Portable in-clinic video-based gait analysis: validation study on prosthetic users

Abstract

Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinic. Specifically, estimated walking velocity was similar to annotated 10-meter walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pre-trained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but it is possible to train a prosthetic-specific joint detector. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code and trained weights will be released upon publication.