Post-stroke wrist rehabilitation assisted with an intention-driven functional electrical stimulation (FES)-robot system

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users' performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance.

Effects of EMG-based robot for upper extremity rehabilitation on post-stroke patients: a systematic review and meta-analysis

Objective: A growing body of research shows the promise and efficacy of EMG-based robot interventions in improving the motor function in stroke survivors. However, it is still controversial whether the effect of EMG-based robot is more effective than conventional therapies. This study focused on the effects of EMG-based robot on upper limb motor control, spasticity and activity limitation in stroke survivors compared with conventional rehabilitation techniques. Methods: We searched electronic databases for relevant randomized controlled trials. Outcomes included Fugl-Meyer assessment scale (FMA), Modified Ashworth Scale (MAS), and activity level. Result: Thirteen studies with 330 subjects were included. The results showed that the outcomes post intervention was significantly improved in the EMG-based robot group. Results from subgroup analyses further revealed that the efficacy of the treatment was better in patients in the subacute stage, those who received a total treatment time of less than 1000 min, and those who received EMG-based robotic therapy combined with electrical stimulation (ES). Conclusion: The effect of EMG-based robot is superior to conventional therapies in terms of improving upper extremity motor control, spasticity and activity limitation. Further research should explore optimal parameters of EMG-based robot therapy and its long-term effects on upper limb function in post-stroke patients. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/; Identifier: 387070.

Distal versus proximal - an investigation on different supportive strategies by robots for upper limb rehabilitation after stroke: a randomized controlled trial

Background

Different mechanical supporting strategies to the joints in the upper extremity (UE) may lead to varied rehabilitative effects after stroke. This study compared the rehabilitation effectiveness achieved by electromyography (EMG)-driven neuromuscular electrical stimulation (NMES)-robotic systems when supporting to the distal fingers and to the proximal (wrist-elbow) joints.

Methods

Thirty subjects with chronic stroke were randomly assigned to receive motor trainings with NMES-robotic support to the finger joints (hand group, n = 15) and with support to the wrist-elbow joints (sleeve group, n = 15). The training effects were evaluated by the clinical scores of Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), and Modified Ashworth Scale (MAS) before and after the trainings, as well as 3 months later. The cross-session EMG monitoring of EMG activation level and co-contraction index (CI) were also applied to investigate the recovery progress of muscle activations and muscle coordination patterns through the training sessions.

Results

Significant improvements (P < 0.05) in FMA full score, FMA shoulder/elbow (FMA-SE) and ARAT scores were found in both groups, whereas significant improvements (P < 0.05) in FMA wrist/hand (FMA-WH) and MAS scores were only observed in the hand group. Significant decrease of EMG activation levels (P < 0.05) of UE flexors was observed in both groups. Significant decrease in CI values (P < 0.05) was observed in both groups in the muscle pairs of biceps brachii and triceps brachii (BIC&TRI) and the wrist-finger flexors (flexor carpi radialis-flexor digitorum) and TRI (FCR-FD&TRI). The EMG activation levels and CIs of the hand group exhibited faster reductions across the training sessions than the sleeve group (P < 0.05).

Conclusions

Robotic supports to either the distal fingers or the proximal elbow-wrist could achieve motor improvements in UE. The robotic support directly to the distal fingers was more effective than to the proximal parts in improving finger motor functions and in releasing muscle spasticity in the whole UE.

Clinical trial registration

ClinicalTrials.gov, identifier NCT02117089; date of registration: April 10, 2014. https://clinicaltrials.gov/ct2/show/NCT02117089

Early Stroke Rehabilitation of the Upper Limb Assisted with an Electromyography-Driven Neuromuscular Electrical Stimulation-Robotic Arm

Background

Effective poststroke motor rehabilitation depends on repeated limb practice with voluntary efforts. An electromyography (EMG)-driven neuromuscular electrical stimulation (NMES)-robot arm was designed for the multi-joint physical training on the elbow, the wrist, and the fingers.

Objectives

To investigate the training effects of the device-assisted approach on subacute stroke patients and to compare the effects with those achieved by the traditional physical treatments.

Method

This study was a pilot randomized controlled trial with a 3-month follow-up. Subacute stroke participants were randomly assigned into two groups, and then received 20-session upper limb training with the EMG-driven NMES-robotic arm (NMES-robot group, n = 14) or the time-matched traditional therapy (the control, n = 10). For the evaluation of the training effects, clinical assessments including Fugl-Meyer Assessment (FMA), Modified Ashworth Score (MAS), Action Research Arm Test (ARAT), and Function Independence Measurement (FIM) were conducted before, after the rehabilitation training, and 3 months later. Session-by-session EMG parameters in the NMES-robot group, including normalized co-contraction Indexes (CI) and EMG activation level of target muscles, were used to monitor the progress in muscular coordination patterns.

Results

Significant improvements were obtained in FMA (full score and shoulder/elbow), ARAT, and FIM [P < 0.001, effect sizes (EFs) > 0.279] for both groups. Significant improvement in FMA wrist/hand was only observed in the NMES-robot group (P < 0.001, EFs = 0.435) after the treatments. Significant reduction in MAS wrist was observed in the NMES-robot group after the training (P < 0.05, EFs = 0.145) and the effects were maintained for 3 months. MAS scores in the control group were elevated following training (P < 0.05, EFs > 0.24), and remained at an elevated level when assessed 3 months later. The EMG parameters indicated a release of muscle co-contraction in the muscle pairs of biceps brachii and flexor carpi radialis and biceps brachii and triceps brachii, as well as a reduction of muscle activation level in the wrist flexor in the NMES-robot group.

Conclusion

The NMES-robot-assisted training was effective for early stroke upper limb rehabilitation and promoted independence in the daily living comparable to the traditional physical therapy. It could achieve higher motor outcomes at the distal joints and more effective release in muscle tones than the traditional therapy.

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT02117089; date of registration: April 10, 2014.

Review of devices used in neuromuscular electrical stimulation for stroke rehabilitation

Neuromuscular electrical stimulation (NMES), specifically functional electrical stimulation (FES) that compensates for voluntary motion, and therapeutic electrical stimulation (TES) aimed at muscle strengthening and recovery from paralysis are widely used in stroke rehabilitation. The electrical stimulation of muscle contraction should be synchronized with intended motion to restore paralysis. Therefore, NMES devices, which monitor electromyogram (EMG) or electroencephalogram (EEG) changes with motor intention and use them as a trigger, have been developed. Devices that modify the current intensity of NMES, based on EMG or EEG, have also been proposed. Given the diversity in devices and stimulation methods of NMES, the aim of the current review was to introduce some commercial FES and TES devices and application methods, which depend on the condition of the patient with stroke, including the degree of paralysis.

Design of a high voltage stimulator chip for a stroke rehabilitation system

This paper describes the design of an 8-channel high voltage stimulator chip for rehabilitation of stroke patients through surface stimulation, which requires high stimulation currents and high compliance voltage. The chip gets stimulation control data through its Serial Peripheral Interface (SPI), and can accordingly generate biphasic stimulation currents with different amplitudes, duration, frequencies and polarities independently for each channel. The current driver is implemented with thick oxide devices with a supply voltage up to 90V. The chip is designed in a 0.35εm X-FAB high voltage process.

Brain computer interfaces, a review

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.