Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare

The rapid advance of the Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of IoT assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse life patterns in an IoT environment, lifelogging personal data contains much uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, it takes lifelogging physical activity as a target to explore the possibility of improving validity of lifelogging data in an IoT based healthcare environment. A rule based adaptive lifelogging physical activity validation model, LPAV-IoT, is proposed for eliminating irregular uncertainties and estimating data reliability in IoT healthcare environments. In LPAV-IoT, a methodology specifying four layers and three modules is presented for analyzing key factors impacting validity of lifelogging physical activity. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on an IoT enabled personalized healthcare platform MHA [38] connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of irregular uncertainty and adaptively indicating the reliability of lifelogging physical activity data on certain condition of an IoT personalized environment