Human Activity Analysis In Smart Environments

“（AI）驱动的虚拟训练伙伴远程虚拟健身训练最近出现了巨大的繁荣。这一趋势非常受欢迎，不仅因为越来越多的人参与体育活动，还因为远程训练的可持续性。然而，与教练和训练伙伴的互动仍然严重依赖于直接的人类干预，限制了健身训练的可负担性和可定制性。随着计算机视觉和自然语言处理新工具的最新发展，例如可靠的无标记姿势跟踪和逼真的语言生成，可以可靠地跟踪受训者的表现并提供逼真的反馈。拟议的项目是关于创建一个人工智能，有能力进行口头和非口头沟通的虚拟培训伙伴有效地与用户进行口头交流，并向用户演示练习。个性化的类人代理的目的是增加培训动机并最终提高培训依从性。虚拟培训伙伴将与用户建立初步的融洽关系，在预培训中设定目标和动机，在培训期间指导用户，并提供培训后的反馈。调查将围绕什么沟通方法和风格（口头和非口头）是最有效的，以最大限度地提高用户的内在和外在的培训动机。人工智能技术的适用性，包括机器学习和计算智能方法的最佳干预时机和方法将进行研究。通过建立成套练习的表现指标，代理商试图估计疲劳，并通过调整休息时间和练习长度来保持受训者的活力。此外，为了实现个性化，我们设想建立基于与用户交互的自主虚拟训练伙伴的自我改进机制。该项目与全球领先的工业合作伙伴”

"(AI)-Driven Virtual Training BuddyRemote virtual fitness training has recently seen a massive boom. This trend is verywelcome not only because more people are engaging in physical activity, but also becauseof the sustainability of remote training.However, the interaction with coaches and training buddies still heavily relies on directhuman intervention limiting the affordability and customisability of the fitness training. Withrecent developments in computer vision and natural language processing new tools such asreliable markerless pose tracking and realistic language generation, the performance of thetrainee can be reliably tracked and realistic feedback can be given.The proposed project is about the creation of an artificially intelligent, autonomousvirtual training buddy that has the ability to communicate verbally and non-verbally with theuser effectively as well as to demonstrate exercises to the user. The aim of the personalisedhuman-like agent is to increase training motivation and ultimately training adherence.Thevirtual training buddy will establish initial rapport with the user, set goals and motivation inpre-training, guide the user during the training session, and provide post-training feedback.The investigation will be centred around what communication method and style(verbally and non-verbally) is the most effective to maximise the users' intrinsic and extrinsictraining motivation. The applicability of artificial intelligence techniques including machinelearning and computational intelligence methods for the optimal intervention timing andmethod will be studied. By establishing performance metrics for the set exercises, the agentwill try to estimate fatigue, and by adjusting rest times and exercise set length to keep thetrainee enged. Additionally, to achieve personalisation, we envisage building in mechanismsfor self-improvement of the autonomous virtual training buddy based on interactions with theuser. This project is in collaboration with the global, world-leading industrial partner"