INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping - Hamid Mansoor; Walter Gerych; Abdulaziz Alajaji; Luke Buquicchio; Kavin Chandrasekaran; Emmanuel Agu; Elke Rundensteiner; Angela Incollingo Rodriguez (Visual Informatics, 2023)
Digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’ state and especially to identify ailments. To support supervised machine learning, digital phenotyping requires gathering data from study participants’ smartphones as they live their lives. Periodically, participants are then asked to provide ground truth labels about their health status. Analyzing such complex data is challenging due to limited contextual information and imperfect health/wellness labels. We propose INteractive PHOne-o-typing VISualization (INPHOVIS), an interactive visual framework for exploratory analysis of smartphone health data to study phone-o-types. Prior visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as smartwatches. However, unlike smartphones which are owned by over 85 percent of the US population, wearable devices are less prevalent thus reducing the number of people from whom such data can be collected. In contrast, the “low-level” sensor data (e.g., accelerometer or GPS data) supported by INPHOVIS can be easily collected using smartphones. Data visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive phone-o-types. To guide the design of INPHOVIS, we performed a hierarchical task analysis of phone-o-typing requirements with health domain experts. We then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns, calendar views to visualize day-level data along with bar charts, and correlation views to visualize important wellness predictive data. We demonstrate the usefulness of INPHOVIS with walk-throughs of use cases. We also evaluated INPHOVIS with expert feedback and received encouraging responses.