Grupo de Automática y Diseño A+D, Cir. 1 #73-76, B22, Medellín, 050031, Colombia.
Grupo de Investigaciones en Bioingeniería, Cir. 1 #73-76, B22, Medellín, 050031, Colombia.
Biomed Eng Online. 2019 Jan 3;18(1):3. doi: 10.1186/s12938-018-0622-1.
A direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, "Nukawa", to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises.
Surface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot.
An experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was [Formula: see text], [Formula: see text], and [Formula: see text] for the hip, knee, and ankle joints, respectively.
The MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.
前交叉韧带（ACL）受伤的一种方式是直接撞击膝盖，例如在足球或交通事故中。机器人辅助治疗（RAT）康复，模拟常规行走，可改善 ACL 重建后的行走和平衡能力以及伸肌力量。但是，在尝试行走等高级运动之前，需要在 ACL 损伤康复的其他阶段进行 RAT。本文旨在为机器人辅助康复系统“Nukawa”提出一种肌电控制（MEC）算法，以协助膝关节在这些类型的运动中的运动，例如在主动辅助伸展运动中。
开发了表面肌电图（sEMG）信号处理算法来检测膝关节的运动意图。sEMG 信号处理算法和运动控制算法是作者在之前的出版物中报告的，它们被合并为硬件在环仿真，以创建和测试 MEC 算法，而不是使用实际的机器人。
对 17 名健康受试者进行了实验方案，以在 12 种 ACL 康复运动中获取 sEMG 信号及其下肢运动学。所提出的运动意图算法在 100%的时间内检测到伸展和弯曲运动的意图方向。此外，它在伸展运动和弯曲运动中，以与最大自主收缩（MVC）相当的方式，分别以 94%和 59%的情况下检测到运动意图的强度。髋关节、膝关节和踝关节的最大位置平均绝对误差分别为[公式：见正文]、[公式：见正文]和[公式：见正文]。
MEC 算法以与 MVC 和方向大致相当的方式检测运动意图的强度。此外，它不需要预先培训或额外的扭矩传感器。它还控制 Nukawa 的膝关节速度，以协助膝关节运动，例如在主动辅助伸展运动中。
