Influence of body segments’ parameters estimation models on inverse dynamics solutions during gait

• Published in: Journal of biomechanics Vol. 39; no. 8; pp. 1531 - 1536
• Main Authors: Rao, Guillaume, Amarantini, David, Berton, Eric, Favier, Daniel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2006; Elsevier Limited; Elsevier
• Subjects: Adult; Algorithms; Ankle; Biomechanics; BSP estimation models; Computer Simulation; Dimensional analysis; Engineering Sciences; Estimates; Female; Gait; Gait - physiology; Hip joint; Humans; Inertia; Inverse dynamics; Joint kinetics; Joints - physiology; Kinematics; Male; Mechanics; Models, Biological; Muscle Contraction - physiology; Postural Balance - physiology; Joint kinetics; Gait; BSP estimation models; Inverse dynamics
• ISSN: 0021-9290; 1873-2380; 1873-2380
• DOI: 10.1016/j.jbiomech.2005.04.014

The purpose of the present study was to examine the influence of anthropometric data on joint kinetics during gait. We particularly focused on the sensitivity of inverse dynamics solutions to the use of models for body segment parameters (BSP) estimation. Six often used estimation models were selected to provide BSP values for the three segments of the lower limb. Kinematics and dynamics were sampled from seven subjects performing barefoot gait at three different speeds. Joint kinetics were estimated with the bottom-up method using BSP values derived from each estimation model as anthropometric inputs. The BSP estimates were highly sensitive to the model used with deviations ranging from at least 9.73% up to 60%. Maximal variations of peak values for the hip joint flexion/extension moment during the swing phase were 20.11%. Hence, our findings suggest that the influence of BSP cannot be neglected. Observed deviations are especially due to the effect of varying simultaneously the mass, moments of inertia and the center of mass location values, according to the underlying relationship of interdependency linking each component. Considering both the differences found in joint kinetics and the level of accuracy of BSP models, evidence is provided that using multiple regression BSP estimation functions derived from Zatsiorsky and Seluyanov (Biomechanics VIII-B, 1983, pp. 1152–1159) should be recommended to assess joint kinetics.