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Over the past decade, rehabilitative devices have undergone significant advancements. Orthoses, which are assistive limb devices, have transitioned from passive supportive devices to active or semiactive devices with adjustable damping. Similarly, prosthetic limb-replacement devices for amputees have shifted towards the integration of semiactive dampers instead of passive or bulky dampers. The implementation of semiactive damping is achieved through the use of smart magnetorheological (MR) fluids that can modify their viscosity based on the input current, resulting in reduced power consumption. This article focuses mainly on modelling, sensors and control strategies in MR damper-based rehabilitative devices since these areas contribute to the development of the overall end product. There have been notable improvements in the modelling of human knee joints and the damping system components, aiming to achieve more efficient damping control. Traditional mathematical equations, such as Lagrangian and Newtonian formulations, have been supplemented with machine learning algorithms. Additionally, the utilization of various sensor combinations to measure knee/ankle joint angles has advanced. These sensors range from basic mechanical sensors to wireless inertial and piezoelectric sensors, enabling faster and more diverse communication. Furthermore, control algorithms have also witnessed a progression from classical control approaches to more sophisticated strategies such as fuzzy control and neural network controllers. These advanced control algorithms enhance the overall performance and responsiveness of the rehabilitative devices. However, there are certain disadvantages found in MR fluids, sensors, modelling and control algorithms that are discussed further in this review article. This review explores the developments in different rehabilitative devices that integrate modelling, sensors, and control designs to achieve optimal and efficient outcomes.

Review of the evolution of magnetorheological fluid-based rehabilitative devices: from the perspective of modelling, sensors and control strategies