Quantification of gait parameters with inertial sensors and inverse kinematics

• Published in: Journal of biomechanics Vol. 72; pp. 207 - 214
• Main Authors: Bötzel, Kai, Olivares, Alberto, Cunha, João Paulo, Górriz Sáez, Juan Manuel, Weiss, Robin, Plate, Annika
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 27.04.2018; Elsevier Limited
• Subjects: Accelerometers; Angular position; Angular velocity; Chain mobility; Computation; Filtration; Gait; Gait recognition; Gait recording; Gyroscopes; Human; Inertial sensing devices; Inertial sensors; Inverse kinematics; Kalman filter; Kalman filters; Kinematics; Leg; Mathematical models; Motion capture; Parameters; Pelvis; Position sensing; Scientific papers; Segments; Sensors; Spheres; Three dimensional motion; Velocity; United States > US; Human; Gait recording; Inertial sensors; Kalman filter
• ISSN: 0021-9290; 1873-2380; 1873-2380
• DOI: 10.1016/j.jbiomech.2018.03.012

Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson’s disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill. We found the precision of the Kalman filtered angular position is in the range of 2–6° (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed.