for a larger sample size

Assessment of Physical Activity in Search and Rescue Operations Using Accelerometer Based Technologies.

Helicopter search and rescue crews (SARC) operate on a 24 hour shift with crew members either sleeping on-base or at home depending on their proximity to the base. This may lead to possible variations in the level of physical activity (PA) that occurs between members of the crew. Aim: To investigate the levels of PA of members of the SARC during a 24 hour on-call shift using several novel sensing technologies. Method: Ten members of the Dublin SARC were instrumented with 2 tri-axial accelerometers (GT3X+), a Sensewear armband (SW) with an internal accelerometer (SWXL) and a Sensecam with an internal accelerometer. Data was recorded for a 26 hour period during which the subjects kept a written record of their activity. Sensors were kept on during all operations, the Sensecam was the only sensor removed while sleeping. Results: Within Group: Significant differences were observed for PA (p<0.01) due to the location of the sensors on the subject. Between Home and Base: Significant differences were seen for PA on the SW and SWXL (p<0.01). Conclusion: The location and type of sensor utilised in SARC operations appears to play a role in measurement of PA. The SW recorded significant differences in PA between SARC on-base and off-base, however the GT3X+ and SWXL were no different. Further research is required to align data from the Sensecam with the sensors used in this study to determine if it is possible to measure PA in this population with the Sensecam accelerometer data while also adding visual contextual data

A comparison of the aerobic energy demands of two commercially available cycle ergometers in trained cyclists

The purpose of this study was to compare the energy demands of two cycling ergometers, (Velotron Dynafit Pro and Monark 834E) commonly used in the physiological monitoring of elite athletes. Eight trained male cyclists with a minimum 2 years training and racing experience participated in the study. Each subject completed an exercise trial involving a maximal incremental test. Testing was performed in a random order on either the Velotron or Monark cycle ergometer at the same time of day with no more than 14 days between each testing session. Subjects were requested to maintain their normal training and nutritional practices during the course of the study but to refrain from any intensive training 48 hours prior to each testing session. During the incremental testing significant differences for power output (PO), heart rate (HR), and oxygen uptake (VO2) were found at both at fixed blood lactate (BL) reference points of; 2.5mmol l-1 (REF2.5mM) and at 4mmol.l-1 (REF4mM). Overall the Velotron appeared to provide a more specific measure of cycling performance with significantly lower energy demands at fixed submaximal exercise intensities being observed as well as a significantly greater peak power output and time to exhaustion being attained, which may reflect the specific cycling position adopted. Further research is required to compare the findings of this study with actual cycling performance

Expanding sensor networks to automate knowledge acquisition

The availability of accurate, low-cost sensors to scientists has resulted in widespread deployment in a variety of sporting and health environments. The sensor data output is often in a raw, proprietary or unstructured format. As a result, it is often difficult to query multiple sensors for complex properties or actions. In our research, we deploy a heterogeneous sensor network to detect the various biological and physiological properties in athletes during training activities. The goal for exercise physiologists is to quickly identify key intervals in exercise such as moments of stress or fatigue. This is not currently possible because of low level sensors and a lack of query language support. Thus, our motivation is to expand the sensor network with a contextual layer that enriches raw sensor data, so that it can be exploited by a high level query language. To achieve this, the domain expert specifies events in a tradiational event-condition-action format to deliver the required contextual enrichment

A simple control law for reducing the effective characteristic acceleration of a solar sail

The direction and magnitude of a solar sail acceleration are strongly related. For this reason, once the characteristic acceleration has been fixed, it is not possible to modulate the acceleration in a particular direction. In this work, a semi-analytical switching control law is derived, enabling a solar sail to emulate a smaller effective characteristic acceleration (without changes in geometry or optical properties); by periodically changing the pitch (cone) angle of the sail, in average over time, the acceleration produced by the sail matches exactly (in both direction and magnitude) that of a “smaller” sail. The range in which this is possible is determined, and the limitations on this range due to the size difference is computed. The method is validated on optimal Earth-Mars trajectories

The banks that said no: banking relationships, credit supply and productivity in the UK

This paper uses a large firm-level dataset of UK companies and information on their pre-crisis lending relationships to identify the causal links from changes in credit supply to the real economy following the 2008 financial crisis. Controlling for demand in the product market, we find that the contraction in credit supply reduced labour productivity, wages and the capital intensity of production at the firm level. Firms experiencing adverse credit shocks were also more likely to fail, other things equal. We find that these effects are robust, statistically significant and economically large, but only when instruments based on pre-crisis banking relationships are used. We show that banking relationships were conditionally randomly assigned and were strong predictors of credit supply, such that any bias in our estimates is likely to be small