An automated image-based method of 3D subject-specific body segment parameter estimation for kinetic analyses of rapid movements.
Accurate subject-specific body segment parameters (BSPs) are necessary to perform kinetic analyses of human movements with large accelerations, or no external contact forces or moments. A new automated topographical image-based method of estimating segment mass, center of mass (CM) position, and moments of inertia is presented. Body geometry and volume were measured using a laser scanner, then an automated pose and shape registration algorithm segmented the scanned body surface, and identified joint center (JC) positions. Assuming the constant segment densities of Dempster, thigh and shank masses, CM locations, and moments of inertia were estimated for four male subjects with body mass indexes (BMIs) of 19.7-38.2. The subject-specific BSP were compared with those determined using Dempster and Clauser regression equations. The influence of BSP and BMI differences on knee and hip net forces and moments during a running swing phase were quantified for the subjects with the smallest and largest BMIs. Subject-specific BSP for 15 body segments were quickly calculated using the image-based method, and total subject masses were overestimated by 1.7-2.9%.When compared with the Dempster and Clauser methods, image-based and regression estimated thigh BSP varied more than the shank parameters. Thigh masses and hip JC to thigh CM distances were consistently larger, and each transverse moment of inertia was smaller using the image-based method. Because the shank had larger linear and angular accelerations than the thigh during the running swing phase, shank BSP differences had a larger effect on calculated intersegmental forces and moments at the knee joint than thigh BSP differences did at the hip. It was the net knee kinetic differences caused by the shank BSP differences that were the largest contributors to the hip variations. Finally, BSP differences produced larger kinetic differences for the subject with larger segment masses, suggesting that parameter accuracy is more important for studies focused on overweight populations. The new image-based BSP estimation method described in this paper addressed the limitations of currently used geometric and regression methods by using exact limb geometry to determine subject-specific parameters. BSP differences have the largest effect on kinetic analyses of motions with large limb accelerations, for joints farther along the kinematic chain from the known forces and moments, and for subjects with larger limb masses or BMIs.
Department of Mechanical Engineering, Biomotion Laboratory, Stanford University, Stanford, CA 94305, USA. alsheets@stanford.edu
Alison L
AL
Sheets
Stefano
S
Corazza
Thomas P
TP
Andriacchi
D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D006621:Hip Joint / Q000033:anatomy & histology* / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007719:Knee Joint / Q000033:anatomy & histology* / Q000502:physiology*; D008297:Male; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D019415:Torque; D051598:Whole Body Imaging / Q000379:methods*