Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living

Abstract

Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.