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Evaluation of a Pointwise Local Visual Pattern Exploration Method

Tsinghua Science and Technology 2012年第4期17卷 429-439页

作者： Matthew O.Ward elke a.rundensteiner Carolina Ruiz Department of Computer Science Worcester Polytechnic InstituteWorcesterMA 01609USA

Sensitivity analysis is a powerful method for discovering the significant factors that contribute to understanding the interaction between variables in multivariate datasets. A number of sensitivity analysis methods fall into the class of local analysis, in which the sensitivity is defined as the partial derivatives of a target variable with respect to a group of independent variables. In a recent paper, we presented a novel pointwise local pattern exploration system for visual sensitivity analysis. Using this system, analysts are able to explore local patterns and the sensitivity at individual data points, which reveals the relationships between a focal point and its neighbors this paper we present several evaluations of the system, including case studies with real datasets, user studies the effectiveness of the visualizations and interactions, and a detailed description of the experience of a user In on

关键词： knowledge discovery sensitivity analysis local pattern visualization evaluation

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ARGUS: Interactive visual analysis of disruptions in smartphone-detected Bio-Behavioral Rhythms

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Visual Informatics 2021年第3期5卷 39-53页

作者： Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu elke rundensteiner Worcester Polytechnic Institute USA

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 *** 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 *** 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 *** 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 *** assist analysts in the discovery and understanding of HBR patterns,disruptions and causes,we propose ARGUS,an interactive visual analytics *** 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 *** score is then visualized using a glyph that makes it easy to recognize disruptions in the regularity of *** 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 *** array of visualization overlays in ARGUS enables analysts to gain a more comprehensive picture of HBRs,behavioral patterns and deviations from *** design of ARGUS was guided by a goal and task analysis study involving an expert versed in HBR and smartphone *** demonstrate its utility and generalizability,two different datasets were explored using ARGUS and our use ca

关键词： Interactive visual analytics Circadian rhythms Smartphone-sensed data

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INPHOVIS:Interactive visual analytics for smartphone-based digital phenotyping

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Visual Informatics 2023年第2期7卷 13-29页

作者： Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu elke rundensteiner Angela Incollingo Rodriguez Worcester Polytechnic Institute USA

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 *** support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their ***,participants are then asked to provide ground truth labels about their health *** such complex data is challenging due to limited contextual information and imperfect health/wellness *** propose INteractive PHOne-o-typing VISualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study *** 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 ***,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 *** contrast,the‘‘low-level"sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using *** 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 *** guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain *** 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 *** demonstrate the usefulness of INPHOVIS with walk-throughs of use *** also evaluated INPHOVIS with expert feedb

关键词： Interactive visual analytics Smartphone-sensed data Digital phenotyping

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