Measuring clinically relevant knee motion with a self-calibrated wearable sensor

Todd J. Hullfish, Feini Qu, Brendan D. Stoeckl, Peter M. Gebhard, Robert L. Mauck, Josh R. Baxter

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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. 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. Sagittal plane knee motion was quantified using a 9-axis motion sensor and directly compared to motion capture data. The motion sensor measured the field strength of a strong earth magnet secured to the distal femur, which was correlated with knee angle during a simple calibration process. Peak knee motions and kinematic patterns were compared with motion capture data using paired t-tests and cross correlation, respectively. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (root mean square error: 2.6 degrees, P = 0.29). Knee flexion during gait strongly correlated (0.84 ≤ r xy ≤ 0.99)with motion capture measurements but demonstrated peak flexion errors of 10 degrees. In this study, we present a low-cost sensor (≈$ 35 US)that accurately determines knee extension angle following a calibration procedure that did not require any other equipment. Our findings demonstrate that this sensor paradigm is a feasible tool to monitor patient progress throughout physical therapy. However, dynamic motions that are associated with soft-tissue artifact may limit the accuracy of this type of wearable sensor.

Original languageEnglish
Pages (from-to)105-109
Number of pages5
JournalJournal of Biomechanics
StatePublished - May 24 2019


  • Inertial measurement unit
  • Low-cost sensor
  • Motion capture
  • Wearable sensors


Dive into the research topics of 'Measuring clinically relevant knee motion with a self-calibrated wearable sensor'. Together they form a unique fingerprint.

Cite this