# Machine learning for analysis of walking patterns and physical activity in knee osteoarthritis

> **NIH NIH F32** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2021 · $12,654

## Abstract

Project Summary/Abstract
Individuals with knee osteoarthritis (OA) exhibit altered walking patterns that cause repetitive, abnormal forces
on the knee joint, leading to disease progression. Existing interventions to reduce knee loading during walking
have not resulted in meaningful change in knee OA symptoms or joint structure. A key limitation of existing
research has been the use of simplified metrics to describe walking patterns and not accounting for walking
amount and intensity (i.e. physical activity). The goal of this research is to comprehensively characterize
walking patterns and activity in people with and without knee OA and to assess the associations of walking
with 2-year change in knee OA outcomes. Machine learning approaches will be used to analyze ground
reaction force (GRF) data and accelerometer-derived physical activity metrics in an existing, large, well-
characterized cohort (n=2575) from the Multicenter Osteoarthritis Study (MOST). Machine learning approaches
that use selected features and those that are agnostic and utilize all available information from time-varying
GRFs will be used in combination with physical activity metrics to classify symptomatic and structural change.
The results from machine learning approaches will be compared to those of common statistical approaches.
This research will allow for characterization of the complex relationships between walking patterns and activity,
providing novel insights into OA disease processes. Further, this research will provide valuable training in
applying machine learning approaches to biomechanics data and may inform patient-specific strategies to
optimize walking patterns and physical activity for personalized knee OA management.
 The principal investigator will leverage prior training in biomedical engineering applied to OA research
to further advance her skills in traditional and agnostic machine learning approaches for analyses of
biomechanics and physical activity data. The sponsor and co-sponsor at Boston University (BU) will provide
mentorship in clinical aspects of OA, implementation of the proposed studies in a large cohort, grantsmanship,
and career development. The team will work with a collaborator in computational biomedicine at BU with
expertise in machine learning to achieve the scientific and training goals of this project. In addition to hands-on
training, the principal investigator will enroll in didactic coursework and workshops at BU related to machine
learning and computer programming. Other key aspects of training include participation in research and
networking opportunities at BU and other local Institutions, as well as national and international meetings. The
sponsors and the Institution provide an environment where the PI will work and learn as a part of a diverse and
interdisciplinary team of OA researchers across rehabilitation, rheumatology, epidemiology, computational
methods, and imaging specialties. This postdoctoral training environment wi...

## Key facts

- **NIH application ID:** 10251919
- **Project number:** 5F32AR076907-02
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Kerry Elizabeth Costello
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $12,654
- **Award type:** 5
- **Project period:** 2020-08-01 → 2021-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10251919

## Citation

> US National Institutes of Health, RePORTER application 10251919, Machine learning for analysis of walking patterns and physical activity in knee osteoarthritis (5F32AR076907-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10251919. Licensed CC0.

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