Context-Aware Scheduling in Personal Data Collection From Multiple Wearable Devices

Due to the prevalence of smartphones and various wearable devices, the collection of rich personal data that can be used for human activity recognition, user modeling, and personalized services has become feasible. Because of its popularity and high accessibility, the smartphone has not only become an effective terminal in personal data collection, but also a gateway connecting wearable devices and gathering various types of personal data from these wearables. In most current applications, such wearables operate to collect data according to a fixed schedule, often preset manually by a user. The main problems in the data collection arising from such fixed scheduling are weak adaptiveness to wearables' state changes, a high level of redundancy in collected data, and possible mismatches in the dynamic precision requirements of data capture. Therefore, we propose a context-aware scheduler, that is able to dynamically adjust a data collection schedule based on contingent situations in the condition of wearables, system resource availability, and user behavior. This paper is focused on context data detection and data collection scheduling in a smartphone-based client-server system. The smartphone functions as not only a gateway gathering data from multiple wearables, but also a terminal for the performance of a context-aware scheduler. A context-aware engine is implemented to handle different contextual information. The data quality and system performance have been evaluated and verified in practical experimental tests.