An Active Spring Mass Model With Biomimetic Ground Reaction Forces for Multiple Terrains

Common activities of daily living naturally blend walking on level ground, stairs, and slopes. Predicting human locomotion behavior across these environments is critical for control design in lower-limb assistive devices. One approach to achieving this goal is to use feedforward simulation of conceptual gait models that capture the whole body dynamics of human locomotion. So far, however, these models have mainly been developed to predict human walking and running dynamics on level ground.

To develop a conceptual gait model that can walk on level ground as well as negotiate stairs and ramps of different inclinations (up to $\pm 42^\circ$) while producing biomimetic ground reaction forces (GRFs).

We extended the passive bipedal spring mass model by incorporating active elements, introducing seven control parameters. These parameters were then tuned to match the human GRFs for stairs.

By optimizing these control parameters, we found that the resultant model's GRF correlates strongly with human data with median Pearson correlation coefficients $R_{x}>0.89$ and $R_{y}>0.96$ for stair walking. Furthermore, we observe its predictive capabilities are not limited to stairs, as it can also mimic the GRFs of human slope walking. Finally, we conducted a parameter analysis to assess how model parameters affect GRFs and identified a reduced, five parameter model that can reproduce human GRFs with a fidelity similar to the seven parameter model.

The model may help to predict human locomotion behavior in more complex terrain, for instance, when planning foot placements in the interactive control of powered lower-limb exoskeletons and prostheses.