Enabling large-scale human activity inference on smartphones using community similarity networks (csn)

Abstract

Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor-data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor-data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.