Effectiveness of robot-assisted therapy on ankle rehabilitation--a systematic review.

OBJECTIVE: The aim of this study was to provide a systematic review of studies that investigated the effectiveness of robot-assisted therapy on ankle motor and function recovery from musculoskeletal or neurologic ankle injuries. METHODS: Thirteen electronic databases of articles published from January, 1980 to June, 2012 were searched using keywords 'ankle*', 'robot*', 'rehabilitat*' or 'treat*' and a free search in Google Scholar based on effects of ankle rehabilitation robots was also conducted. References listed in relevant publications were further screened. Eventually, twenty-nine articles were selected for review and they focused on effects of robot-assisted ankle rehabilitation. RESULTS: Twenty-nine studies met the inclusion criteria and a total of 164 patients and 24 healthy subjects participated in these trials. Ankle performance and gait function were the main outcome measures used to assess the therapeutic effects of robot-assisted ankle rehabilitation. The protocols and therapy treatments were varied, which made comparison among different studies difficult or impossible. Few comparative trials were conducted among different devices or control strategies. Moreover, the majority of study designs met levels of evidence that were no higher than American Academy for Cerebral Palsy (CP) and Developmental Medicine (AACPDM) level IV. Only one study used a Randomized Control Trial (RCT) approach with the evidence level being II. CONCLUSION: All the selected studies showed improvements in terms of ankle performance or gait function after a period of robot-assisted ankle rehabilitation training. The most effective robot-assisted intervention cannot be determined due to the lack of universal evaluation criteria for various devices and control strategies. Future research into the effects of robot-assisted ankle rehabilitation should be carried out based on universal evaluation criteria, which could determine the most effective method of intervention. It is also essential to conduct trials to analyse the differences among different devices or control strategies.

Development of a biological signal-based evaluator for robot-assisted upper-limb rehabilitation: a pilot study.

Bio-signal based assessment for upper-limb functions is an attractive technology for rehabilitation. In this work, an upper-limb function evaluator is developed based on biological signals, which could be used for selecting different robotic training protocols. Interaction force (IF) and participation level (PL, processed surface electromyography (sEMG) signals) are used as the key bio-signal inputs for the evaluator. Accordingly, a robot-based standardized performance testing (SPT) is developed to measure these key bio-signal data. Moreover, fuzzy logic is used to regulate biological signals, and a rules-based selector is then developed to select different training protocols. To the authors' knowledge, studies focused on biological signal-based evaluator for selecting robotic training protocols, especially for robot-based bilateral rehabilitation, has not yet been reported in literature. The implementation of SPT and fuzzy logic to measure and process key bio-signal data with a rehabilitation robot system is the first of its kind. Five healthy participants were then recruited to test the performance of the SPT, fuzzy logic and evaluator in three different conditions (tasks). The results show: (1) the developed SPT has an ability to measure precise bio-signal data from participants; (2) the utilized fuzzy logic has an ability to process the measured data with the accuracy of 86.7% and 100% for the IF and PL respectively; and (3) the proposed evaluator has an ability to distinguish the intensity of biological signals and thus to select different robotic training protocols. The results from the proposed evaluator, and biological signals measured from healthy people could also be used to standardize the criteria to assess the results of stroke patients later.

A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot.

OBJECTIVE: This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility. METHOD: This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation. RESULT: All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5 bits/min when the biggest one of the robots for this proposed training is set as 24 bits/min. CONCLUSION: The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.

Towards compliant and wearable robotic orthoses: A review of current and emerging actuator technologies.

Robotic orthoses, or exoskeletons, have the potential to provide effective rehabilitation while overcoming the availability and cost constraints of therapists. However, current orthosis actuation systems use components designed for industrial applications, not specifically for interacting with humans. This can limit orthoses' capabilities and, if their users' needs are not adequately considered, contribute to their abandonment. Here, a user centered review is presented on: requirements for orthosis actuators; the electric, hydraulic, and pneumatic actuators currently used in orthoses and their advantages and limitations; the potential of new actuator technologies, including smart materials, to actuate orthoses; and the future of orthosis actuator research.

An in-vivo lateral ankle ligament strain behavior assessment technique for potential use in robot-assisted therapy.

Ankle sprains are very common, especially in sports activities. Accurate assessment of ankle ligament strain behavior is crucial in understanding ankle function and optimizing ankle rehabilitation programs. This study proposed an in-vivo lateral ankle ligament strain assessment technique for potential use in robot-assisted therapy. It consists of two phases: real-time identification of ankle joint and subtalar joint orientations and simulation of lateral ankle ligament strain behavior. A healthy participant conducted robot-assisted rehabilitation exercises and the results compared to a kinematic model. The model was found to be realistic, leading to the conclusion that this method may be appropriate for determining lateral ankle ligament strain in robot-assisted therapy.

Reviewing effectiveness of ankle assessment techniques for use in robot-assisted therapy.

This article provides a comprehensive review of studies that investigated ankle assessment techniques to better understand those that can be used in the real-time monitoring of rehabilitation progress for implementation in conjunction with robot-assisted therapy. Seventy-six publications published between January 1980 and August 2013 were selected based on eight databases. They were divided into two main categories (16 qualitative and 60 quantitative studies): 13 goniometer studies, 18 dynamometer studies, and 29 studies about innovative techniques. A total of 465 subjects participated in the 29 quantitative studies of innovative measurement techniques that may potentially be integrated in a real-time monitoring device, of which 19 studies included less than 10 participants. Results show that qualitative ankle assessment methods are not suitable for real-time monitoring in robot-assisted therapy, though they are reliable for certain patients, while the quantitative methods show great potential. The majority of quantitative techniques are reliable in measuring ankle kinematics and kinetics but are usually available only for use in the sagittal plane. Limited studies determine kinematics and kinetics in all three planes (sagittal, transverse, and frontal) where motions of the ankle joint and the subtalar joint actually occur.

Neuromuscular interfacing: a novel approach to EMG-driven multiple DOF physiological models.

This paper presents a novel approach that involves first identifying and verifying the available superficial muscles that can be recorded by surface electromyography (EMG) signals, and then developing a musculoskeletal model based on these findings, which have specifically independent DOFs for movement. Such independently controlled multiple DOF EMG-driven models have not been previously developed and a two DOF model for the masticatory system was achieved by implementing independent antagonist muscle combinations for vertical and lateral movements of the jaw. The model has six channels of EMG signals from the bilateral temporalis, masseter and digastric muscles to predict the motion of the mandible. This can be used in a neuromuscular interface to manipulate a jaw exoskeleton for rehabilitation. For a range of different complexities of jaw movements, the presented model is able to consistently identify movements with 0.28 - 0.46 average normalized RMSE. The results demonstrate the feasibility of the approach at determining complex multiple DOF movements and its applicability to any joint system.

Neuromuscular interfacing: establishing an EMG-driven model for the human elbow joint.

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.