Reliable Discovery of Places of Importance and Behavioral Patterns from Mobile Crowdsensed Datasets

It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is relevant to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network connectivity or GPS signal) results in a large number of small segments, where many of these segments actually belong together. This work proposes a novel segmentation approach that opportunistically fills gaps in a user's location trace by either borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm or by utilizing a user's personal data obtained from multiple sensor sources and devices such as the battery recharge behavior (measured on smartphones), step counts, and sleep patterns (measured by wearables). Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach able to generate fewer, but more complete segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place.

This work further presents a user validation mechanism based on combinations of three types of coarse-grained minute-level biometrics: behavioral (step counts), physiological (heart rate), and hybrid (calorie burn and metabolic equivalent of task) collected from wearables. This continuous user validation can help the researchers to build a reliable subject compliance and incentive mechanisms, which is missing in existing mechanism that relies on either data volume or quality. This can further be extended to build a continuous and implicit wearable device user authentication mechanism which will overcome the limitations of existing explicit authentication approaches (i.e., PINs or pattern locks) and thereby, can provide various types of secure services including health and fitness tracking, financial transactions, and unlocking smart locks and vehicles.