Florida tight ends coach Larry Scott had been offering the same advice for years, trying to push life skills along with the blocking and catching. And at the end of Kyle Pitts’ sophomore year with the Gators, the player sat in Scott’s office listening to a variation of the message many others before him had heard.
Find a schedule. Manage your world. Pitts listened and understood.
… “Today, athletes definitely have different expectations surrounding career longevity,” said Tim Grover, author of “Winning: The Unforgiving Race to Greatness.” “They want to play longer.”
He credits this new wave of mature success to combination of mental training, nutrition and technological advancements that help manage bodily stress and mitigate injury.
“There’s so many resources available out there that allow us to gather this information on performance and incorporate it into working out, rest and nutrition, and massage therapy and muscle activation,” said Grover, who has trained Michael Jordan, Kobe Bryant, Dwyane Wade and Russell Wilson. “We were just kind of playing with all of these pieces in the ’80s and ’90s. Now, every athlete has found their teams who all have their specialities.”
… Head coach Brandon Staley made it clear that safety and ensuring that no one gets hurt are the main priorities, which is why they won’t be doing any competitive drills during OTAs.
Even though the sessions are all walk-throughs, Staley wants to create that same game-like mental intensity without doing anything physical.
On Monday, Staley said the Chargers were able to run 65 plays over two 20-minute 11-on-11 periods, as well as two special teams periods.
We’ve all felt the fog come over us when we mistake someone’s name right after being introduced, fail to remember where we left our car in the parking lot or tell a friend the same story twice. Our memory is rarely as reliable as we’d like.
But at times, it also surprises us. We may somehow remember family stories told to us long ago, the names of our middle school teachers or trivia facts buried deep in back of our brain. Despite the standard glitches, our memory can retain far more than either experts or we expect.
Conclusions about its reliability vary tremendously. Some studies conclude that memory is extremely accurate, whereas others conclude that it is not only faulty but utterly unreliable. Even memory experts can struggle to predict how accurate our recollections are.
Stanford University, Stanford Medicine, News Center from
A smartwatch can signal physiological changes, such as a change in red blood cell count, as well as early signs of dehydration, anemia and illness, according to a new study led by researchers at Stanford Medicine.
The study is among the first to show that smartwatch data correlates with laboratory test results.
Scientists from the lab of Michael Snyder, PhD, professor and chair of genetics, tracked data from smartwatches, blood tests and other tests conducted in a doctor’s office in a small group of study participants. They were curious whether smartwatch readouts, such as heart rate and physical activity, could show physiological changes that are typically revealed through clinical measurements, including blood tests.
Imagine a Star Trek-style body scanner that examines your body in such depth that it can produce a 3D computerised model to track your health. Jeff Kaditz didn’t just imagine it, he built one. He’s the CEO of QBio, a US start-up that wants to facilitate a data-led, personalised approach to medicine. The firm’s scanner measures hundreds of biomarkers in a person’s body and tracks them over time in a so-called digital twin – a sort of databank-cum-avator of your body. Here, he tells us what the physical of the future looks like and how it will revolutionise healthcare.
What is a digital twin?
It’s basically a three-dimensional digital model of something. It isn’t new actually. In manufacturing, having a digital twin of, say, an aeroplane engine lets you tweak the design and see how it affects the model. The human body is different. It’s more that we’re tracking what’s changing in a digital twin across all these different biomarkers of your body, and identifying the progression of disease much earlier. So more of a diagnostic.
Becoming Human: Artificial Intelligence Magazine, ANOLYTICS from
Annotation of images is crucial to help machines or computer vision models identify and interpret the objects correctly. Annotation is done with predetermined labels with the help of expert human annotators. In simple words, annotation of images is all about adding metadata to a dataset, which can help machines to recognize the specific given objects in the image. An annotator tags objects within images and makes them more informative so that the machine learning algorithms can interpret the data, and get trained to solve real-life challenges.
This paper presents an in-depth overview of the Bluetooth 5.1 Direction Finding standard’s potentials, thanks to enhancing the Bluetooth Low Energy (BLE) firmware. This improvement allows producers to create location applications based on the Angle of Departure (AoD) and the Angle of Arrival (AoA). Accordingly, it is conceivable to design proper Indoor Positioning Systems (IPS), for instance, for the traceability of resources, assets, and people. First of all, Radio Frequency (RF) radiogoniometry techniques, helpful in calculating AoA and AoD angles, are introduced in this paper. Subsequently, the topic relating to signal direction estimation is deepened. The Bluetooth Core Specification updates concerning version 5.1, both at the packet architecture and prototyping levels, are also reported. Some suitable platforms and development kits for running the new features are then presented, and some basic applications are illustrated. This paper’s final part allows ascertaining the improvement made by this new definition of BLE and possible future developments, especially concerning applications related to devices, assets, or people’s indoor localization. Some preliminary results gathered in a real evaluation scenario are also presented. [full text]
Dynamical systems theory suggests that studying the complexity of biological signals could lead to a single gait metric that reliably predicts risk of running-related injury (RRI). The purposes of this pilot study were to examine center of mass (COM) acceleration complexity at baseline, prior to RRI, and the change between timepoints between collegiate runners who developed RRI during a competitive season and those who remained uninjured, and to determine if complexity at these timepoints was associated with increased odds of RRI. Twenty-two collegiate runners from the same cross-country team wore a waist-mounted triaxial accelerometer (100 Hz) during easy-intensity runs throughout the competitive season. RRIs requiring medical attention were reported via an online survey. Control entropy was used to estimate the complexity of the resultant COM acceleration recorded during each run. Associations between complexity and RRI were assessed using a frequency-matching strategy where uninjured participants were paired with injured participants using complexity from the most time-proximal run prior to RRI. Seven runners sustained an RRI. No significant differences were observed between injured and uninjured groups for baseline complexity (p = 0.364, d = 0.405), pre-injury complexity (p = 0.258, d = 0.581), or change from baseline to pre-injury (p = 0.101, d = 0.963). There were no statistically significant associations found between complexity and RRI risk. Although no significant associations were found, the median effect from the models indicated that an increase in baseline complexity, pre-injury complexity, and change in complexity from baseline each corresponded to an increased odds of sustaining an RRI [baseline: odds ratio (OR) = 1.560, 95% CI = 0.587–4.143, p = 0.372; pre-injury: OR = 1.926, 95% CI: 0.689–5.382, p = 0.211; change from baseline: OR = 1.119; 95% CI: 0.839–1.491, p = 0.445). Despite non-significance and wide confidence intervals that included both positive and negative associations, the point estimates for >98% of the 10,000 frequency-case–control-matched model fits indicated that matching strategy did not influence the directionality of the association estimates between complexity and RRI risk (i.e., odds ratio >1.0). This pilot study demonstrates initial feasibility that additional research may support COM acceleration complexity as a useful single-metric monitoring system for RRI risk during real-world training. Follow-up work should assess longitudinal associations between gait complexity and running-related injury in larger cohorts. [full text]
People don’t gain or lose weight because they live near a fast-food restaurant or supermarket, according to a new study led by the University of Washington. And, living in a more “walkable”, dense neighborhood likely only has a small impact on weight.
These “built-environment” amenities have been seen in past research as essential contributors to losing weight or tending toward obesity. The idea appears obvious: If you live next to a fast-food restaurant, you’ll eat there more and thus gain weight. Or, if you have a supermarket nearby, you’ll shop there, eat healthier and thus lose weight. Live in a neighborhood that makes walking and biking easier and you’ll get out, exercise more and burn more calories.
The new study based on anonymized medical records from more than 100,000 Kaiser Permanente Washington patients did not find that living near supermarkets or fast-food restaurant had any impact on weight. However, urban density, such as the number of houses in a given neighborhood, which is closely linked to neighborhood “walkability” appears to be the strongest element of the built environment linked to change in body weight over time.
With 2/3 of all injuries to the leg, specifically ankle, knee & thigh. LegHow can we reduce risk of injury? Rightwards arrow Try to include elements of strength, balance, agility + core stability work in training routine.
Overview:
‣ Why would you measure HRV during the night?
‣ Why can’t we use a single data point (for example 5 minutes) collected during the night?
‣ What if we use only data collected during deep sleep?
‣ What about using a specific deep sleep segment? (new stuff here)
