A persistent challenge in sign language video processing, including the task of sign language to written language translation, is how we learn representations of sign language in an effective and efficient way that can preserve the important attributes of these languages, while remaining invariant to irrelevant visual differences. Informed by the nature and linguistics of signed languages, our proposed method focuses on just the most relevant parts in a signing video: the face, hands and body posture of the signer. However, instead of using pose estimation coordinates from off-the-shelf pose tracking models, which have inconsistent performance for hands and faces, we propose to learn the complex handshapes and rich facial expressions of sign languages in a self-supervised fashion. Our approach is based on learning from individual frames (rather than video sequences) and is therefore much more efficient than prior work on sign language pre-training. Compared to a recent model that established a new state of the art in sign language translation on the How2Sign dataset, our approach yields similar translation performance, using less than 3\% of the compute.

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffness, affecting the contact angle between the swing leg's end-effector and the climbing surface during the gait cycle. The relationship between desired and actual foot positions is investigated here in a leg-stiffness-enhanced model under external forces, focusing on the challenge of unreliable end-effector attachment on climbing surfaces in such robots. Inspired by the difference in ceiling attachment postures of dead and living geckos, feedforward compensation of the stance phase legs is the key to solving this problem. A feedforward gravity compensation (FGC) strategy, complemented by leg coordination, is proposed to correct gravity-influenced body posture and improve adhesion stability by reducing body inclination. The efficacy of this strategy is validated using a quadrupedal climbing robot, EF-I, as the experimental platform. Experimental validation on an inverted surface (ceiling walking) highlight the benefits of the FGC strategy, demonstrating its role in enhancing stability and ensuring reliable end-effector attachment without external assistance. In the experiment, robots without FGC only completed in 3 out of 10 trials, while robots with FGC achieved a 100\% success rate in the same trials. The speed was substantially greater with FGC, achieved 9.2 mm/s in the trot gait. This underscores the proposed potential of FGC strategy in overcoming the challenges associated with inconsistent end-effector attachment in robots with low leg stiffness, thereby facilitating stable locomotion even at inverted body attitude.

Machine learning and deep learning has shown very good results when applied to various computer vision applications such as detection of plant diseases in agriculture (Kamilaris & Prenafeta-Boldú, 2018), fault diagnosis in industrial engineering (Wen et al., 2018), brain tumor recognition from MR images (Chen et al., 2018a), segmentation of endoscopic images for gastric cancer (Hirasawa et al., 2018), or skin lesion recognition (Li & Shen, 2018) and even autonomous vehicles (Alam et al., 2019). As our daily life increasingly depends on sitting work and the opportunities for physical exercising (in the context of COVID-19 pandemic associated restrictions and lockdowns are diminished), many people are facing various medical conditions directly related to such sedentary lifestyles. One of the frequently mentioned problems is back pain, with bad sitting posture being one of the compounding factors to this problem (Grandjean & Hünting, 1977; Sharma & Majumdar, 2009). Inadequate postures adopted by office workers are one of the most significant risk factors of work-related musculoskeletal disorders. The direct consequence may be back pain, while indirectly it has been associated with cervical disease, myopia, cardiovascular diseases and premature mortality (Cagnie et al., 2006).

The advent of artificial intelligence and its subsets (computer vision, machine learning, NLP, and more) is modernizing the health and fitness industry at an unprecedented rate. By making fitness machines, gadgets, wearables, and mobile applications smarter, this technology is helping people to stay fit and healthy. Right from helping businesses in this industry in improving their marketing and sales strategies to assisting people to reshape their day-to-day habits, AI is playing a big role in the fitness world. And if you are wondering how AI has become a game-changer, then this article is for you. Here, we have listed all the benefits it renders to the fitness world.

Perhaps you've heard of AI conducting interviews. Or maybe you've been interviewed by one yourself. Companies like HireVue claim their software can analyze video interviews to figure out a candidate's "employability score." The algorithms don't just evaluate face and body posture for appearance; they also tell employers whether the interviewee is tenacious, or good at working on a team. These assessments could have a big effect on a candidate's future.

In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects, and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion. We observed chess players engaged in problems of increasing difficulty while recording their behavior. Such recordings can be used to estimate a participant's awareness of the current situation and to predict ability to respond effectively to challenging situations. Results show that a multimodal approach is more accurate than a unimodal one. By combining body posture, visual attention and emotion, the multimodal approach can reach up to 93% of accuracy when determining player's chess expertise while unimodal approach reaches 86%. Finally this experiment validates the use of our equipment as a general and reproducible tool for the study of participants engaged in screen-based interaction and/or problem solving.

Emotional intelligence is a cornerstone of human interactions--an essential part of what it means to be human. But now, artificial intelligences are being developed to better read and process human emotions, which is already changing the way we interact with robots. In the early 1990s, psychologists Salovey and Mayer were the first to recognize emotional intelligence as a set of knowledge and skills distinct from other forms of intelligence, defining it as "the ability to monitor one's own and other's feelings and emotions, to discriminate among them, and to use this information to guide one's thinking and actions." Emotional intelligence is something that seems wonderfully and innately human. But it turns out the tenets of emotional intelligence--which we start picking up in infancy and which seem so closely linked to human nature itself--can be quantified and reduced to logical procedures and algorithms.

Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures that are necessary for playing a language game. We present a specific example of a language strategy for playing an Action Game  in which one robot asks another robot to take on a body posture (such as stand or sit), and show how it effectively allows a population of agents to self-organise a perceptually grounded ontology and a lexicon from scratch, without any human intervention. Next, we show how a new language strategy can arise by exaptation from an existing one, concretely, how the body posture strategy can be exapted to a strategy for playing language games about the spatial position of objects (as in "the bottle stands on the table").