This $249 AI console has the best elements of the Kinect. Just be prepared for ongoing subscription fees. It's the year 2026 and the hottest game in my living room is . No, I'm not in the midst of an ill-advised retro mobile gaming kick. Instead, my family and I have been jumping around and slicing flying fruit in our living room using the Nex Playground .

Released in 2010 and bundled with the Xbox 360, the Kinect looked like the future – for a brief moment, at least. A camera that could detect your gestures and replicate them on-screen in a game, the Kinect allowed players to control video games with their bodies. It was initially a sensation, selling 1m units in its first 10 days; it remains the fastest-selling gaming peripheral ever. However, a lack of games, unreliable performance and a motion-control market already monopolised by the Nintendo Wii caused enthusiasm for the Kinect to quickly cool. Microsoft released a new version of the Kinect with the Xbox One in 2013, only for it to become an embarrassing flop; the Kinect line was unceremoniously discontinued in 2017.

Abstract-- Poor sitting posture can lead to various work-related musculoskeletal disorders (WMSDs). Office employees spend approximately 81.8% of their working time seated, and sedentary behavior can result in chronic diseases such as cervical spondylosis and cardiovascular diseases. Our results show that the ensemble learning model based on the soft voting mechanism achieves the highest F1 score of 98.1%. Finally, we deployed the SitPose system based on this ensemble model to encourage better sitting posture and to reduce sedentary habits. Office workers typically remain seated throughout their provided insights into the health implications of prolonged workday due to the nature of their tasks and various other sedentary lifestyles. Consequently, many experience backaches, primarily cohort of 360,047 participants from the UK Biobank, delved due to their poor sitting posture and prolonged sedentary into the relationship between sedentary behavior (exceeding 6 habits. Furthermore, prolonged sitting can aims to mitigate such risks by introducing a novel double the risk of developing diabetes, as well as contribute to sitting posture health detection system that utilizes visual the accumulation of abdominal fat, leading to health problems detection technology to provide interactive reminders. The RoSeFi [5] system between increased durations of sedentary behavior in adopted WiFi channel state information to monitor sedentary the workplace and a decline in self-reported general health status.

Causal decoder-only transformer models used for generative language modelling, such as Generative Pre-trained Transformers (GPT), are trained to predict the next token in a sequence based only on its previous tokens. Despite this simple training objective, they have proved to be powerful AI tools. However, only predicting the next token results in top layer embedding vectors that are highly token-focused. There may be benefits in generating embedding vectors at each token position that better capture the overall meaning of longer sequences of future text. Recent studies matching brain scans with deep language models suggest that humans also predict upcoming words when listening or reading but consider multiple future tokens rather than just one. This research investigates a new pretraining method called Future Token Prediction (FTP). In FTP, a large transformer encoder generates top layer embedding vectors for each token position, which, instead of being passed to a language head, are linearly and expansively projected to a pseudo-sequence, which is cross attended to by a small transformer decoder to predict the next N tokens forward from that position in the sequence. The top layer embedding vectors from FTP models exhibit distinct properties compared to those from standard GPT models, varying smoothly along a text sequence as measured by cosine similarity between adjacent tokens. Text generated by FTP models show improved topic coherence compared to standard GPT-like models trained with the same prediction perplexity for the next single token. The vectors are shown to better represent the topic of text based on the results of text classification examples. On a toy, but complex, coding problem, FTP networks produce significantly better results than GPT networks.

Many neurological conditions, e.g., a stroke, can cause patients to experience upper limb (UL) motor impairments that hinder their daily activities. For such patients, while rehabilitation therapy is key for regaining autonomy and restoring mobility, its long-term nature entails ongoing time commitment and it is often not sufficiently engaging. Virtual reality (VR) can transform rehabilitation therapy into engaging game-like tasks that can be tailored to patient-specific activities, set goals, and provide rehabilitation assessment. Yet, most VR systems lack built-in methods to track progress over time and alter rehabilitation programs accordingly. We propose using arm kinematic modeling and capability maps to allow a VR system to understand a user's physical capability and limitation. Next, we suggest two use cases for the VR system to utilize the user's capability map for tailoring rehabilitation programs. Finally, for one use case, it is shown that the VR system can emphasize and assess the use of specific UL joints.

Robotic-assistive therapy has demonstrated very encouraging results for children with Autism. Accurate estimation of the child's pose is essential both for human-robot interaction and for therapy assessment purposes. Non-intrusive methods are the sole viable option since these children are sensitive to touch. While depth cameras have been used extensively, existing methods face two major limitations: (i) they are usually trained with adult-only data and do not correctly estimate a child's pose, and (ii) they fail in scenarios with a high number of occlusions. Therefore, our goal was to develop a 3D pose estimator for children, by adapting an existing state-of-the-art 3D body modelling method and incorporating a linear regression model to fine-tune one of its inputs, thereby correcting the pose of children's 3D meshes. In controlled settings, our method has an error below $0.3m$, which is considered acceptable for this kind of application and lower than current state-of-the-art methods. In real-world settings, the proposed model performs similarly to a Kinect depth camera and manages to successfully estimate the 3D body poses in a much higher number of frames.

The main goal of this article is to analyze the effect on pose estimation accuracy when using a Kalman filter added to 4-dimensional deformation part model partial solutions. The experiments run with two data sets showing that this method improves pose estimation accuracy compared with state-of-the-art methods and that a Kalman filter helps to increase this accuracy.

Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.

UK broadcaster Sky has unveiled a webcam device called Sky Live designed to add features like watch parties with friends, fitness and gaming features, the company announced. It attaches magnetically to the top of the company's Sky Glass smart TVs via USB-C and HDMI, and supports motion tracking for games and workouts, along with video calls, group chats and more. "Sky Live makes your TV much more than just a TV, by introducing new entertainment experiences for the heart of your home," said Sky global chief product officer Fraser Stirling in a statement. "Get active with motion control games, work out with body tracking technology, video call on the big screen and watch TV with loved ones – even from afar. And [with] our powerful Entertainment OS ecosystem, it will keep getting better with every update." The 12-megapixel webcam looks a bit like a mini Xbox One Kinect, with a rectangular design and lens on the right.

Rest in peace, Microsoft PC peripherals. You've probably heard of the Google Graveyard, the collection of apps, services and products that Google shut down before their time. But like any big company, Microsoft also tried and failed to make certain products work. In light of Microsoft's decision to discontinue PC peripherals like the Microsoft Sculpt Desktop Keyboard, let's look at some of the products that litter the Microsoft hardware graveyard. Microsoft made RAMCards, one of the first solid-state disks, for both the Apple II as well as the IBM PC in the early 1980s. Instead of non-volatile memory like today's SSDs, however, these were simply more like memory expansion cards, adding 16KB of RAM to an Apple II with 48KB already in place.