Activity scenarios simulation by discovering knowledge through activities of daily living datasets

Efficiently recognizing Activities of Daily Living (ADLs) requires overcoming challenges in collecting datasets through innovative approaches. Simultaneously, it involves adapting to the demand for interpreting human activities amidst temporal sequences of actions and interactions with objects, considering real-life scenarios and resource constraints. This study investigates the potential of generating synthetic training data for ADLs recognition using the VirtualHome2KG framework. Furthermore, we investigate the transformative potential of simulating activities in virtual spaces, as evidenced by our survey of real-world activity datasets and exploration of synthetic datasets in virtual environments. Our work explicitly simulates activities in the 3D Unity platform, affording seamless transitions between environments and camera perspectives. Furthermore, we meticulously construct scenarios not only for regular daily activities but also for abnormal activities to detect risky situations for independent living, ensuring the incorporation of critical criteria. We incorporate one contemporary method for abnormal activity detection to demonstrate the efficacy of our simulated activity data. Our findings suggest that our activity scenario preparation accomplishes the intended research objective while paving the way for an interesting research avenue.