Monitoring Daily Activities of Multiple Sclerosis Patients with Connected Health Devices

Sourav Bhattacharya, Alberto Gil C.P. Ramos, Fahim Kawsar, Nicholas D. Lane, Lynn M. Gionta, Joanne Manidis, Greg Silvesti, Mathieu Vegreville

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

We report results from a pilot study that focuses mainly on understanding the everyday life quality of patients suffering from multiple sclerosis through the lens of connected Nokia Health devices. Our dataset comprises of 198 individuals (184 females and 14 males) and the study lasted over six months. By analyzing carefully crafted user-studies and correlating with personal sensor data collected with Nokia devices, we found that the level of fatigue is one of the main sources of discomfort across the majority of the patients. We further perform an exploratory analysis, which provides an early indication that by actively monitoring and perturbing users' daily activity levels, such as increasing daily step-counts, sleep duration and decreasing body weight, patients can potentially reduce their daily fatigue level.

Original languageEnglish
Title of host publicationUbiComp'18
Subtitle of host publicationProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
Place of PublicationNew York, NY
PublisherACM
Pages666-669
Number of pages4
ISBN (Print)978-145035966-5
DOIs
Publication statusPublished - 2018
Event2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018

Conference

Conference2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
Country/TerritorySingapore
CitySingapore
Period8/10/1812/10/18

Keywords

  • Connected devices
  • Health
  • Machine learning
  • Statistical analysis

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