Design, Measurement

Many ubiquitous computing applications involve human ac-tivity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard bench-marks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consist-ing of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for health-care scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others