The Combined Effects of Adaptive Control and Virtual Reality on Robot-Assisted Fine Hand Motion Rehabilitation in Chronic Stroke Patients: A Case Study

https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.08.027Get rights and content

Robot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. However, the lack of fully adaptive assist-as-needed control of the robotic devices and an inadequate immersive virtual environment that can promote active participation during training are obstacles hindering the achievement of better training results with fewer training sessions required. This study thus focuses on these research gaps by combining these 2 key components into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The effectiveness of the proposed system is tested by conducting clinical trials on a chronic stroke patient and verified through clinical evaluation methods by measuring the key kinematic features such as active range of motion (ROM), finger strength, and velocity. By comparing the pretraining and post-training results, the study demonstrates that the proposed method can further enhance the effectiveness of fine hand motion rehabilitation training by improving finger ROM, strength, and coordination.

Introduction

Stroke can result in paralysis, speech impairment, loss of memory and reasoning ability, coma, or even death.1 Complete or partial loss of movability in an upper extremity (UE) is the most commonly reported impairment after suffering a stroke, which can hinder the performance of activities of daily living (ADLs) and significantly undermine the quality of life (QOL) of the stroke patient.2 Recent neuroplasticity studies indicate that a highly repetitive and task-specific training may induce changes in the brain, and this response can be optimized if the task is challenging enough.3 This approach holds promise for the improvement or even recovery of the affected motion skills. However, conventional therapies (CTs) such as physical and occupational therapy can be time-consuming and labor intensive, requiring long training sessions and incurring high costs. Moreover, it is difficult to effectively evaluate the effectiveness of CTs quantitatively and objectively. Robot-assisted therapy (RT)4 can provide a highly repetitive movement training and overcome the shortcomings of CTs through the reduction of associated labor and the offer of an accurate, automated movement control and the quantification of rehabilitation performance.5

Multisensory feedback has been proven to be critical in re-establishing the neural pathways damaged by stroke and closing the sensor motor loop.6 Virtual reality (VR) technology has recently been widely used in the field of augmented stroke rehabilitation. VR allows users to interact with a multisensory simulated environment and receive real-time feedback on performance, thus allowing patients to correct their motion. This, in turn, can facilitate repetition, intensity, and task-oriented training, all of which promote voluntary active motion. Therefore, VR-based RT offers the potential to specifically promote and/or enhance functional movement recovery.7

VR systems can provide safe, ecological, and individualized 3-dimensional environments where patients can perform specific actions to achieve a goal. Another advantage of a VR intervention is that patients can perceive such interventions as enjoyable exercise games rather than treatment methods and, thereby, increase motivation and treatment compliance.8 VR can also be used as an assessment method by recording and objectively measuring the performance of patients and their behavioral responses within the virtual world. VR-based RT thus has the potential to utilize motor learning principles in relation to task-oriented training. However, the intensity and dose–response aspects of a VR game–based intervention training with evidence-based efficacy and clear objectives and outcomes need to be further determined.9

The aim of this study was to further evaluate the effectiveness of the developed VR-based RT treatments with an adaptive control method deployed in a robotic training device. In addition, the study explores more effective methods to transform the achieved improvements to the performance of ADL tasks in real life. Special attention is paid to fine hand motion rehabilitation, which involves small, precise, and coordinated movements of the fingers and requires the integration of muscular, skeletal, and neurological functions. This is because fine hand motion is vital for more delicate ADL tasks such as eating, drinking, and personal hygiene and is necessary for the improvement of the QOL of stroke patients.

Section snippets

RT Platform

In this study, a 5 degrees of freedom hand rehabilitation robotic device named Amadeo (Tyromotion GmbH, Graz, Austria)10, 11 was used as the experimental platform. Amadeo (Tyromotion GmbH) can provide position-based passive and active assistive training modes that emphasize the flexion and extension of each finger. The moving finger slides are attached to the fingers using a small magnetic disc and an adhesive tape for connection to the robot. The slides then transfer, bend, or stretch

VR-Based RGS and Transferring Environment

To make the training less dull and monotonous as well as to motivate the patient's voluntary and active participation in the training, 2 interactive VR-based RGS and 1 transferring virtual environment (VE) were designed. The customized training program was developed with close collaboration between clinicians and developers. When implemented on Amadeo (Tyromotion GmbH), the highly repetitive but functional tasks serve as an enriched motivating environment with challenging and customizable

Results and Analysis

A number of parameters associated with fine hand motion rehabilitation including spasticity, reflexes, level of voluntary control, and function movement were evaluated. Kinematic measurements, including hand movement speed and movement duration, were calculated using data collected by the robot.

Discussions and Conclusions

In this study the advantages of force-rendered AAN adaptive control,15 VR8-based RGS,16 and transferring VE17 were combined with the aim of optimizing the training effectiveness and enhancing the rehabilitation efficiency. The chronic stroke subject who underwent the proposed rehabilitation approach showed improvement in clinical evaluation methods using the FMA and the MAS, as well as kinematic characteristics suggested by active ROM and output force intensity. The result of this study was

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