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This paper presents an algorithm that makes novel use of distancemeasurements alongside a constrained Kalman filter to accurately estimatepelvis, thigh, and shank kinematics for both legs during walking and other bodymovements using only three wearable inertial measurement units (IMUs). Thedistance measurement formulation also assumes hinge knee joint and constantbody segment length, helping produce estimates that are near or in theconstraint space for better estimator stability. Simulated experiments shownthat inter-IMU distance measurement is indeed a promising new source ofinformation to improve the pose estimation of inertial motion capture systemsunder a reduced sensor count configuration. Furthermore, experiments show thatperformance improved dramatically for dynamic movements even at high noiselevels (e.g., $\sigma_{dist} = 0.2$ m), and that acceptable performance fornormal walking was achieved at $\sigma_{dist} = 0.1$ m. Nevertheless, furthervalidation is recommended using actual distance measurement sensors.

Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count