Biorobotics Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
Med Biol Eng Comput. 2011 Oct;49(10):1173-85. doi: 10.1007/s11517-011-0816-1. Epub 2011 Sep 1.
In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibration.
在本文中，我们提出了一种在周期性运动中提供辅助的新方法。该方法是无轨迹的，也就是说，它提供了用户辅助，而与执行的运动无关，并且除了辅助机器人的编码器之外，不需要其他传感器。该方法基于自适应振荡器，即能够学习周期性输入信号的高级特征（频率、包络等）的数学工具。在这里，我们介绍了最近进行的两项实验，以验证我们的方法：设计为概念验证的简单肘部正弦运动，以及步行实验。在这两种情况下，我们都收集了证据，证明我们的方法确实在运动执行过程中辅助了健康受试者。由于涉及下肢的日常生活运动固有周期性，我们推测我们的方法有望为下肢设计创新的康复和辅助协议，而只需要很少或不需要用户特定的校准。
