Deep-learning to map a benchmark dataset of non-amputee ambulation for controlling an open source bionic leg
Powered lower limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as users intend. This paper proposes a direct mapping of voluntary movement of a residual limb (i.e., thigh) to the desired movement of amputated limbs (i.e., knee and ankle) to control the prosthetic legs. The proposed model was learned with the gait trajectory of intact limb individuals from a publicly available biomechanics dataset and applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require the training time with amputation patients nor configuration time to use while providing a closely resembling gait trajectory of the intact limb. As preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long- and short-steps. The results indicate that intact benchmark data can be directly used to train the model to control prosthetic legs.