Randomized Controlled Trial of Robot-Assisted Gait Training versus Therapist-Assisted Treadmill Gait Training as Add-on Therapy in Early Subacute Stroke Patients: The GAITFAST Study Protocol

Effects and mechanisms of synchronous virtual reality action observation and electrical stimulation on upper extremity motor function and activities of daily living in patients with stroke: a protocol for a randomized controlled trial

Background

Existing rehabilitation techniques are not satisfactory in improving motor function after stroke, resulting in heavy social burdens. With discovery of mirror neuron system (MNS), action observation (AO) has become a promising strategy to promote motor learning in rehabilitation. Based on MNS theory and virtual reality (VR) technology, we designed an innovative rehabilitative approach: synchronous 360° VR video AO (VRAO) and neuromuscular electrical stimulation (NMES). We hypothesized that VRAO+NMES could enhance MNS activation, thus to improve upper limb motor function and activities of daily living in stroke survivors.

Methods

To explore the efficacy and mechanism of VRAO+NMES, we designed this single center, evaluator blinded, prospective, two arm parallel group randomized controlled trial with 1:1 allocation ratio. The experiment group will receive VRAO+NMES, while the control group will receive VR landscape observation combined with NMES. The Fugl-Meyer Assessment for Upper Extremity is the primary outcome of this study, Brunstrom Recovery Stages for Upper Extremity, Manual Muscle Test, Range of Motion, Modified Barthel Index, and Functional Independence Measure are the secondary outcomes. In addition, functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG) will be used to evaluate the activation of MNS brain regions and related muscles, respectively.

Discussion

Applying VR in AO therapy (AOT) has become popular, another study direction to improve AOT is to combine it with peripheral stimulations simultaneously. Due to its full immersive characteristic and multi-sensory input, 360° videos based VRAO+NMES could improve the motivation and engagement level of participants. In addition, fNIRS and sEMG test results may act as good biomarkers to predict rehabilitation outcomes, helping select suitable candidates for this new intervention.

Conclusion

The results of this study will provide evidence for the feasibility and potential clinical efficacy of VRAO+NMES in stroke rehabilitation, thus to promote the clinical applicability and generalize its use in hospital, community, and home rehabilitation settings.

Clinical trial registration

https://www.chictr.org.cn/showproj.html?proj=178276, Identifier [ChiCTR2200063552].

Clinical Feasibility of Applying Immersive Virtual Reality during Robot-Assisted Gait Training for Individuals with Neurological Diseases: A Pilot Study

BACKGROUND

Immersive virtual reality has the potential to motivate and challenge patients who need and want to relearn movements in the process of neurorehabilitation.

OBJECTIVE

The aim of this study was to evaluate the feasibility and user acceptance of an innovative immersive virtual reality system (head-mounted display) used in combination with robot-assisted gait training in subjects suffering from neurological diseases.

METHODS

Fifteen participants suffering from cerebrovascular accident or spinal cord injury completed a single session of immersive virtual reality using a head-mounted display during a Lokomat® gait session. Training parameters and safety indicators were collected, and acceptance was investigated among participants and therapists.

RESULTS

The results suggest that an immersive virtual reality system is feasible in terms of safety and tolerance. Furthermore, the very positive overall acceptance of the system suggests that it has the potential to be included in a robot-assisted gait training session using Lokomat®.

CONCLUSION

Overall, this study demonstrates that a fully immersive virtual reality system based on a head-mounted display is both feasible and well received by cerebrovascular accident and spinal cord injury patients and their therapists during robot-assisted gait training. This study suggests that such a virtual reality system could be a viable alternative to the screen-based training games currently used in neurorehabilitation. It may be especially suitable for enhancing patient motivation and adherence to training, particularly if the application is enjoyable and not mentally taxing.

Gait disorder classification based on effective feature selection and unsupervised methodology

In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients' progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients' progress. The dataset in this study includes 84 measurements of 37 patients based on a physician's opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key-point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator-discriminator networks (ET-GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high-level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi-loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.