Avoid injury and improve efficiency with tips from a physical therapist. Don't be a snow hero. Breakthroughs, discoveries, and DIY tips sent every weekday. You know, for life's most essential resource, water knows a hundred ways to kill you if you're not careful. When it's not trying to drown you in its pools and coastlines during the summer, it shape-shifts to snow in the winter, piling up emergency room visits for those forced to shovel it.

Objective gait analysis using wearable sensors and AI is critical for managing neurological and orthopedic conditions. However, models are vulnerable to hidden dataset biases, and task-specific sensor optimization remains a challenge. We propose a multi-stream attention-based deep learning framework that functions as both a sensor optimizer and an automated data auditor. Applied to the Voisard et al. (2025) multi-cohort gait dataset on four clinical tasks (PD, OA, CVA screening; PD vs CVA differential), the model's attention mechanism quantitatively discovered a severe dataset confound. For OA and CVA screening, tasks where bilateral assessment is clinically essential, the model assigned more than 70 percent attention to the Right Foot while statistically ignoring the Left Foot (less than 0.1 percent attention, 95 percent CI [0.0-0.1]). This was not a clinical finding but a direct reflection of a severe laterality bias (for example, 15 of 15 right-sided OA) in the public dataset. The primary contribution of this work is methodological, demonstrating that an interpretable framework can automatically audit dataset integrity. As a secondary finding, the model proposes novel, data-driven sensor synergies (for example, Head plus Foot for PD screening) as hypotheses for future optimized protocols.

Flexibility is an essential part of staying fit. Breakthroughs, discoveries, and DIY tips sent every weekday. In your efforts to exercise, chances are you've worked on improving the four components of physical fitness. The problem is there are actually five . Criminally overlooked in the pursuit of big-ticket goals like strength, endurance, lung capacity and body composition is flexibility.

Abstract-- Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends the periodic reward formulation of locomotion to skateboarding for the REEM-C robot. Brax/MJX is used to implement the RL problem to achieve fast training. Initial results in simulation are presented with hardware experiments in progress.

The biggest difference from the games is that "Halo," a show developed by Kyle Killen ("Mind Games," "The Beaver") and Steven Kane ("The Closer," "The Last Ship"), kick-starts immediately with Master Chief navigating an identity crisis. The super soldier's thoughts are bouncing between his role as a duty-bound, RoboCop-like galactic enforcer and becoming a kinder, fuzzier Terminator with a heart of gold. The stimulus for this self-awareness is the show's most contrived plot point, namely Chief's connection with a magical ancient stone, an ever-reliable modern McGuffin. But the consequences of that internal crisis, at least for the first two episodes, also provide the best source of rising tension between Master Chief's dawning sense of righteousness and the borderline fascist government of the United Nations Space Command.

Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend. However, such exercises are hard to follow without expert guidance (which is impossible to scale for personalized feedback to every trainee remotely). Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect descriptions of correcting a "current" pose to look like a "target" pose (in both English and Hindi). The collected descriptions have interesting linguistic properties such as egocentric relations to environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our dataset, we introduce the pose-correctional-captioning task and its reverse target-pose-retrieval task. During the correctional-captioning task, models must generate descriptions of how to move from the current to target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. To verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images.

This work proposes improvements in the electroencephalogram (EEG) recording protocols for motor imagery through the introduction of actual motor movement and/or somatosensory cues. The results obtained demonstrate the advantage of requiring the subjects to perform motor actions following the trials of imagery. By introducing motor actions in the protocol, the subjects are able to perform actual motor planning, rather than just visualizing the motor movement, thus greatly improving the ease with which the motor movements can be imagined. This study also probes the added advantage of administering somatosensory cues in the subject, as opposed to the conventional auditory/visual cues. These changes in the protocol show promise in terms of the aptness of the spatial filters obtained on the data, on application of the well-known common spatial pattern (CSP) algorithms. The regions highlighted by the spatial filters are more localized and consistent across the subjects when the protocol is augmented with somatosensory stimuli. Hence, we suggest that this may prove to be a better EEG acquisition protocol for detecting brain activation in response to intended motor commands in (clinically) paralyzed/locked-in patients.

In my work as a journalist I am lucky enough to meet some brilliant people and learn about exciting advances in technology - along with a few duds. But every now and then I come across something that resonates in a deeply personal way. So it was in October 2018, when I visited a company called Medopad, based high up in London's Millbank Tower. This medical technology firm was working with the Chinese tech giant Tencent on a project to use artificial intelligence to diagnose Parkinson's Disease. This degenerative condition affects something like 10 million people worldwide.

In my work as a journalist I am lucky enough to meet some brilliant people and learn about exciting advances in technology - along with a few duds. But every now and then I come across something that resonates in a deeply personal way. So it was in October 2018, when I visited a company called Medopad, based high up in London's Millbank Tower. This medical technology firm was working with the Chinese tech giant Tencent on a project to use artificial intelligence to diagnose Parkinson's Disease. This degenerative condition affects something like 10 million people worldwide.

Boston Dynamics' Atlas humanoid robot can do a lot of things I can't, including backflips and running through snow without falling on its face. Now add parkour to that list of feats. In a new video, you can see Atlas bounding up a multi-layered platform, shifting its weight from its right foot to its left foot, and back to the right foot as it runs up the steps. Like Atlas' previous athletic achievements, the maneuver is hypnotizing. Atlas continues to bound through the uncanny valley, not to put humans in our place, but to get humanoid robots to a level where they can walk and, sure, sometimes do backflips among us.