Robotic assistance for children with cerebral palsy based on learning from tele-cooperative demonstration

Impedance Variation and Learning Strategies in Human-Robot Interaction

In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.

The Effect of Individual Combination Therapy on Children with Motor Deficits from the Perspective of Comprehensive Rehabilitation

Objective: The paper presents the results of a research survey focused on individual combination therapy in individuals with motor deficits during childhood. The research was carried out in 2020/2021. Two patients were selected based on predetermined relevant criteria and participated in the research survey. Intervention approaches within the research survey were focused on the development of the motor skills of the chosen patients suffering from cerebral palsy or dyspraxia. Furthermore, the patients’ social adaptability was supported together with their independence and self-sufficiency in coping with everyday tasks. Sample: Two patients (N = 2) participated in the research survey based on intended sampling (i.e., motor deficit, age 7–9 years, participation in rehabilitation interventions max. 4 times a month). Based on the predetermined criteria, the two patients were contacted, one of which (N = 1) was diagnosed with cerebral palsy diparesis (ICD-10; G80.1: spastic diplegic cerebral palsy, 8.8 years of age), and the other patient (N = 1) suffered from developmental dyspraxia (ICD-10; F82: a specific developmental disorder of motor functions, 7.4 years of age). The single-case research design method was applied to process the results. This type of qualitative research enabled us to study in detail a small number of participants, specifically in our research (N = 2), one individual patient who suffered from cerebral palsy and the other individual patient diagnosed with developmental dyspraxia. The choice of two individual patients would help us to obtain a better idea of the effect of the chosen combination therapy. A standardized modified FIM test (Functional Independence Measure) was used to present the results. Results: The presented results of the research survey using the single-case research design method point to the following findings. The chosen intervention method using combination therapy demonstrably improved the patients’ conditions within the monitored indicators. When the intervention was omitted and only the usual rehabilitation procedures were conducted, the patients’ conditions deteriorated and decreased to the initial values. Conclusion: Based on the presented results, combination therapy appears to be an effective approach for individuals with motor deficits at a younger school age. The combination of selected rehabilitation approaches using classical procedures as well as robotically assisted therapy is desirable in practice as it meets the requirements for rehabilitation in the 21st century. The survey results offer conclusions and recommendations for practice regarding the research topic.

Assist-As-Needed Control Strategy of Bilateral Upper Limb Rehabilitation Robot Based on GMM

Robotic-assisted rehabilitation therapy has been shown to be effective in improving upper limb motor function and the daily behavior of patients with motor dysfunction. At present, the majority of upper limb rehabilitation robots can only move in the two-dimensional plane, and cannot adjust the assistance mode in real-time according to the patient’s rehabilitation needs. In this paper, according to the shortcomings of the current rehabilitation robot only moving in the two-dimensional plane, a type of bilateral mirror upper limb rehabilitation robot structure with the healthy side assisting the affected side is proposed. This can move in three-dimensional space. Additionally, an assist-as-needed (AAN) control strategy for upper limb rehabilitation training is proposed based on the bilateral upper limb rehabilitation robot. The control strategy adopts Gaussian Mixture Model (GMM) and impedance controller to maximize the patient’s rehabilitation effect. In the task’s design, there is no need to rely on the assistance of the therapist, only the patients who completed the task independently. GMM guides the rehabilitation robot to provide different assistance for the patients at different task stages and induces the patients to complete the rehabilitation training independently by judging the extent to which the patients can complete the task. Furthermore, in this paper, the effectiveness of the proposed control strategy was verified by three volunteers participating in a two-dimensional task. The experimental results show that the proposed AAN control strategy can effectively provide appropriate assistance according to the classification stage of the interaction between the patients and the rehabilitation robot, and thus, patients can better achieve the rehabilitation effect during the rehabilitation task as much as possible.

One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI

This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human-Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed good potential for increasing the extensibility of sign vocabulary in sign language-based human-robot interactions. The expansibility and promising results of the one-shot Learning from Demonstration technique in this study are the main achievements of conducting such machine learning algorithms in social Human-Robot Interaction.

Review of Advanced Medical Telerobots

The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper.

Nine Degree-of-Freedom Kinematic Modeling of the Upper-Limb Complex for Constrained Workspace Evaluation

The design of rehabilitation devices for patients experiencing musculoskeletal disorders (MSDs) requires a great deal of attention. This article aims to develop a comprehensive model of the upper-limb complex to guide the design of robotic rehabilitation devices that prioritize patient safety, while targeting effective rehabilitative treatment. A 9 degree-of-freedom kinematic model of the upper-limb complex is derived to assess the workspace of a constrained arm as an evaluation method of such devices. Through a novel differential inverse kinematic method accounting for constraints on all joints1820, the model determines the workspaces in which a patient is able to perform rehabilitative tasks and those regions where the patient needs assistance due to joint range limitations resulting from an MSD. Constraints are imposed on each joint by mapping the joint angles to saturation functions, whose joint-space derivative near the physical limitation angles approaches zero. The model Jacobian is reevaluated based on the nonlinearly mapped joint angles, providing a means of compensating for redundancy while guaranteeing feasible inverse kinematic solutions. The method is validated in three scenarios with different constraints on the elbow and palm orientations. By measuring the lengths of arm segments and the range of motion for each joint, the total workspace of a patient experiencing an upper-limb MSD can be compared to a preinjured state. This method determines the locations in which a rehabilitation device must provide assistance to facilitate movement within reachable space that is limited by any joint restrictions resulting from MSDs.

A Novel Robust Model Reference Adaptive Impedance Control Scheme for an Active Transtibial Prosthesis

In this paper, a novel robust model reference adaptive impedance control (RMRAIC) scheme is presented for an active transtibial ankle prosthesis. The controller makes the closed loop dynamics of the prosthesis similar to a reference impedance model and provides asymptotic tracking of the response trajectory of this impedance model. The interactions between human and prosthesis are taken into account by designing a second-order reference impedance model. The proposed controller is robust against parametric uncertainties in the nonlinear dynamic model of the prosthesis. Also, the controller has robustness against bounded uncertainties due to unavailable ground reaction forces and unmeasurable feedbacks of accelerations at the socket place. Moreover, an appropriate Series Elastic Actuator (SEA) mechanism for the prosthetic ankle is included in this work and its effects are discussed. Tracking performance and stability of the closed-loop system are proven via the Lyapunov stability analysis. Using simulations on an overall amputee prosthetic foot system, the effectiveness of the proposed RMRAIC controller is investigated for the task of level ground walking.

Supporting Play by Applying Haptic Guidance Along a Surface Learnt from Single Motion Trajectories

Haptic-enabled teleoperated robots can help children with physical disabilities to reach toys by applying haptic guidance towards their toys, thus compensating for their limitations in reaching and manipulating objects. In this article we preliminarily tested a learning from demonstration (LfD) approach, where a robotic system learnt the surface that best approximated to all motion trajectories demonstrated by the participants while playing a whack-a-mole game. The end-goal of the system is for therapists or parents to demonstrate to it how to play a game, and then be used by children with physical disabilities. In this study, four adults without disabilities participated, to identify aspects that will be necessary to improve before conducting trials with children. During the demonstration phase, participants played the game in normal teleoperation, assuming the role of the therapist/parent. Then, the surface was modeled using a neural network. Participants played the game without and with the haptic guidance. The movements of the robotic system were mirrored to induce errors in movements, and thus require the guidance. Participants spent more time, moved the robot longer distances, and had jerkier movements when they played the game with the guidance than without it. Possible reasons were discussed, and several solutions were proposed to improve the system. The main contribution of this paper was the learning of a surface instead of learning a single motion trajectory.

Robotic learning from demonstration of therapist's time-varying assistance to a patient in trajectory-following tasks

The number of people with physical disabilities and impaired motion control is increasing. Consequently, there is a growing demand for intelligent assistive robotic systems to cooperate with people with disability and help them carry out different tasks. To this end, our group has pioneered the use of robot learning from demonstration (RLfD) techniques, which eliminate the need for task-specific robot programming, in robotic rehabilitation and assistive technologies settings. First, in the demonstration phase, the therapist (or in general, a helper) provides an intervention (typically assistance) and cooperatively performs a task with a patient several times. The demonstrated motion is modelled by a statistical RLfD algorithm, which will later be used in the robot controllers to reproduce a similar intervention robotically. In this paper, by proposing a Tangential-Normal Varying-Impedance Controller (TNVIC), the robotic manipulator not only follows the therapist's demonstrated motion, but also mimics his/her interaction impedance during the therapeutic/assistive intervention. The feasibility and efficacy of the proposed framework are evaluated by conducting an experiment involving a healthy adult with cerebral palsy symptoms being induced using transcutaneous electrical nerve stimulation.