Deep-Learning to Map a Benchmark Dataset of Non-Amputee Ambulation for Controlling an Open Source Bionic Leg
Deep-Learning to Map a Benchmark Dataset of Non-Amputee Ambulation for Controlling an Open Source Bionic Leg
复制标题
通过深度学习绘制非截肢者行走的基准数据集以控制开源仿生腿
DOI：
10.1109/lra.2022.3194323
复制
发表时间：
2022-10-01
影响因子：
5.2
通讯作者：
Hargrove, Levi J.
中科院分区：
文献类型：
--
作者：
Kim, Minjae;Hargrove, Levi J.
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 those of non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as intended. This letter proposes the direct mapping of the voluntary movement of a residual limb (i.e., thigh) to the desired impedance parameters for amputated limbs (i.e., knee and ankle). The proposed model was learned from the gait trajectories of intact limb individuals from a publicly available biomechanics dataset, and was applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require training time with individuals with amputation nor configuration time for its use, and it provides a closely resembling gait trajectory of the intact limb. For 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-step lengths. The results indicate that intact benchmark data with different sensor configurations can be directly used to train the model to control prosthetic legs.
