Electronically augmented gait abnormality assessment following lower extremity trauma.
OTA Int 2019;
2:e032. [PMID:
33937664 PMCID:
PMC7997086 DOI:
10.1097/oi9.0000000000000032]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 12/26/2018] [Indexed: 11/26/2022]
Abstract
Background:
Objective evaluation of patient outcomes has become an essential component of patient management. Along with patient-reported outcomes, performance-based measures (PBMs) such as gait analysis are an important part of this evaluation. The purpose of this study was to evaluate the validity of utilizing a wearable inertial measurement unit (IMU) in an outpatient clinic setting to assess its ability to provide clinically relevant data in patients with altered gait resulting from lower extremity trauma.
Methods:
Five orthopaedic trauma patients with varying degrees of gait pathologies were compared to 5 healthy control subjects. Kinematic data were simultaneously recorded by the IMU and a gold standard Vicon video motion analysis system (Vicon Motion Systems Ltd, Oxford, UK) during a modified 10-m walk test. Raw data captured by the IMU were directly compared to Vicon data. Additionally, 5 objective gait parameters were compared for controls and the 5 trauma patients.
Results:
The IMU data streams strongly correlated with Vicon data for measured variables used in the subsequent gait analysis: vertical acceleration, vertical displacement, pitch angular velocity, and roll angular velocity (Pearson r-value > 0.9 for all correlations). Quantitative kinematic data in post-trauma patients significantly differed from control data and correlated with observed gait pathology.
Conclusions:
When compared to the gold standard motion capture reference system (Vicon), an IMU can reliably and accurately measure clinically relevant gait parameters and differentiate between normal and pathologic gait patterns. This technology is easily integrated into clinical settings, requires minimal time, and represents a performance-based method for quantifiably assessing gait outcomes.
Level of Evidence:
Diagnostic Level 1.
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