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The measurement of wrist active range of motion (ROM) is essential for determining the progress of hand functional recovery, which can provide insight into quantitative improvements and enable effective monitoring during hand rehabilitation. Compared with manual methods, which depend on the experience of the therapist, the proposed robot-assisted assessment technique can measure active ROM of human wrists. The robot with a reconfigurable handle design allows for multiple wrist motions. Experiments were conducted with 11 human subjects to measure ROMs of human wrist flexion/extension and radial/ulnar deviation. Reliability analysis was conducted by calculating the intra-class correlation coefficients (ICC), standard error of measurement (SEM) and SEM%. Results showed high reliability (ICC 2,1  ≥ 0.89, SEM ≤ 2.36°, SEM% ≤ 6.81%). Future will focus on adaptive joint self-alignment design between human users and robots to further improve its assessment accuracy., (Copyright © 2019. Published by Elsevier Ltd.)

Interaction control plays an important role in rehabilitation devices to ensure training safety and efficacy. Compliance adaptation of interaction is vital for enabling robot movements to better suit the patient's requirements as human joint characteristics vary. This paper proposes an interactive compliance control scheme on a wrist rehabilitation device (WR e D) for enhanced training safety and efficacy. This control system consists of a low-level trajectory tracking loop and a high-level admittance loop. Experiments were conducted with zero load and human interaction, respectively. Satisfactory trajectory tracking responses were obtained, with the normalized root mean square deviation (NRMSD) values being 1.08% with zero load and the NRMSD values no greater than 1.4% with real-time disturbance and interaction from human users. Results demonstrate that such an interactive compliance control method can adaptively adjust the range of training motions and encourage active engagement from human users simultaneously. These findings suggest that the proposed control method of the WR e D has great potentials for clinical applications due to enhanced training safety and efficacy. Future work will focus on evaluating its efficacy on a large sample of participants.

The measurement of wrist passive ranges of motion (ROMs) can provide insight into improvements and allow for effective monitoring during a rehabilitation program. Compared with conventional methods, this study proposed a new robotic assessment technique for measuring passive ROMs of the wrist. The robotic system has a reconfigurable handle structure that allows for multi-dimensional applications of wrist motions. The assessment reliability of this robotic system was analysed on 11 subjects for measuring wrist extension/flexion and radial/ulnar deviation. Preliminary data demonstrated its potential with intraclass correlation coefficient (ICC 2,1 ) all greater than 0.857 and standard error of measurement (SEM) less than 3.38°. Future work will focus on the standardization of the assessment protocol of this robotic system for assessment purposes, paving the way for its clinical application., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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.

This article explores wide-ranging potential of the wearable ankle robot for in-house rehabilitation. The presented robot has been conceptualized following a brief analysis of the existing technologies, systems, and solutions for in-house physical ankle rehabilitation. Configuration design analysis and component selection for ankle robot have been discussed as part of the conceptual design. The complexities of human robot interaction are closely encountered while maneuvering a rehabilitation robot. We present a fuzzy logic-based controller to perform the required robot-assisted ankle rehabilitation treatment. Designs of visual haptic interfaces have also been discussed, which will make the treatment interesting, and the subject will be motivated to exert more and regain lost functions rapidly. The complex nature of web-based communication between user and remotely sitting physiotherapy staff has also been discussed. A high-level software architecture appended with robot ensures user-friendly operations. This software is made up of three important components: patient-related database, graphical user interface (GUI), and a library of exercises creating virtual reality-specifically developed for ankle rehabilitation.

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.

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. Implications for Rehabilitation Ankle joint functions are restricted/impaired as a consequence of stroke or injury during sports or otherwise. Robots can help in reinstating functions faster and can also work as tool for recording rehabilitation data useful for further analysis. Evolution of ankle robots with respect to their design and control aspects has been discussed in the present paper and a novel design with futuristic control approach has been proposed.

Intervention of robotic devices in the field of physical gait therapy can help in providing repetitive, systematic, and economically viable training sessions. Interactive or assist-as-needed (AAN) gait training encourages patient voluntary participation in the robotic gait training process which may aid in rapid motor function recovery. In this paper, a lightweight robotic gait training orthosis with two actuated and four passive degrees of freedom (DOFs) is proposed. The actuated DOFs were powered by pneumatic muscle actuators. An AAN gait training paradigm based on adaptive impedance control was developed to provide interactive robotic gait training. The proposed adaptive impedance control scheme adapts the robotic assistance according to the disability level and voluntary participation of human subjects. The robotic orthosis was operated in two gait training modes, namely, inactive mode and active mode, to evaluate the performance of the proposed control scheme. The adaptive impedance control scheme was evaluated on ten neurologically intact subjects. The experimental results demonstrate that an increase in voluntary participation of human subjects resulted in a decrease of the robotic assistance and vice versa. Further clinical evaluations with neurologically impaired subjects are required to establish the therapeutic efficacy of the adaptive-impedance-control-based AAN gait training strategy.

Cadence or stride frequency is an important parameter being controlled in gait training of neurologically impaired subjects. The aim of this study was to examine the effects of cadence variation on muscle activation patterns during robot assisted unimpaired gait using dynamic simulations. A twodimensional (2-D) musculoskeletal model of human gait was developed considering eight major muscle groups along with existing ground contact force (GCF) model. A 2-D model of a robotic orthosis was also developed which provides actuation to the hip, knee and ankle joints in the sagittal plane to guide subjects limbs on reference trajectories. A custom inverse dynamics algorithm was used along with a quadratic minimization algorithm to obtain a feasible set of muscle activation patterns. Predicted patterns of muscle activations during slow, natural and fast cadence were compared and the mean muscle activations were found to be increasing with an increase in cadence. The proposed dynamic simulation provide important insight into the muscle activation variations with change in cadence during robot assisted gait and provide the basis for investigating the influence of cadence regulation on neuromuscular parameters of interest during robot assisted gait.

Abstract: Robot assisted gait training may help in producing rapid improvements in functional gait parameters. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis. The newly developed robotic orthosis has 6 degrees of freedom (DOFs). A trajectory tracking controller based on the boundary layer augmented sliding control (BASMC) law was implemented to guide the subject’s limbs on physiological gait trajectories. The compliance of the robotic orthosis sagittal plane hip and knee joints was also controlled, independently of the trajectory tracking control. The robotic orthosis and the control scheme were evaluated on three neurologically intact subjects walking on a treadmill. A maximum trajectory tracking error of 10° was recorded at the hip and knee sagittal plane joints. The results showed that subjects can walk in the robotic orthosis with comfort and the BASMC law was able to guide the subject’s limbs on reference physiological trajectories. [Copyright &y& Elsevier]

Some of the most exciting possibilities for dielectric elastomer artificial muscles consist of biologically inspired networks of smart actuators working towards common goals. However, the creation of these networks will only be realised once intelligence and feedback can be fully distributed throughout an artificial muscle device. Here we show that dielectric elastomer artificial muscles can be built with intrinsic sensor, control, and driver circuitry, bringing them closer in capability to their natural analogues. This was achieved by exploiting the piezoresistive behaviour of the actuator’s highly compliant electrodes using what we have called the dielectric elastomer switch. We developed suitable switching material using carbon loaded silicone grease and experimentally demonstrated the primitives required for self-sensing actuators and digital computation, namely compliant electromechanical NAND gates and oscillator circuits. We anticipate that dielectric elastomer switches will reduce the need for bulky and rigid external circuitry as well as provide the simple distributed intelligence required for soft, biologically inspired networks of actuators. Examples include many-degree-of-freedom robotic hearts, intestines, and manipulators; wearable assistive devices; smart sensor skins and fabrics; and ultimately new types of artificial muscle embedded, electromechanical computers. [ABSTRACT FROM AUTHOR]