Intelligent Parametric Adaptive Hybrid Active–Passive Training Control Method for Rehabilitation Robot

Abstract

Rehabilitation robots facilitate patients to take part in physical and occupational training. Most of the rehabilitation robots used in clinical practice adopt pure passive training or active training, which cannot sense the active participation of patients during passive training and lack adaptive dynamic adjustment of training parameters for patients. In this paper, an intelligent hybrid active–passive training control method is proposed to enhance the active participation of patients in passive training mode. Firstly, the patients’ joint mobility and maximum muscle power are modelized and calibrated. Secondly, the robot joints are actuated to train according to joint mobility and speed for two cycles. The human–machine coupled force interaction control model can recognize the patients’ active participation in the training process. Finally, the passive training joint motion speed for the next training cycle is adaptively updated by the proposed control method. The experimental results demonstrate that the control method can sense the patients’ active participation and adjust the passive training speed according to the patients’ active force interaction. In conclusion, the hybrid active–passive training control method proposed in this paper achieves the desired goal and effectively improves the patients’ rehabilitation effect.

Active Training Control Method for Rehabilitation Robot Based on Fuzzy Adaptive Impedance Adjustment

“…The robot can be adjusted to different sizes and support positions according to individual patient differences. During use, the patient is placed in a sitting or lying position with both feet on the pedals of the mechanical leg, the centers of the joints of the lower limbs are aligned with the centers of rotation of the robot joints, and the body is fixed to the robot through safety straps and aids to prevent secondary injuries caused by overturning during the training process [16]. In this study, tension pressure sensors were used to identify HMI forces [17], as shown in Figure 2, with pressure sensors on the thigh, shank, and plantar.…”

Passive and Active Training Control of an Omnidirectional Mobile Exoskeleton Robot for Lower Limb Rehabilitation

“…Examples include standing lower limb rehabilitation robots Lokomat [4], ReWalk [5], and Ailegs [6], as well as sitting lower limb rehabilitation robots Motion Maker [7,8], LR2 [9], and LRR-Ro [10]. These rehabilitation robots can provide passive training, active training, and auxiliary training [11]. The sitting and lying lower limb rehabilitation robot can reduce the unnecessary weight load on the body and improve the comfort of the person, as well as facilitate exercise and increase the range of motion of the lower limb joints.…”