CARMUS: Towards a General Framework for Continuous Activity Recognition with Missing Values on Smartphones.

This paper presents the CARMUS framework for continuous activity recognition with missing values on smartphones. Besides the power and resource constraints discussed in existing work, our framework is proposed to further tackle the critical issue of missing values during data collection. We demonstrate the issueu0027s impact on continuous recognition through a motivating example, and specify two challenges—blackouts and resource constraints-with respect to smartphone-based sensing and processing platforms. To address the challenges, CARMUS provides a novel framework which involves a light-weight admission control unit and a data imputation unit intuited by the daily repeated pattern and temporal smoothness of human activity data. Based on extensive experiments conducted on a real-world data set with 37% of the data missing, we show that the CARMUS framework is effective for achieving an 85.5% recognition accuracy by adopting the state-of-the-art imputation algorithms.