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Exploratory Data Analysis of Population Level Smartphone-Sensed Data

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1691))

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Abstract

Mobile health involves gathering smartphone-sensor data passively from user’s phones, as they live their lives ’In-the-wild”, periodically annotating data with health labels. Such data is used by machine learning models to predict health. Purely Computational approaches generally do not support interpretability of the results produced from such models. In addition, the interpretability of such results may become difficult with larger study cohorts which make population-level insights desirable. We propose Population Level Exploration and Analysis of smartphone DEtected Symptoms (PLEADES), an interactive visual analytics framework to present smartphone-sensed data. Our approach uses clustering and dimension reduction to discover similar days based on objective smartphone sensor data, across participants for population level analyses. PLEADES enables analysts to apply various clustering and projection algorithms to several smartphone-sensed datasets. PLEADES overlays human-labelled symptom and contextual information from in-the-wild collected smartphone-sensed data, to empower the analyst to interpret findings. Such views enable the contextualization of the symptoms that can manifest in smartphone sensor data. We used PLEADES to visualize two real world in-the-wild collected datasets with objective sensor data and human-provided health labels. We validate our approach through evaluations with data visualization and human context recognition experts.

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Authors and Affiliations

  1. Worcester Polytechnic Institute, MA, USA

    Hamid Mansoor, Walter Gerych, Abdulaziz Alajaji, Luke Buquicchio, Kavin Chandrasekaran, Emmanuel Agu & Elke Rundensteiner

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  1. Hamid Mansoor
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  2. Walter Gerych
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  3. Abdulaziz Alajaji
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  4. Luke Buquicchio
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  5. Kavin Chandrasekaran
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  6. Emmanuel Agu
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  7. Elke Rundensteiner
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Correspondence to Hamid Mansoor .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Augusto de Sousa

  2. Czech Technical University in Prague, Prague, Czech Republic

    Vlastimil Havran

  3. Mines ParisTech, Paris, France

    Alexis Paljic

  4. Davidson College, Davidson, NC, USA

    Tabitha Peck

  5. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  6. Monash University, Melbourne, Australia

    Helen Purchase

  7. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  8. University of Barcelona, Barcelona, Spain

    Petia Radeva

  9. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

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Mansoor, H. et al. (2023). Exploratory Data Analysis of Population Level Smartphone-Sensed Data. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2021. Communications in Computer and Information Science, vol 1691. Springer, Cham. https://doi.org/10.1007/978-3-031-25477-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-25477-2_10

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