Functas Usability for Human Activity Recognition using Wearable Sensor Data

Abstract

Recent advancements in data science have introduced implicit neural representations as a powerful approach for learning complex, high-dimensional functions, bypassing the need for explicit equations or manual feature engineering. In this paper, we present our research on employing the weights of these implicit neural representations to characterize and classify batches of data, referred to as 'functas.' This approach eliminates the need for manual feature engineering on raw data. Specifically, we showcase the efficacy of the 'functas' method in the domain of human activity recognition, utilizing output data from sensors such as accelerometers and gyroscopes. Our results demonstrate the promising potential of the 'functas' approach, suggesting a potential shift in the paradigm of data science methodologies.