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Objective: This review aims to provide a systematical investigation of clinical effectiveness of active training strategies applied in platform-based ankle robots., Method: English-language studies published from Jan 1980 to Aug 2017 were searched from four databases using key words of "Ankle ∗ " AND "Robot ∗ " AND "Effect ∗ OR Improv ∗ OR Increas ∗ ." Following an initial screening, three rounds of discrimination were successively conducted based on the title, the abstract, and the full paper., Result: A total of 21 studies were selected with 311 patients involved; of them, 13 studies applied a single group while another eight studies used different groups for comparison to verify the therapeutic effect. Virtual-reality (VR) game training was applied in 19 studies, while two studies used proprioceptive neuromuscular facilitation (PNF) training., Conclusion: Active training techniques delivered by platform ankle rehabilitation robots have been demonstrated with great potential for clinical applications. Training strategies are mostly combined with one another by considering rehabilitation schemes and motion ability of ankle joints. VR game environment has been commonly used with active ankle training. Bioelectrical signals integrated with VR game training can implement intelligent identification of movement intention and assessment. These further provide the foundation for advanced interactive training strategies that can lead to enhanced training safety and confidence for patients and better treatment efficacy.

A rehabilitation robot plays an important role in relieving the therapists' burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles' good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM's nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions. [ABSTRACT FROM AUTHOR]

Background: Robot-assisted ankle assessment could potentially be conducted using sensor-based and model-based methods. Existing ankle rehabilitation robots usually use torquemeters and multiaxis load cells for measuring joint dynamics. These measurements are accurate, but the contribution as a result of muscles and ligaments is not taken into account. Some computational ankle models have been developed to evaluate ligament strain and joint torque. These models do not include muscles and, thus, are not suitable for an overall ankle assessment in robot-assisted therapy. Methods: This study proposed a computational ankle model for use in robot-assisted therapy with three rotational degrees of freedom, 12 muscles, and seven ligaments. This model is driven by robotics, uses three independent position variables as inputs, and outputs an overall ankle assessment. Subject-specific adaptations by geometric and strength scaling were also made to allow for a universal model. Results: This model was evaluated using published results and experimental data from 11 participants. Results show a high accuracy in the evaluation of ligament neutral length and passive joint torque. The subject-specific adaptation performance is high, with each normalized root-mean-square deviation value less than 10%. Conclusion: This model could be used for ankle assessment, especially in evaluating passive ankle torque, for a specific individual. The characteristic that is unique to this model is the use of three independent position variables that can be measured in real time as inputs, which makes it advantageous over other models when combined with robot-assisted therapy. [ABSTRACT FROM AUTHOR]

Ankle rehabilitation robots can play an important role in improving outcomes of the rehabilitation treatment by assisting therapists and patients in number of ways. Consequently, few robot designs have been proposed by researchers which fall under either of the two categories, namely, wearable robots or platform-based robots. This paper presents a review of both kinds of ankle robots along with a brief analysis of their design, actuation and control approaches. While reviewing these designs it was observed that most of them are undesirably inspired by industrial robot designs. Taking note of the design concerns of current ankle robots, few improvements in the ankle robot designs have also been suggested. Conventional position control or force control approaches, being used in the existing ankle robots, have been reviewed. Apparently, opportunities of improvement also exist in the actuation as well as control of ankle robots. Subsequently, a discussion on most recent research in the development of novel actuators and advanced controllers based on appropriate physical and cognitive human-robot interaction has also been included in this review. [ABSTRACT FROM AUTHOR]