3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling

In this paper, we present a novel low-cost computationally efficient method to accurately assess human gait by monitoring the 3D trajectory of the lower limb, both left and right legs outside the lab in any unconstrained environment. Our method utilizes a network of miniaturized wireless inertial sensors, coupled with a suite of real-time analysis algorithms and can operate in any unconstrained environment. First, we adopt a modified computationally efficient, highly accurate, and near real-time gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative in order to obtain the 3D orientation of each of the six segments. Second, by utilizing the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the left and right feet during functional phases of a gait cycle. Third, by setting the foot segment as the root node we calculate the 3D orientation and position of the other two segments as well as the left and right ankle, knee, and hip joints. We then employ a customized kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behavior of the leg. Pearson’s correlation coefficient (
$r$
) and significant difference test results (
$P$
) were used to quantify the relationship between the calculated and measured movements for all joints in the sagittal plane. The correlation between the calculated and the reference was found to have similar trends for all six joints
$(r>0.94, p<0.005)$
.