Less is more: Efficient behavioral context recognition using Dissimilarity-Based Query Strategy.

ABSTRACT: With the advancement of ubiquitous computing, smartphone sensors are generating a vast amount of unlabeled data streams ubiquitously. This sensor data can potentially help to recognize various behavioral contexts in the natural environment. Accurate behavioral context recognition has a wide variety of applications in many domains like disease prevention and independent living. However, despite the availability of enormous amounts of sensor data, label acquisition, due to its dependence on users, is still a challenging task. In this work, we propose a novel context recognition approach i.e., Dissimilarity-Based Query Strategy (DBQS). Our approach DBQS leverages Active Learning based selective sampling to find the informative and diverse samples in the sensor data to train the model. Our app

SUBMITTER: Akram A 

PROVIDER: S-EPMC10246805 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature