Abstract. This paper describes how we recognize activities of daily living (ADLs) with our designed sensor device, which is equipped with heterogeneous sensors such as a camera, a ...
Abstract. This paper describes daily life activity recognition using wearable acceleration sensors attached to four different parts of the human body. The experimental data set con...
A major challenge in using multi-modal, distributed sensor systems for activity recognition is to maintain a temporal synchronization between individually recorded data streams. A ...
We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and signi...