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 supp...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.展开更多
Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health ramifications.Ubiquitous devices such as smartphones can...Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health ramifications.Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns.As human behavior is complex and smartphone data is voluminous with many channels(sensor types),it can be challenging to make meaningful observations,detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions.Prior work has largely utilized computational methods such as machine and deep learning approaches,which while accurate,are often not explainable and present few actionable insights on HBR patterns or causes.To assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics framework.As a foundation of ARGUS,we design an intuitive Rhythm Deviation Score(RDS)that analyzes users’smartphone sensor data,extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs.ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state(screen locked,charging),and user-provided or smartphone-inferred ground truth information.This array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from regularity.The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing.To demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.展开更多
基金This material is based on research sponsored by DARPA,United States under agreement number FA8750-18-2-0077The U.S.Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation thereonThe views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements,either expressed or implied,of DARPA or the U.S.Government。
文摘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.
基金This material is based on research sponsored by DARPA,USA under agreement number FA8750-18-2-0077。
文摘Human Bio-Behavioral Rhythms(HBRs)such as sleep-wake cycles(Circadian Rhythms),and the degree of regularity of sleep and physical activity have important health ramifications.Ubiquitous devices such as smartphones can sense HBRs by continuously analyzing data gathered passively by built-in sensors to discover important clues about the degree of regularity and disruptions in behavioral patterns.As human behavior is complex and smartphone data is voluminous with many channels(sensor types),it can be challenging to make meaningful observations,detect unhealthy HBR deviations and most importantly pin-point the causes of disruptions.Prior work has largely utilized computational methods such as machine and deep learning approaches,which while accurate,are often not explainable and present few actionable insights on HBR patterns or causes.To assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics framework.As a foundation of ARGUS,we design an intuitive Rhythm Deviation Score(RDS)that analyzes users’smartphone sensor data,extracts underlying twenty-four-hour rhythms and quantifies their degree of irregularity.This score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of HBRs.ARGUS also facilitates deeper HBR insights and understanding of causes by linking multiple visualization panes that are overlaid with objective sensor information such as geo-locations and phone state(screen locked,charging),and user-provided or smartphone-inferred ground truth information.This array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from regularity.The design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone sensing.To demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use cases and designs were strongly validated in evaluation sessions with expert and non-expert users.
