  This paper presents an algorithm that makes novel use of distance
measurements alongside a constrained Kalman filter to accurately estimate
pelvis, thigh, and shank kinematics for both legs during walking and other body
movements using only three wearable inertial measurement units (IMUs). The
distance measurement formulation also assumes hinge knee joint and constant
body segment length, helping produce estimates that are near or in the
constraint space for better estimator stability. Simulated experiments shown
that inter-IMU distance measurement is indeed a promising new source of
information to improve the pose estimation of inertial motion capture systems
under a reduced sensor count configuration. Furthermore, experiments show that
performance improved dramatically for dynamic movements even at high noise
levels (e.g., $\sigma_{dist} = 0.2$ m), and that acceptable performance for
normal walking was achieved at $\sigma_{dist} = 0.1$ m. Nevertheless, further
validation is recommended using actual distance measurement sensors.
