INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Visual Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468502X23000025 |
| _version_ | 1797797137073831936 |
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| author | Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez |
| author_facet | Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez |
| author_sort | Hamid Mansoor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:43:37Z |
| format | Article |
| id | doaj.art-7e3973d0c30e44f0a51747b42c9ef0f4 |
| institution | Directory Open Access Journal |
| issn | 2468-502X |
| language | English |
| last_indexed | 2024-03-13T03:43:37Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Visual Informatics |
| spelling | doaj.art-7e3973d0c30e44f0a51747b42c9ef0f42023-06-23T04:43:47ZengElsevierVisual Informatics2468-502X2023-06-01721329INPHOVIS: Interactive visual analytics for smartphone-based digital phenotypingHamid Mansoor0Walter Gerych1Abdulaziz Alajaji2Luke Buquicchio3Kavin Chandrasekaran4Emmanuel Agu5Elke Rundensteiner6Angela Incollingo Rodriguez7Corresponding author.; Worcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USAWorcester Polytechnic Institute, USADigital 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.http://www.sciencedirect.com/science/article/pii/S2468502X23000025Interactive visual analyticsSmartphone-sensed dataDigital phenotyping |
| spellingShingle | Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping Visual Informatics Interactive visual analytics Smartphone-sensed data Digital phenotyping |
| title | INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping |
| title_full | INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping |
| title_fullStr | INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping |
| title_full_unstemmed | INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping |
| title_short | INPHOVIS: Interactive visual analytics for smartphone-based digital phenotyping |
| title_sort | inphovis interactive visual analytics for smartphone based digital phenotyping |
| topic | Interactive visual analytics Smartphone-sensed data Digital phenotyping |
| url | http://www.sciencedirect.com/science/article/pii/S2468502X23000025 |
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