Human-machine interfaces (HMI) play a pivotal role in the rehabilitation and daily assistance of lower-limb amputees. The brain of such interfaces is a control model that detects the user's intention using sensor input and generates corresponding output (control commands). With recent advances in technology, AI-based policies have gained attention as control models for HMIs. However, supervised learning techniques require affluent amounts of labeled training data from the user, which is challenging in the context of lower-limb rehabilitation. Moreover, a static pre-trained model does not take the temporal variations in the motion of the amputee (e.g., due to speed, terrain) into account. In this study, we aimed to address both of these issues by creating an incremental training approach for a torque prediction model using incomplete user-specific training data and biologically inspired temporal patterns of human gait. To reach this goal, we created a hybrid of two distinct approaches, a generic inter-individual and an adapting individual-specific model that exploits the inter-limb synergistic coupling during human gait to learn a function that predicts the torque at the ankle joint continuously based on the kinematic sequences of the hip, knee, and shank. An inter-individual generic base model learns temporal patterns of gait from a set of able-bodied individuals and predicts the gait patterns for a new individual, while the individual-specific adaptation model learns and predicts the temporal patterns of gait specific to a particular individual. The iterative training using the hybrid model was validated on eight able-bodied and five transtibial amputee subjects. It was found that, with the addition of estimators fitted to individual-specific data, the accuracy significantly increased from the baseline inter-individual model and plateaued within two to three iterations.
翻译:人体机器界面(HMI)在低limb 截肢者的康复和日常援助中发挥着关键作用。 这种界面的大脑是一个控制模型,它用传感器输入检测用户的意图并生成相应的输出(控制命令)。随着技术的最近进步,基于AI的政策作为HMI的控制模型得到了关注。然而,监督的学习技术需要用户提供大量贴标签的培训数据,这在低limb 恢复方面具有挑战性。此外,静态的预培训模型并不考虑截肢者运动(例如,由于速度、地形)的时间变化。在这个研究中,我们的目标是通过对调心预测模型的渐进式培训方法,使用不完整的用户特定培训数据和生物激发的人类动作时间模式。为了实现这一目标,我们创建了两种不同的混合方法,一种通用的跨个体模型和适应个人模式,在人造图期间,利用经确认的周期间周期性变精度变精度(例如,由于速度、地形)的动作变化模式。我们的目标是,通过对调整尾部进行渐进式分析,然后在连续的骨架上,通过一个连续的模型进行学习。