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Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor

Todd J. Hullfish, Feini Qu, Brendan D. Stoeckl, Peter M. Gebhard, Robert L. Mauck, Josh R. Baxter
doi: https://doi.org/10.1101/309328
Todd J. Hullfish
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Feini Qu
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
2Translational Musculoskeletal Research Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
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Brendan D. Stoeckl
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Peter M. Gebhard
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Robert L. Mauck
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
2Translational Musculoskeletal Research Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
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Josh R. Baxter
1Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Abstract

Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. Therefore, the purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Knee flexion was quantified using a 9-axis motion sensor and directly compared to motion capture data. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (RMS error: 2.6 degrees, P = 0.29) but less accurate during sit-to-stand exercises (RMS error: 16.6 degrees, P = 0.48). Knee flexion during gait strongly correlated (0.84 ≤ rxy ≤ 0.99) with motion capture measurements but demonstrated average errors of 10 degrees. This study demonstrated a low-cost sensor that satisfied our criteria: a simple calibration procedure resulting in accurate measures of joint function during clinical exercises, making it a feasible tool for continuous patient monitoring to guide regenerative rehabilitation.

Footnotes

  • Ethics Approval and Consent to Participate this study was approved by the Institutional Review Board at the University of Pennsylvania (#826667). Subjects provided written-informed consent.

  • Funding no funding has been provided for this research

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 10, 2018.
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Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor
Todd J. Hullfish, Feini Qu, Brendan D. Stoeckl, Peter M. Gebhard, Robert L. Mauck, Josh R. Baxter
bioRxiv 309328; doi: https://doi.org/10.1101/309328
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Measuring Clinically Relevant Knee Motions With A Self-Calibrated Wearable Sensor
Todd J. Hullfish, Feini Qu, Brendan D. Stoeckl, Peter M. Gebhard, Robert L. Mauck, Josh R. Baxter
bioRxiv 309328; doi: https://doi.org/10.1101/309328

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