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

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

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

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

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

Cycling ergometers are routinely used in a laboratory setting to evaluate physiological function and monitor changes in training status. One limitation of many cycle ergometers, in relation to the performance testing, is their inability to replicate the cyclist own specific cycling position thereby bringing the validity of the ergometer used into question. Purpose: The purpose of this study was to compare the aerobic and anaerobic energy demands of two commercially available cycle ergometers in trained cyclists. The first ergometer allowed full adjustment of cycling position and was electromagnetically braked (EB). The second ergometer allowed for saddle height adjustment only and was resistance braked (RB). Methods: Ten trained male cyclists were tested on 2 separate occasions within a 14 day period under the same conditions. Subjects performed a 30 second Wingate maximal sprint test followed 60 minutes later by a continuous maximal incremental step test on either the EB or RB cycle ergometer, in a random order. The Wingate test was performed at 9% of body mass and for 30 seconds with a 5 second speed up period. The incremental test started at 100W and increased in resistance by 50W every 3 minutes until volitional exhaustion. Heart rate, VO2, power output and blood lactate were measured during the maximal incremental test. Results: The results showed a significant difference (p<0.01) for the Wingate test between the RB and EB both in terms of peak power output (POmax) and mean power output (POmean) with subjects generating greater power outputs on the EB. During the maximal incremental test, significant differences (p<0.01) were found between EB and RB for submaximal power output, heart rate, and VO2 at both lactate threshold 1 (1mmol.l-1 rise above baseline, LT1) and onset of blood lactate accumulation (4mmol.l-1 blood lactate reference point, OBLA), as well as peak power output at VO2max (PVO2max). Conclusions: Overall it was shown that significant differences in physiological demands were present between the two ergometers under both anaerobic and aerobic conditions. This is may in part be explained by the different positions that the cyclists adopted on either ergometer. Further research is required to compare the findings of the current study with actual cycling performance

Correlating multimodal physical sensor information with biological analysis in ultra endurance cycling

The sporting domain has traditionally been used as a testing ground for new technologies which subsequently make their way into the public domain. This includes sensors. In this article a range of physical and biological sensors deployed in a 64 hour ultra-endurance non-stop cycling race are described. A novel algorithm to estimate the energy expenditure while cycling and resting during the event are outlined. Initial analysis in this noisy domain of "sensors in the field" are very encouraging and represent a first with respect to cycling

A sensing platform for physiological and contextual feedback to tennis athletes

In this paper we describe our work on creating a multi-modal sensing platform for providing feedback to tennis coaches and players. The platform includes a fixed installation around a tennis court consisting of a video camera network and a localisation system as well as wearable sensing technology deployed to individual athletes. We describe the various components of this platform and explain how we can capture synchronised multi-modal sensor data streams for games or training sessions. We then describe the content-based retrieval system we are building to facilitate the development of novel coaching tools. We provide some examples of the queries that the system can support, where these queries are chosen to be suitably expressive so as to reflect a coach's complex information needs regarding tennis-related performance factors