Activity recognition for persons with stroke using mobile phone technology: Toward improved performance in a home setting

Megan K. O'Brien, Nicholas Shawen, Chaithanya K. Mummidisetty, Saninder Kaur, Xiao Bo, Christian Poellabauer, Konrad Kording, Arun Jayaraman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Background: Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. Objective: In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). Methods: Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone’s accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. Results: A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). Conclusions: Stroke-based training data is needed for high quality AR among gait-impaired individuals with stroke. Additionally, AR systems for home and community monitoring would likely benefit from including at-home activities in the training data.

Original languageEnglish (US)
Article numbere184
JournalJournal of medical Internet research
Issue number5
StatePublished - May 2017


  • Activities of daily living
  • Ambulatory monitoring
  • Machine learning
  • Smartphone
  • Stroke rehabilitation

ASJC Scopus subject areas

  • Health Informatics


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