Estimation of ground reaction forces during stair climbing in patients with ACL reconstruction using a depth sensor-driven musculoskeletal model.

Abstract Background Although stair ambulation should be included in the rehabilitation of the long-term effects of ACL injury on knee function, the assessment of kinetic parameter in the situation where stair gait can only be established using costly and cumbersome force platforms via conventional inverse dynamic analysis. Therefore, there is a need to develop a practical laboratory setup as an assessment tool of the stair gait abnormalities in lower extremity that arise from an ACL deficiency. Research question Can the use of a single depth sensor-driven full-body musculoskeletal gait model be considered an accurate assessment tool of the ground reaction forces (GRFs) during stair climbing for patients following ACL reconstruction (ACLR) surgery? Methods A total of 15 patients who underwent ACLR participated in this study. GRFs data during stair climbing was collected using a custom-built 3-step staircase with two embedded force platforms. A single depth sensor, commercially available and cost effective, was used to obtain participants’ depth map information to extract the full-body skeleton information. The AnyBody TM GaitFullBody model was utilized to estimate GRFs attained by 25 artificial muscle-like actuators placed under each foot. Mean differences between the measured and estimated GRFs were compared using paired samples t-tests. The ensemble curves of the GRFs were compared between both approaches during stance phase of the gait cycle. Results The findings of this study showed that the estimation of the GRFs produced during staircase gait using a depth sensor-driven musculoskeletal model can produce acceptable results when compared to the traditional inverse dynamics modelling approach as an alternative tool in clinical settings for individuals who had undergone ACLR. Significance The introduced approach of full-body musculoskeletal modelling driven by a single depth sensor has the potential to be a cost-effective stair gait analysis tool for patients with ACL injury.