… As part of their official match report, Juventus described Ronaldo’s leap as “worthy of Michael Jordan” and claimed his feet reached an altitude of 71 centimetres (2 feet, 4 inches) — which is roughly one-third the height of a regulation crossbar.
Sky Italia also reviewed the footage and assessed that Ronaldo’s head reached a maximum height of 2.56 metres (8 feet, 5 inches) as he hung in the air for 1.5 seconds.
… What’s changed? How has Wilson’s game gotten even better? The answer, like everything Wilson, is complicated and fascinating.
It begins with a fundamental belief that as good as Wilson has been, he has always had an extra gear to ascend to. He’s hitting that gear now. Wilson will say it’s just a product of determination. Others say the difference in his approach isn’t just gradual.
… Drenth isn’t worried. “She’s going to run Boston unless something happens that doesn’t allow her to do it physically,” he said. “She’s excited to run Boston. It’s a community that seems to have embraced her, and her them. I don’t see why she can’t go there and be relatively effective. … To manage both marathons, Linden said she’ll have to be “incredibly professional” in her approach to training. “Which I should be anyways,” she said. “But even more diligent during this whole buildup with recovery. Scratch the vacations and celebrations after the Trials. Those are easy things; they’re not sacrifices. I’m passionate enough about Boston, I think it will be really easy to get rolling again.”
We tend to think of burnout as an individual problem, solvable by “learning to say no,” more yoga, better breathing techniques, practicing resilience — the self-help list goes on. But evidence is mounting that applying personal, band-aid solutions to an epic and rapidly evolving workplace phenomenon may be harming, not helping, the battle. With “burnout” now officially recognized by the World Health Organization (WHO), the responsibility for managing it has shifted away from the individual and towards the organization. Leaders take note: It’s now on you to build a burnout strategy.
… Your body will likely let you know when to take a rest day but if you’re one of the people who need to see it to believe it, you may find it useful to monitor your recovery and training to have data to prove you’re feeling right – cold hard numbers are harder to ignore.
Here are some of the physical signs and metrics that you should keep an eye out for to know when to take a rest day.
Whether you’re traveling for work or for fun, nothing ruins the start of a trip quite like jet lag. Engineers affiliated with the Lighting Enabled Systems & Applications (LESA) Center at Rensselaer Polytechnic Institute have developed a way to deliver personalized advice using smart wearable technology that would help travelers adjust more quickly.
In a series of articles, including one published today in PLOS ONE, the researchers explain how they have developed and demonstrated a series of algorithms that can analyze biometric information recorded by a smart device and then recommend the best combination of sleep and light to help a person readjust their circadian rhythm.
“Using these algorithms and a mathematical model of a person’s circadian rhythm, we have the ability to compute the best light to adjust your circadian rhythm and foster your well-being. This opens the opportunity to create a smart and healthy environment,” said Agung Julius.
Stanford University, Stanford Engineering, The Future of Everything podcast from
A biomechanical engineer explains how new diagnostics and improved understanding of human movement are yielding great leaps forward in the treatment of motor dysfunction. [video, 28:21]
Quantifiably monitoring sweat rate and volume is important to assess the stress level of individuals and/or prevent dehydration, but despite intense research, a convenient, continuous, and low-cost method to monitor sweat rate and total sweat volume loss remains an un-met need. We present here an ultra-simple wearable sensor capable of measuring sweat rate and volume accurately. The device continuously monitors sweat rate by wicking the produced sweat into hydrogels that measurably swell in their physical geometry. The device has been designed as a simple to fabricate, low-cost, disposable patch. This patch exhibits stable and predictable operation over the maximum variable chemistry expected for sweat (pH 4-9 and salinity 0-100 mM NaCl). Preliminary in vivo testing of the patch has been achieved during aerobic exercise, and the sweat rates measured via the patch accurately follow actual sweat rates.
arXiv, Computer Science > Computer Vision and Pattern Recognition; Tanmay Randhavane, Uttaran Bhattacharya, Kyra Kapsaskis, Kurt Gray, Aniket Bera, Dinesh Manocha from
We present a data-driven deep neural algorithm for detecting deceptive walking behavior using nonverbal cues like gaits and gestures. We conducted an elaborate user study, where we recorded many participants performing tasks involving deceptive walking. We extract the participants’ walking gaits as series of 3D poses. We annotate various gestures performed by participants during their tasks. Based on the gait and gesture data, we train an LSTM-based deep neural network to obtain deep features. Finally, we use a combination of psychology-based gait, gesture, and deep features to detect deceptive walking with an accuracy of 93.4%. This is an improvement of 16.1% over handcrafted gait and gesture features and an improvement of 5.9% and 10.1% over classifiers based on the state-of-the-art emotion and action classification algorithms, respectively. Additionally, we present a novel dataset, DeceptiveWalk, that contains gaits and gestures with their associated deception labels. To the best of our knowledge, ours is the first algorithm to detect deceptive behavior using non-verbal cues of gait and gesture.
Providing secure communications between wireless devices that encounter each other on an ad-hoc basis is a challenge that has not yet been fully addressed. In these cases, close physical proximity among devices that have never shared a secret key is sometimes used as a basis of trust; devices in close proximity are deemed trustworthy while more distant devices are viewed as potential adversaries. Because radio waves are invisible, however, a user may believe a wireless device is communicating with a nearby device when in fact the user’s device is communicating with a distant adversary. Researchers have previously proposed methods for multi-antenna devices to ascertain physical proximity with other devices, but devices with a single antenna, such as those commonly used in the Internet of Things, cannot take advantage of these techniques.
We present theoretical and practical evaluation of a method called SNAP – SiNgle Antenna Proximity – that allows a single-antenna Wi-Fi device to quickly determine proximity with another Wi-Fi device. [video, 9:15]
Accurate quantification of energy intake is imperative in athletes; however traditional dietary assessment tools are frequently inaccurate. Therefore, this study investigated the validity of a contemporary dietary assessment tool or wearable technology to determine the total energy intake (TEI) of professional young athletes. The TEI of eight professional young male rugby league players was determined by three methods; Snap-N-Send, SenseWear Armbands (SWA) combined with metabolic power and doubly labelled water (DLW; intake-balance method; criterion) across a combined ten-day pre-season and seven-day in-season period. Changes in fasted body mass were recorded, alongside changes in body composition via isotopic dilution and a validated energy density equation. Energy intake was calculated via the intake-balance method. Snap-N-Send non-significantly over-reported pre-season and in-season energy intake by 0.21 (2.37) MJ.day−1 (p = 0.833) and 0.51 (1.73) MJ.day−1 (p = 0.464), respectively. This represented a trivial and small standardised mean bias, and very large and large typical error. SenseWear Armbands and metabolic power significantly under-reported pre-season and in-season TEI by 3.51 (2.42) MJ.day−1 (p = 0.017) and 2.18 (1.85) MJ.day−1 (p = 0.021), respectively. This represents a large and moderate standardised mean bias, and very large and very large typical error. There was a most likely larger daily error reported by SWA and metabolic power than Snap-N-Send across pre-season (3.30 (2.45) MJ.day−1; ES = 1.26 ± 0.68; p = 0.014) and in-season periods (1.67 (2.00) MJ.day−1; ES = 1.27 ± 0.70; p = 0.012). This study demonstrates the enhanced validity of Snap-N-Send for assessing athlete TEI over combined wearable technology, although caution is required when determining the individual TEIs of athletes via Snap-N-Send.
There’s so much to look at on food packages, both front and back. But the most important info you’ll find is in the little black and white Nutrition Facts panel. So how exactly should we use this panel? What should and shouldn’t we look for? While the food label can be an incredibly helpful tool in selecting products that fit your goals, needs, and lifestyle, it can also be challenging to navigate. So let’s give a break down of the things you should keep an eye on and what you can ignore when you flip a food package around.
Following the deadline for submissions, the OptaPro Analytics Forum judges spent November reviewing dozens of presentation proposals for the 2020 event, which will be taking place in central London on 5th February.
Team photo day at Dodger Stadium requires time and space. One afternoon each year, all the players and coaches walk from the clubhouse to left field. They climb a series of portable bleacher seats to assemble a sea of white, outnumbered by rows of non-uniformed staff. A team photographer ascends a tall ladder. Several photos are snapped. The process lasts about 90 minutes from set-up to tear-down.
Wednesday was team photo day at Zelus Analytics in Austin, Texas, where president Doug Fearing and his seven full-time employees were quickly on to bigger and better things. In Culver City, Reboot Motion co-founder and CEO Jimmy Buffi hasn’t even scheduled a photo day yet. His company has barely existed for a month. It consists of two employees: co-founder Evan Demchick and Buffi, whose commute now spans the length of his bedroom.