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We used acoustic telemetry and acceleration sensors to compare population‐specific measures of the metabolic costs of an apex fish predator living in four separate lakes. We chose our study species and populations to provide a strong test of recent theoretical predictions that optimal foraging by an apex fish predator in a typical aquatic environment would be consistent with feeding to satiation rather than continuous feeding. We chose four populations where the primary prey type differed along a body size gradient (from small invertebrates to large planktivorous fish) and along a thermal accessibility gradient (from easily accessible cold‐water pelagic prey to less accessible warm‐water epilimnetic and littoral prey). We expected that these gradients in prey type would evoke distinctly different activity gradients depending on whether predators fed to satiation (e.g., less frequent “rest” detections where primary prey are smaller/less accessible) or fed continuously (e.g., fixed level of “rest” detections under all prey conditions). Our study organism was a fall spawning, cold‐water visual apex predator (lake trout). Therefore, we focused our study on diel (early night, dawn, day, dusk, late night) changes in metabolic costs associated with summer feeding behaviour. The duration (~20 days) and fine temporal scale (~30 min) of our behavioural data provided a uniquely detailed picture of intra‐ and inter‐population differences in activity patterns over a critical period in the annual growing season. In all populations, diel shifts in activity were qualitatively consistent with that expected of a visual predator (e.g., resting state detections were most frequent at night). Between‐lake differences in daytime thermal experience were qualitatively consistent with between‐lake differences in the location of primary prey (e.g., excursions to warm habitats were common in lakes with epilimnetic/littoral fish as primary prey and relatively rare in lakes with pelagic cold‐water invertebrates/fish as primary prey). Daytime activity patterns were more consistent with the feeding pattern expected from feeding to satiation rather than continuous feeding: (a) individuals in all four populations exhibited clearly delineated bouts of resting behaviour and active behaviour; (b) the frequency of resting bouts and the resultant overall cost of daily activity were strongly associated with the size and accessibility of prey—in lakes with smaller and/or less accessible prey, predators rested less frequently, exhibited marginally higher costs when active and had higher overall daytime activity costs. Within each lake, similar changes in activity occurred concurrently with diel changes in prey accessibility/relative density. Using a relatively new technology, this paper empirically supports important theories regarding the foraging activity of a top predator fish living in typical freshwater systems. The feeding pattern of this predator was more consistent with a feeding pattern expected from feeding to satiation rather than the pattern expected from continuous feeding.

Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain’s response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0’s validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s2 for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.

Purpose Wearable sensors are used to measure head impact exposure in sports. The Head Impact Telemetry (HIT) System is a helmet-mounted system that has been commonly utilized to measure head impacts in American football. Advancements in sensor technology have fueled the development of alternative sensor methods such as instrumented mouthguards. The objective of this study was to compare peak magnitude measured from high school football athletes dually instrumented with the HIT System and a mouthpiece-based sensor system. Methods Data was collected at all contact practices and competitions over a single season of spring football. Recorded events were observed and identified on video and paired using event timestamps. Paired events were further stratified by removing mouthpiece events with peak resultant linear acceleration below 10 g and events with contact to the facemask or body of athletes. Results A total of 133 paired events were analyzed in the results. There was a median difference (mouthpiece subtracted from HIT System) in peak resultant linear and rotational acceleration for concurrently measured events of 7.3 g and 189 rad/s 2 . Greater magnitude events resulted in larger kinematic differences between sensors and a Bland Altman analysis found a mean bias of 8.8 g and 104 rad/s 2 , respectively. Conclusion If the mouthpiece-based sensor is considered close to truth, the results of this study are consistent with previous HIT System validation studies indicating low error on average but high scatter across individual events. Future researchers should be mindful of sensor limitations when comparing results collected using varying sensor technologies.

With an estimated 3.8 million sport- and recreation-related concussions occurring annually, targeted prevention and diagnostic methods are needed. Biomechanical analysis of head impacts may provide quantitative information that can inform both prevention and diagnostic strategies. To assess available head-impact devices and their clinical utility. We performed a systematic search of the electronic database PubMed for peer-reviewed publications, using the following phrases: accelerometer and concussion, head impact telemetry, head impacts and concussion and sensor, head impacts and sensor, impact sensor and concussion, linear acceleration and concussion, rotational acceleration and concussion, and xpatch concussion. In addition to the literature review, a Google search for head impact monitor and concussion monitor yielded 15 more devices. Included studies were performed in vivo, used commercially available devices, and focused on sport-related concussion. One author reviewed the title and abstract of each study for inclusion and exclusion criteria and then reviewed each full-text article to confirm inclusion criteria. Controversial articles were reviewed by all authors to reach consensus. In total, 61 peer-reviewed articles involving 4 head-impact devices were included. Participants in boxing, football, ice hockey, soccer, or snow sports ranged in age from 6 to 24 years; 18% (n = 11) of the studies included female athletes. The Head Impact Telemetry System was the most widely used device (n = 53). Fourteen additional commercially available devices were presented. Measurements collected by impact monitors provided real-time data to estimate player exposure but did not have the requisite sensitivity to concussion. Proper interpretation of previously reported head-impact kinematics across age, sport, and position may inform future research and enable staff clinicians working on the sidelines to monitor athletes. However, head-impact-monitoring systems have limited clinical utility due to error rates, designs, and low specificity in predicting concussive injury.

Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.

Wearable devices have been shown to effectively measure the head’s movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40 ms and a post-trigger time of 70 ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200 ms time window recorded by the mouthguard. Therefore, approximately 110 ms is recommended for complete modeling of impacts for football.

Supervised learning of behavioural modes from body‐worn sensor data, especially accelerometers, has become a transformative research tool in behavioural ecology over the past years. Due to the popularity of the methodology and diverging needs of users, there are a number of software packages dedicated to it, ranging from web based graphical user interfaces to R software packages. In pyecoacc, we aim to augment the functionality of the existing software by integrating recent methodological findings and recommendations. pyecoacc is an open‐source Python package for supervised learning of behavioural modes from accelerometer data. It is designed to work with minimum configuration, while remaining flexible enough to accommodate customization, additions and extensions. The pyecoacc software package includes the common accelerometer feature computations that have become standard in the field, and pipelines for traditional as well as deep learning‐based models. Model selection is facilitated via simple comparison tables with the recommended metrics. The correct computation of behavioural time budgets with the confusion matrix correction is also supported. We demonstrate the software using a dataset of body acceleration of a rodent species (Damaraland mole‐rat, Fukomys damarensis).

Concussion is a growing public health issue in the United States, and chronic traumatic encephalopathy (CTE) is the chief long-term concern linked to repeated concussions. Recently, attention has shifted toward subconcussive blows and the role they may play in the development of CTE. We recruited a cohort of high school football players for two seasons of observation. Acceleration sensors were placed in the helmets, and all contact activity was monitored. Pre-season computer-based neuropsychological tests and functional magnetic resonance imaging (fMRI) tests were also obtained in order to assess cognitive and neurophysiological health. In-season follow-up scans were then obtained both from individuals who had sustained a clinically-diagnosed concussion and those who had not. These changes were then related through stepwise regression to history of blows recorded throughout the football season up to the date of the scan. In addition to those subjects who had sustained a concussion, a substantial portion of our cohort who did not sustain concussions showed significant neurophysiological changes. Stepwise regression indicated significant relationships between the number of blows sustained by a subject and the ensuing neurophysiological change. Our findings reinforce the hypothesis that the effects of repetitive blows to the head are cumulative and that repeated exposure to subconcussive blows is connected to pathologically altered neurophysiology.

Concussion is a heterogeneous injury occurring throughout a range of impact magnitudes. Consequently, research focusing on a single or set of variables at the time of injury to understand concussive biomechanics has been thwarted by low injury prediction sensitivity. The current study examined the role of Impact Density in estimating concussive injury risk. Head impact data were collected across seven high school football seasons with the Head Impact Telemetry System (HIT System). Over the study period, 29 concussions were included for data analysis. The linear acceleration of the concussive impact was matched to a Control athlete, along with impacts in the 24 h before. Linear and rotational acceleration for the 19 impacts leading into the final event and the cumulative accelerations over time were evaluated. Analyses indicated no difference in impact counts within the final 24 h, or impact magnitudes for linear and rotational acceleration among the final 20 impacts (p > 0.05). A novel metric, Impact Density, was calculated from the final 20 impacts by summing the acceleration magnitude divided by time from the previous impact. Analyses indicated the Concussed athletes incurred a significantly higher linear (concussed: 255.4g/sec (standard error of the mean [SEM] = 40.1), controls:145.4g/sec (SEM = 23.8), p = 0.016), and rotational (Concussed:10311.3 rad/s/s/s (SEM = 1883.7), Controls: 6083.8 rad/s/s/s (SEM = 1115.9), p = 0.029) Impact Density than the Control athletes. Similar to other investigations, there was no difference in individual linear or rotational impact magnitude in the 20 impacts before and including the injury. The measure of Impact Density, however, revealed differences between the Concussed and Control athletes. These data suggest that the biomechanical threshold for concussion fluctuates downwardly with a greater impact magnitude and number with a return to pre-impact levels with time, suggesting physiological vulnerability to repeated head impacts. The current results highlight that time between impacts, not just impact magnitude, influences risk for concussion.

On-field measurement of head impacts has relied on the Head Impact Telemetry (HIT) System, which uses helmet mounted accelerometers to determine linear and angular head accelerations. HIT is used in youth and collegiate football to assess the frequency and severity of helmet impacts. This paper evaluates the accuracy of HIT for individual head impacts. Most HIT validations used a medium helmet on a Hybrid III head. However, the appropriate helmet is large based on the Hybrid III head circumference (58cm) and manufacturer's fitting instructions. An instrumented skull cap was used to measure the pressure between the head of football players (n=63) and their helmet. The average pressure with a large helmet on the Hybrid III was comparable to the average pressure from helmets used by players. A medium helmet on the Hybrid III produced average pressures greater than the 99th percentile volunteer pressure level. Linear impactor tests were conducted using a large and medium helmet on the Hybrid III. Testing was conducted by two independent laboratories. HIT data were compared to data from the Hybrid III equipped with a 3-2-2-2 accelerometer array. The absolute and root mean square error (RMSE) for HIT were computed for each impact (n=90). Fifty-five percent (n=49) had an absolute error greater than 15% while the RMSE was 59.1% for peak linear acceleration.