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Soft Ankle Rehabilitation Robot


Pneumatic muscle actuators (PMAs) possess advantages in terms of light weight, inherent compliance and high power/weight ratio. This research focuses on the development of a soft ankle rehabilitation robot and advanced human-robot interaction control strategies. Current work involves the development of a wearable, parallel robot driven by PMAs that can be used to assist therapists in the treatment of a variety of ankle injuries. Our motivation is to develop a “human-centered” physiological controller to incorporate human intention indicated by interaction or bio-signals for controlling the human-inspired rehabilitation robots.


The current prototype of the soft ankle rehabilitation robot was developed by the University of Auckland. This robot is powered by four PMAs equipped in parallel to realize three DOFs for ankle rotational movement, that is, dorsiflexion/plantarflexion (DP), inversion/eversion (IE), and adduction/abduction (AA), respectively. FESTO™ pneumatic muscles are adopted to guarantee the intrinsic compliance and force generation ability of the robot during operation. Four high dynamic pneumatic regulators are used to regulate the pressure inside each muscle. The end-effector is a three-link serial structure with magnetic encoder installed in each joint to measure the angular positions in Euler X (DP), Y(IE) and Z (AA) axes of the robot end-effector. Joint encoders are used to measure the ankle rotary displacements which can be used to calculate the lengths of PMAs by inverse kinematics. The robot frame supporting the weight of the robot and human shank and ankle is adjustable to suit different patients. A force sensor is placed in series with each PMA to test the pulling force and a six-axis load cell is mounted between the human and robot to measure the interaction torque.

We proposed a new adaptive patient-cooperative control strategy for improving the effectiveness and safety of robot-assisted ankle rehabilitation. The control scheme consists of a position controller implemented in joint space and a high-level admittance controller in task space. The admittance controller adaptively modifies the predefined trajectory based on real-time ankle measurement, which enhances the training safety of the robot. Three training modes include: 1) a passive mode using a joint-space position controller, 2) a patient–robot cooperative mode using a fixed-parameter admittance controller, and 3) a cooperative mode using a variable-parameter admittance controller. Different from rigid robots that can only provide “virtual” compliance when interacting with the environment, the PMAs-driven ones can provide variable inherent compliance during operations, which is able to make the robot being “real” soft. Thus, a new hierarchical compliance control structure including a low-level compliance adjustment controller in joint space and a high-level admittance controller in task space is designed. An adaptive compliance control paradigm is further developed by taking into account patient’s active contribution and movement ability during a previous period of time, in order to provide robot assistance only when it is necessarily required.


In our future work, we will design and develop a new ankle exoskeleton to assess patient’s sprain and assist/restore the ankle-foot functional ability. For the purpose of ankle sprain assessment and treatment, the robot will provide the identified treatment needs in terms of safety and effectiveness of treatment. Biomechanics techniques such as EMG-based musculoskeletal model, muscle force estimation and recognition will also be developed for physiological controller design; and integrate physiological interface and visual reality games to facilitate the robot-assisted ankle rehabilitation in a natural and interesting manner.


The impact of our work has been recognized by international academic and industrial experts: our orthotics won the first prize in the 2015 “Win-in-Suzhou” Competition in Australia and the Australia Rehabilitation Robotics competition in 2016.

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