Ferrarin Maurizio, Rabuffetti Marco, Geda Elisabetta, Sirolli Silvia, Marzegan Alberto, Bruno Valentina, Sacco Katiuscia
1 IRCCS Fondazione Don Carlo Gnocchi Onlus, Polo Tecnologico, Milano, Italy.
2 Dipartimento di Psicologia, Università di Torino, Torino, Italy.
Proc Inst Mech Eng H. 2018 Jun;232(6):619-627. doi: 10.1177/0954411918776682.
Several robotic devices have been developed for the rehabilitation of treadmill walking in patients with movement disorders due to injuries or diseases of the central nervous system. These robots induce coordinated multi-joint movements aimed at reproducing the physiological walking or stepping patterns. Control strategies developed for robotic locomotor training need a set of predefined lower limb joint angular trajectories as reference input for the control algorithm. Such trajectories are typically taken from normative database of overground unassisted walking. However, it has been demonstrated that gait speed and the amount of body weight support significantly influence joint trajectories during walking. Moreover, both the speed and the level of body weight support must be individually adjusted according to the rehabilitation phase and the residual locomotor abilities of the patient. In this work, 10 healthy participants (age range: 23-48 years) were asked to walk in movement analysis laboratory on a treadmill at five different speeds and four different levels of body weight support; besides, a trial with full body weight support, that is, with the subject suspended on air, was performed at two different cadences. The results confirm that lower limb kinematics during walking is affected by gait speed and by the amount of body weight support, and that on-air stepping is radically different from treadmill walking. Importantly, the results provide normative data in a numerical form to be used as reference trajectories for controlling robot-assisted body weight support walking training. An electronic addendum is provided to easily access to such reference data for different combinations of gait speeds and body weight support levels.
已经开发了几种机器人设备，用于帮助因中枢神经系统损伤或疾病而患有运动障碍的患者在跑步机上进行步行康复训练。这些机器人可诱导协调的多关节运动，旨在重现生理步行或踏步模式。为机器人运动训练开发的控制策略需要一组预定义的下肢关节角轨迹作为控制算法的参考输入。此类轨迹通常取自地面无辅助步行的规范数据库。然而，已经证明，步态速度和体重支撑量在步行过程中会显著影响关节轨迹。此外，速度和体重支撑水平都必须根据康复阶段和患者的残余运动能力进行个体化调整。在这项研究中，10名健康参与者（年龄范围：23 - 48岁）被要求在运动分析实验室的跑步机上以五种不同速度和四种不同体重支撑水平行走；此外，还在两种不同节奏下进行了一次全身重量支撑试验，即让受试者悬浮在空中。结果证实，步行过程中的下肢运动学受步态速度和体重支撑量的影响，并且空中踏步与跑步机行走有根本不同。重要的是，研究结果以数值形式提供了规范数据，可作为控制机器人辅助体重支撑步行训练的参考轨迹。本文还提供了一个电子附录，以便轻松获取不同步态速度和体重支撑水平组合的此类参考数据。
