Sudip Vhaduri

Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, inter-person variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

Human Factors in the Design of Longitudinal Smartphone-based Wellness Surveys

Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often subjects respond to such surveys once, but it has become increasingly important to capture their responses multiple times over an extended period to accurately and quickly detect and track changes. With the help of smartphones, it is now possible to automate such longitudinal data collections, e.g., push notifications can be used to alert a subject whenever a new survey is available. This paper investigates various human factors in the design of a longitudinal smartphone-based data collection that contribute to user compliance and quality of collected data. This work presents the design recommendations based on analysis of data collected from 17 subjects over a one month period.

Cooperative Discovery of Personal Places from Location Traces

It is becoming increasingly important to accurately detect a users presence at certain locations during certain times of the day, e.g., to study the users 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 users location trace by borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm. Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach yields more and larger segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place.

Design and Implementation of a Remotely Configurable and Manageable Well-being Study

Surveys are essential tools for obtaining an understanding of factors impacting a person’s physical and mental well-being. Recently, surveys using face-to-face interactions have been replaced with smartphone surveys, with the added benefit of using a phone’s sensor and usage data (e.g., locations, apps used, communication patterns, etc.) to collect valuable contextual information. These data collections, especially if longitudinal, often require a certain degree of flexibility and adaptability, e.g., survey questions may change over time or depend on location, demographics, and previous responses. Data collections may also be re-configured to account for changes in the study goals or to test different intervention techniques. Finally, participant compliance should be monitored and may also lead to modifications in the data collection approach. This paper introduces a data collection tool and study design that not only collects surveys and phone sensor data, but also addresses the need for remote customization, reconfiguration, and management.

Towards Reliable Wearable-User Identification

Wearables, such as Fitbit, Apple Watch, and Microsoft Band, with their rich collection of sensors, facilitate the tracking of healthcare- and wellness-related metrics. However, the assessment of the physiological metrics collected by these devices could also be useful in identifying the user of the wearable, e.g., to detect unauthorized use or to correctly associate the data to a user if wearables are shared among multiple users. Further, researchers and healthcare providers often rely on these smart wearables to monitor research subjects and patients in their natural environments over extended periods of time. Again, it is important to associate the sensed data with the corresponding user and to detect if a device is being used by an unauthorized individual. Existing one-time authentications using credentials (e.g., passwords, certificates) or trait-based biometrics (e.g., face, fingerprints, iris, voice) might fail since such credentials can easily be shared among users. Therefore, we need a reliable and continuous wearable-user identification mechanism.