Yoga is a discipline of physical postures, breathing techniques, and meditative practices rooted in ancient Indian traditions, now embraced worldwide for promoting overall well-being and inner balance. The practices are a large set of items, our term for executable actions like physical poses or breath exercises, to offer for a person's well-being. However, to get benefits of Yoga tailored to a person's unique needs, a person needs to (a) discover their subset from the large and seemingly complex set with inter-dependencies, (b) continue to follow them with interest adjusted to their changing abilities and near-term objectives, and (c) as appropriate, adapt to alternative items based on changing environment and the person's health conditions. In this vision paper, we describe the challenges for the Yoga personalization problem. Next, we sketch a preliminary approach and use the experience to provide an outlook on solving the challenging problem using existing and novel techniques from a multidisciplinary computing perspective. To the best of our knowledge, this is the first paper that comprehensively examines decision support issues around Yoga personalization, from pose sensing to recommendation of corrections for a complete regimen, and illustrates with a case study of Surya Namaskar -- a set of 12 choreographed poses.

Automated pose correction remains a significant challenge in AI-driven fitness systems, despite extensive research in activity recognition. This work presents PosePilot, a novel system that integrates pose recognition with real-time personalized corrective feedback, overcoming the limitations of traditional fitness solutions. Using Yoga, a discipline requiring precise spatio-temporal alignment as a case study, we demonstrate PosePilot's ability to analyze complex physical movements. Designed for deployment on edge devices, PosePilot can be extended to various at-home and outdoor exercises. We employ a Vanilla LSTM, allowing the system to capture temporal dependencies for pose recognition. Additionally, a BiLSTM with multi-head Attention enhances the model's ability to process motion contexts, selectively focusing on key limb angles for accurate error detection while maintaining computational efficiency. As part of this work, we introduce a high-quality video dataset used for evaluating our models. Most importantly, PosePilot provides instant corrective feedback at every stage of a movement, ensuring precise posture adjustments throughout the exercise routine. The proposed approach 1) performs automatic human posture recognition, 2) provides personalized posture correction feedback at each instant which is crucial in Yoga, and 3) offers a lightweight and robust posture correction model feasible for deploying on edge devices in real-world environments.

Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from Human Pose Estimation models for extracting 136 key-points spread all over the body to train a Random Forest classifier which is used for estimation of the Yogasanas. The results are evaluated on an in-house collected extensive yoga video database of 51 subjects recorded from 4 different camera angles. We propose a 3 step scheme for evaluating the generalizability of a Yoga classifier by testing it on 1) unseen frames, 2) unseen subjects, and 3) unseen camera angles. We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important. We empirically analyze over three public datasets, the advantage of transfer learning and the possibilities of target leakage. We further demonstrate that the classification accuracies critically depend on the cross validation method employed and can often be misleading. To promote further research, we have made key-points dataset and code publicly available.

A developer's workflow is full of potential tangents, interruptions, and context switches. But the nature of programming makes accessing and maintaining uninterrupted flow states critical. However, there are many coding tools that can help developers do exactly that, working to protect their space for deep thinking and create more productive workflows. Here are the top 10 coding tools of 2022. Visual Studio Code is an integrated development environment made by Microsoft for Windows, Linux, and macOS.

Imagine a day in the future when you wake up to an alarm buzz of a smart assistant. Well, you may wonder is that not something available already in the present day? That's right, but what if the smart assistant also transforms itself into a'smart fitness trainer' that can interact and guide on a personalised workout routine depending on your wellness goals, prevailing health conditions, and recovery needed based on previous night's sleep pattern? Yes, in future, those smart assistants could become one's true'wellness' companion to help individuals keep up with their habits and maintain healthy lifestyle – be it Mindfulness, Exercise, Diet, and Sleep (MEDS). That day in the future is not too far away.

Vancouver, Canada-based Dooly, a startup developing an AI-powered plugin for customer relationship management (CMR) platforms, today announced that it raised $20 million, a combination of $3.3 million seed and $17 million series A tranches. The company plans to use the capital to scale its platform well into this year, according to CEO Kris Hartvigsen. Salesforce's 2019 State of Sales Report found that, on average, salespeople only spend 34% of their day selling products. Among the biggest culprits of the lost time is the disconnect between enterprises' need for a CRM and the fact that these platforms don't always map to how salespeople work. According to a recent survey, one of the top barriers to CRM adoption is the amount of manual data entry.

Microsoft is introducing the ability to integrate popular workplace apps like Asana into meetings on its Teams collaboration platform. Teams meetings were previously limited to chats and channel communications, but now users can integrate 20 new apps into their meetings. For instance, you can discuss and modify a project built in Asana with others directly within a Teams meeting. The new apps for meetings join the more than 700 already available in the Teams App Store, Microsoft said. The company is also making its Power Platform -- which provides low-code tools to build and deploy apps, chatbots, and workflows -- available right within Teams. The Power Apps app for Teams is a maker studio where users can manage app data and connections, and build and modify app interfaces.

A small working prototype for assisting the yoga practice developed as a part of Build for Digital India, where the current Asana will be predicted..with a decent Accuracy..and the instructions for next one will be given..in text format along with the audio. This app is developed using Flutter and integrated with the Machine Learning model which predicts the Asanas. You can find the working prototype here. Machine Learning model developed by Neha Duggirala.

Today's workforce has changed significantly in the past decade. More and more businesses are hiring remote employees, freelance employees, part-time employees in addition to full-time in-office employees to help with an ever-expanding workload. It is imperative that as the workplace changes, we learn to collaborate on a higher level. Collaboration technology trends are making that possible. It was found that innovative business collaboration techniques can improve your company's productivity by 20-30 percent.

PALO ALTO, Calif., May 6, 2017 /PRNewswire-iReach/ ClickUp, a revolutionary project management platform released today, boasts the best elements from popular project management platforms like Trello, JIRA, and Asana. However, unlike the competition Click Up is also using AI and machine learning to set a higher bar for what project management software can be. While deadlines and time estimates are never right, Clickup pours data into proprietary machine learning algorithms to reduce (and potentially eliminate) human error related to making project estimates. Since estimates of this nature are extremely challenging to predict, even marginal improvements in accuracy can be extraordinarily valuable for companies. Another key aspect of the tool is the way in which it leverages the best features of existing platforms to create a solution unlike anything seen previously.