Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors

Megan K. O’Brien, Francesco Lanotte, Rushmin Khazanchi, Sung Yul Shin, Richard L. Lieber, Roozbeh Ghaffari, John A. Rogers, Arun Jayaraman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objective. Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient’s functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient’s future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. Methods. Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. Results. For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. Conclusion. These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. Impact. Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

Original languageEnglish (US)
Article numberpzad183
JournalPhysical therapy
Volume104
Issue number2
DOIs
StatePublished - Feb 1 2024

Keywords

  • Balance
  • Biomedical Engineering
  • Decision Making: Computer-Assisted
  • Gait
  • Inpatients
  • Outcome Assessment (Health Care)
  • Patient Care Planning
  • Prognosis
  • Rehabilitation
  • Technology Assessment: Biomedical

ASJC Scopus subject areas

  • General Medicine

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