Improving spring-mass parameter estimation in running using nonlinear regression methods [RESEARCH ARTICLE]

We present a method to model runners as spring-mass systems using nonlinear regression (NLR) and the full vertical ground reaction force (vGRF) time series without additional inputs and fewer traditional parameter assumptions. We derived and validated a time-dependent vGRF function characterized by four spring-mass parameters–stiffness, touchdown angle, leg length, and contact time–using a sinusoidal approximation. Next, we compared the NLR-estimated spring-mass parameters to traditional calculations in runners. The mixed-effect NLR method (ME NLR) modeled the observed vGRF best (RMSE:155 N) compared to a conventional sinusoid approximation (RMSE: 230 N). Against the conventional methods, its estimations provided similar stiffness approximations (-0.2±0.6 kN/m) with moderately steeper angles (1.2±0.7°), longer legs (+4.2±2.3 cm), and shorter effective contact times (-12±4 ms). Together, these vGRF-driven system parameters more closely approximated the observed vertical impulses (observed: 214.8 N-s; ME NLR: 209.0 N-s; traditional: 223.6 N-s). Finally, we generated spring-mass simulations from traditional and ME NLR parameter estimates to assess the predicative capacities of each method to model stable running systems. In 6/7 subjects, ME NLR parameters generated models that ran with equal or greater stability than traditional estimates. ME NLR modeling of the vGRF in running is therefore a useful tool to assess runners holistically ...

Source: Journal of Experimental Biology - February 3, 2021 Category: Biology Authors: Burns, G. T., Gonzalez, R., Zernicke, R. F. Tags: RESEARCH ARTICLE Source Type: research