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Human activity recognition, as a significant branch of artificial intelligence, requires increasingly generalized and precise methodologies due to growing demands. Therefore, this paper proposes a context-based method for recognising indoor human activities, which interlinks indoor human activities with interactions with objects, making the contextual relationship between humans and objects particularly crucial. In addition, this research has developed a new dynamic graph dataset based on publicly available video datasets and their associated descriptive scripts, instantiating the relationships between humans and objects. A novel architecture for human activity recognition is developed in this research. This architecture utilizes graph neural networks and self-attention mechanisms to learn the significance of the interactions between humans and objects and capture the relationships between video frames on a temporal level. The results demonstrate that the classification accuracy reaches 0.86 and it also performs better than other current advanced algorithms STGAT and STGCN. It is noteworthy that the approach also effectively reduces ambiguity in activity recognition. [ABSTRACT FROM AUTHOR]

Over the past decade, automating ultrasound scanning has been the subject of intense research. However, training a robot to perform automated ultrasound examinations requires a substantial corpus of training data. Traditionally, researchers have sought to obtain such data either through publicly available datasets or by engaging professional sonographers in experiments aimed at dataset generation. The former approach often yields incomplete datasets insufficient for specialized research objectives, while the latter entails logistical challenges, including the necessity for frequent manual experimentation and ready access to medical professionals. Therefore, the acquisition of a comprehensive and suitable dataset remains an essential yet formidable challenge. Here, we propose a novel framework for achieving the automated acquisition of cardiac ultrasound datasets by controlling robot behaviour using a digital twin within a simulated environment. This framework consists of two modules: physical and virtual. Within the virtual simulation module, diverse body models of varying dimensions can be inputted, enabling the planning of robot arm path and ultrasound scanning manoeuvrers. Then the physical robot arm clones the actions of the robot in the simulation environment and updates its current state in the virtual module. The proposed framework was used to collect 43,000 cardiac ultrasound images from 8 patients with different pathologies and 1 healthy individual using a KUKA LBR Med robot and Intelligent Ultrasound Simulator. It is also expected to be feasible for a real-person dataset collection. [ABSTRACT FROM AUTHOR]