Novel Design and Adaptive Fuzzy Control of a Lower-Limb Elderly Rehabilitation

Abstract

Design and control of a lower-limb exoskeleton rehabilitation of the elderly are the main challenge for health care in the past decades. In order to satisfy the requirements of the elderly or disabled users, this paper presents a novel design and adaptive fuzzy control of lower-limb empowered rehabilitation, namely MOVING UP. Different from other rehabilitation devices, this article considers active rehabilitation training devices. Firstly, a novel product design method based on user experience is proposed for the lower-limb elderly exoskeleton rehabilitation. At the same time, in order to achieve a stable operation control for the assistant rehabilitation system, an adaptive fuzzy control scheme is discussed. Finally, the feasibility of the design and control method is validated with a detailed simulation study and the human-interaction test. With the booming demand in the global market for the assistive lower-limb exoskeleton, the methodology developed in this paper will bring more research and manufacturing interests. Electronics 2020, 9, 343 2 of 17 the effectiveness of rehabilitation training. A wearable upper limb therapy robot based on Pneumatic Muscle Actuator (PMA) was introduced in Reference [13]. This device has five degrees of freedom, works similarly to human muscle functions, has high compliance and safety. Besides, Reference [14] developed a cable drive for the exoskeleton, which has seven degrees of freedom and can be used in medical, remote control, test research, and other fields.In general, the structure of the exoskeleton rehabilitation device is simple, but it is challenging to carry out rehabilitation training for the local body parts, and the rehabilitation effect is not ideal [15,16]. The research of the end-effector rehabilitation device is mainly used in limb rehabilitation, remote control, and other fields, and its research direction focuses on improving freedom, ensuring safety and reducing complexity, and so forth. Existing exoskeleton rehabilitation devices are complicated and expensive, and they are mostly used in experiments and difficult to achieve marketization. Both types of rehabilitation devices have their own advantages and disadvantages. It is necessary to select a suitable rehabilitation device type in specific applications and match different training modes to achieve a better training effect [17].The early version in Reference [18] developed by the Federal Institute of Technology in Zurich, was passive gait training. Continuous passive movement (CPM) machines and simple rehabilitation treadmills, which are widely used in clinical practice, generally only provide passive training modes. An ankle rehabilitation robot based on steady-state visual evoked potentials was discussed in Reference [19], which combined SSVEP signals with a virtual reality environment and was able to determine the subject's intention to perform passive training. In addition, for rule-based control structures, a human-machine interface was presented in Reference [20]. The robot manipulator (RM) can pe...

Model based adaptive controller with grasshopper optimization algorithm for upper-limb rehabilitation robot

“…where Pi is a real positive-definite symmetry matrix. When the derivative of 𝑉 𝑖 (𝑒 𝑖 ) is taken with respect time, we obtain as (14), where expressed as (15).…”

“…Ting and Aiguo [14] studied the joint control of the stereotaxic arm for the purpose of robotassisted upper limb rehabilitation therapy exercises by following the required path and making the system stabilize in the rehabilitation. Zhang et al [15] suggest an adaptive fuzzy control scheme to adapt the control input for lower-limb exoskeleton rehabilitation according to the online performance. Abbasimoshaei et al [16] designed an adaptive fuzzy sliding mode controller (AFSMC) to control hand rehabilitation robots.…”

A Novel Human-Like Control Framework for Mobile Medical Service Robot

“…In order to efficiently transfer the trajectory teaching by human demonstration, it is necessary to control the uncertain disturbance in the trajectory tracking process for the mobile robot [29][30][31]. To overcome the hidden safety hazards in scooter operation [32][33][34], a control scheme based on RBFNN was implemented for the elderly walker system. is scheme has certain disturbances and unknown dynamic characteristics.…”

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

“…For example, rehabilitation training robots, boosting exoskeleton robots, and intelligent prosthetics [3][4][5]. But, the sEMG classification is very complex because of its nonlinearities and instability [6].…”