Journal of NeuroEngineering and Rehabilitation最新文献

Background: Head-mounted displays can be used to offer personalized immersive virtual reality (IVR) training for patients who have suffered an Acquired Brain Injury (ABI) by tailoring the complexity of visual and auditory stimuli to the patient's cognitive capabilities. However, it is still an open question how these virtual environments should be designed.

Methods: We used a human-centered design approach to help define the characteristics of suitable virtual training environments for ABI patients. We conducted (i) observations, (ii) interviews with eleven neurorehabilitation experts, and (iii) an online questionnaire with 24 neurorehabilitation experts to examine how therapists modify current training environments to promote patients' recovery in conventional sensorimotor neurorehabilitation settings. Finally, (iv) we involved eight neurorehabilitation experts in a participatory design workshop to co-create examples of IVR training environments.

Results: Five phases of the recovery process (Screening, Planning, Training, Reflecting, and Discharging) and six key themes describing the characteristics of suitable (physical) training environments (Specific, Meaningful, Versatile, Educational, Safe, and Supportive) were identified. The experts agreed that modulating the number of elements (e.g., objects, people) or distractions (e.g., background noise) in the physical training environment enables therapists to provide their patients with suitable conditions to execute functional tasks. Additionally, the experts highlighted the importance of developing IVR training environments that are meaningful and realistic.

Conclusions: Through consultations with neurorehabilitation experts, we gained insights into how therapists adjust physical training environments to promote the execution of functional sensorimotor tasks in patients with diverse cognitive capabilities. Their recommendations on how to modulate and make IVR environments meaningful may contribute to increased motivation and skill transfer. Future studies on IVR-based neurorehabilitation should involve patients themselves.

Background: Myoelectric pattern recognition (MPR) combines multiple surface electromyography channels with a machine learning algorithm to decode motor intention with an aim to enhance upper limb function after stroke. This study aims to determine the feasibility and preliminary effectiveness of a novel intervention combining MPR, virtual reality (VR), and serious gaming to improve upper limb function in people with chronic stroke.

Methods: In this single case experimental A-B-A design study, six individuals with chronic stroke and moderate to severe upper limb impairment completed 18, 2 h sessions, 3 times a week. Repeated assessments were performed using the Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), grip strength, and kinematics of the drinking task at baseline, during, and post intervention. The results were analyzed by using visual analysis and Tau-U statistics.

Results: All participants improved upper limb function assessed by FMA-UE (Tau-U 0.72-1.0), and five out of six improved beyond the minimal clinical important difference (MCID). Four participants improved ARAT and grip strength scores (Tau-U 0.84-1.0), with one reaching the MCID for ARAT. Three out of four participants in the kinematic analysis achieved improvements beyond the MCID in movement time and smoothness, two with a Tau-U > 0.90, and two participants improved trunk displacement beyond the MCID (Tau-U 0.68). Most participants showed some deterioration in the follow-up phase.

Conclusions: MPR combined with VR and serious gaming is a feasible and promising intervention for improving upper limb function in people with chronic stroke.

Trial registration: ClinicalTrials.gov, reference number NCT04154371.

Background: Treadmill-based gait training is part of rehabilitation programs focused on walking abilities. The use of handrails embedded in treadmill systems is debated, and current literature only explores the issue from a behavioral perspective.

Methods: We examined the cortical correlates of treadmill walking in healthy participants using functional near-infrared spectroscopy. We investigated whether the utilization of treadmill handrails at varying walking speeds could affect cortical activation associated with the task, and we evaluated potential differences in task-based functional connectivity across the various walking conditions.

Results: Significant differences in cortical activation were found between the two walking speeds (3 and 5 km/h) in the unsupported condition; these differences were reduced when using the handrails. Specifically, cortical activation was significantly higher when the participants swung their arms freely while walking at a speed of 5 compared to 3 km/h in several Brodmann's Areas (BA): left BA10, BA3 and BA39, and right BA10, BA9, BA8, BA3, and BA40. No significant differences were found when participants were holding onto the handrails. A significant difference was found in the left BA40 between the two speeds, regardless of whether the participants were holding onto the handrails. Furthermore, at the higher speed and without the use of handrails, a wider pattern of task-based functional connectivity was observed, with significantly stronger connectivity between the left BA10 and BA40.

Conclusions: We suggest that speed and handrails use play a role in walking cortical activity patterns, therefore they are key ingredients to take into account when planning a rehabilitation program.

Background: Motion complexity is necessary for adapting to external changes, but little is known about trunk motion complexity during seated perturbation in individuals with spinal cord injury (SCI). We aimed to investigate changes following SCI in trunk segmental motion complexity across different perturbation directions and how they affect postural control ability in individuals with SCI.

Methods: A total of 17 individuals with SCI and 18 healthy controls participated in challenging sagittal-seated perturbations with hand protection. Upper arm activation was measured using surface electromyography for trial consistency. Motion complexity parameters, quantified across three degrees of freedom, was assessed using relative angular acceleration from six trunk segments obtained through motion capturing system. Motion performance parameters were assessed using center of pressure (CoP) measurements from a force plate, including settling time, maximum CoP displacement (MD) variability, and steady-state error. Statistical analyses examined group and direction differences, while complexity-performance relationships were evaluated using multiple response least partial squares regression.

Results: Compared to healthy controls, individuals with SCI showed significantly lower motion complexity in the lumbar and upper thoracic segments (approximately10% - 20%), with identical settling time and higher MD variability. Backward perturbations, as opposed to forward perturbations, resulted in reduced complexity in the aforementioned segments and increased steady-state error. Lower lumbar rotation complexity negatively correlated with MD variability (β = -0.240) and steady-state error (β = -0.485) in individuals with SCI, while showing a minor positive correlation with settling time (β = 0.152) during backward perturbation.

Conclusion: Simplified motion control in individuals with SCI may arise from uncoordinated lumbar and overactive thoracic neuromuscular control, compromising stability despite maintaining speed. Increasing lumbar motion complexity could enhance postural stability and accuracy during backward perturbation, representing a potential target for developing seated balance rehabilitation strategies and promoting more adaptive trunk control.

Background: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.

Methods: This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.

Results: Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference = 0.52, 95% CI = 0.03-1.00, p = 0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p = 0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional connectivity.

Conclusions: BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.

Trial registration: CRIS (KCT0009013).

Background: Despite the reported efficacy of overground robotic exoskeleton (ORE) for rehabilitation of mobility post-stroke, its effectiveness in real-world practice is still debated. We analysed prospectively collected data from Improving Mobility Via Exoskeleton (IMOVE), a multicentre clinical implementation programme of ORE enrolling participants with various neurological conditions and were given options to choose between 12 sessions of ORE or conventional therapy (control).

Methods: This is analysis of participants under IMOVE who fulfilled the following criteria (i) primary diagnosis was stroke (ischemic, hemorrhagic; first or recurrent), (ii) onset of stroke was within 9 months and (iii) the intervention was during inpatient stay. They should also fulfill the general IMOVE inclusion and exclusion criteria which were resembling general clinical and manufacturing criteria of ORE. Outcome measures included Functional Ambulatory Category (FAC), Rivermead Mobility Index (RMI), Functional Independence Measure (FIM) and Clinical Outcome Variable Scale (COVS), measured immediately before and after the 12 sessions of therapy, and mean distance walked per session.

Results: Of 149 participants (105 OREs and 44 controls), both groups improved significantly in motor outcomes with no significant between-group differences. Participants with baseline FAC 1 had significantly greater improvement in motor sub-score of FIM (FIM-motor) compared to controls (mean difference 8.4, 95% CI 0.65-16.07, η_p² = 0.136, p = 0.034). The mean distance walked per session for ORE group was almost three times that of control for those with baseline FAC 0 (121.5 [SD 31.1]m vs 35.0 [SD 41.0]m, 95% CI 62.2-110.9, d = 2.54 p < 0.001) and FAC 1 (145.8 [SD 31.6]m vs 52.2 [SD 42.5]m, 95% CI 61.8-125.2, d = 2.71, p < 0.001). The difference was not observed for FAC 2 to 3 (162.9 [SD 29.2]m vs 134.2 [SD 87.5]m, 95% CI -22.2 to 79.7, d = 0.41, p = 0.252).

Conclusion: In a pragmatic setting, use of ORE for gait training enabled patients with lower ambulatory capacity to walk longer distances during therapy sessions. Patients who required continuous assistance during ambulation (FAC 1) had significantly better gains in FIM-motor compared to conventional therapy, suggesting possible benefit of ORE for this group.

Trial registration: The trial was registered with clinicaltrials.gov (NCT05659121) on April 14, 2022.

Background: There is growing interest in use of transcutaneous spinal stimulation (TSS) for people with neurologic conditions both to augment volitional control (by facilitating motoneuron excitability), and to decrease spasticity (by activating inhibitory networks). Various electrode montages are used during TSS, with little understanding of how electrode position influences spinal circuit activation. We sought to identify the thoracolumbar electrode montage associated with the most robust activation of spinal circuits by comparing posterior root-muscle reflexes (PRM reflexes) elicited by 6 montages. Additionally, we assessed tolerability of the stimulation during PRM reflex testing.

Methods: Fifteen adults with intact neurological systems participated in this randomized crossover study. PRM reflexes were evoked transcutaneously using electrode montages with dorsal-ventral (DV) or dorsal-midline (DM) current flow. DV montages included: [1] cathode over T11/T12, anodes over iliac crests (DV-I), [2] cathode over T11/T12, anodes over umbilicus (DV-U), [3] dual paraspinal cathodes at T11/12, anodes over iliac crests (DV-PI), and [4] dual paraspinal cathodes at T11/12, anodes over umbilicus (DV-PU). DM montages included: [5] cathode over T11/12, anode 5 cm caudal (DM-C), and [6] cathode over T11/12, anode 5 cm rostral (DM-R). PRM reflex recruitment curves were obtained in the soleus muscle of both lower extremities.

Results: Lower reflex thresholds (mA) for dominant (D) and nondominant (ND) soleus muscles were elicited in DV-U (D: 46.7[33.9, 59.4], ND: 45.4[32.5, 58.2]) and DV-I (D: 48.1[35.3, 60.8], ND: 45.4[32.5, 58.2]) montages compared to DV-PU (D: 64.3[51.4, 77.1], ND:61.7[48.8, 74.6]), DV-PI (D:64.9[52.1, 77.7], ND:61.4[48.5, 75.5]), DM-C(D:60.0[46.9, 73.1], ND:63.6[50.8, 76.5]), and DM-R(D:63.1[50.3, 76.0], ND:62.6[49.8, 75.5]). DV-U and DV-I montages demonstrated larger recruitment curve area than other montages. There were no differences in response amplitude at 120% of RT(1.2xRT) or tolerability among montages.

Conclusions: Differences in spinal circuit recruitment are reflected in the response amplitude of the PRM reflexes. DV-I and DV-U montages were associated with lower reflex thresholds, indicating that motor responses can be evoked with lower stimulation intensity. DV-I and DV-U montages therefore have the potential for lower and more tolerable interventional stimulation intensities. Our findings optimize electrode placement for interventional TSS and PRM reflex assessments.

Clinical trial number: NCT04243044.

For surface electromyography (sEMG) based human-machine interaction systems, accurately recognizing the users' gesture intent is crucial. However, due to the existence of subject-specific components in sEMG signals, subject-specific models may deteriorate when applied to new users. In this study, we hypothesize that in addition to subject-specific components, sEMG signals also contain pattern-specific components, which is independent of individuals and solely related to gesture patterns. Based on this hypothesis, we disentangled these two components from sEMG signals with an auto-encoder and applied the pattern-specific components to establish a general gesture recognition model in cross-subject scenarios. Furthermore, we compared the characteristics of the pattern-specific information contained in three categories of EMG measures: signal waveform, time-domain features, and frequency-domain features. Our hypothesis was validated on an open source database. Ultimately, the combination of time- and frequency-domain features achieved the best performance in gesture classification tasks, with a maximum accuracy of 84.3%. For individual feature, frequency-domain features performed the best and were proved most suitable for separating the two components. Additionally, we intuitively visualized the heatmaps of pattern-specific components based on the topological position of electrode arrays and explored their physiological interpretability by examining the correspondence between the heatmaps and muscle activation areas.

Background: Simultaneous and proportional control (SPC) based on surface electromyographic (sEMG) signals has emerged as a research hotspot in the field of human-machine interaction (HMI). However, the existing continuous motion estimation methods mostly have an average Pearson coefficient (CC) of less than 0.85, while high-precision methods suffer from the problem of long inference time (> 200 ms) and can only estimate SPC of less than 15 hand movements, which limits their applications in HMI. To overcome these problems, we propose a smooth Multi-scale Attention Patching Encoder Network (sMAPEN).

Methods: The sMAPEN consists of three modules, the Multi-scale Attention Fusion (MAF) module, the Patching Encoder (PE) module, and a smoothing layer. The MAF module adaptively captures the local spatiotemporal features at multiple scales, the PE module acquires the global spatiotemporal features of sEMG, and the smoothing layer further improves prediction stability.

Results: To evaluate the performance of the model, we conducted continuous estimation of 40 subjects performing over 40 different hand movements on the Ninapro DB2. The results show that the average Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), coefficient of determination (R²), and smoothness (SMOOTH) of the sMAPEN model are 0.9082, 0.0646°, 0.8163, and - 0.0017, respectively, which significantly outperforms that of the state-of-the-art methods in all metrics (p < 0.01). Furthermore, we tested the deployment performance of sMAPEN on the portable device, with a delay of only 97.93 ms.

Conclusions: Our model can predict up to 40 hand movements while achieving the highest predicting accuracy compared with other methods. Besides, the lightweight design strategy brings an improvement in inference speed, which enables the model to be deployed on wearable devices. All these promotions imply that sMAPEN holds great potential in HMI.

Introduction: Neck pain affects 203 million people globally and is prevalent in various settings due to factors like poor posture, lack of exercise, and occupational hazards. Therefore, addressing ergonomic issues with solutions like a wearable robotic device is crucial. This research presents a novel assistive exosuit, characterized by its slim and lightweight structure and intuitive control without the use of hands, designed to mitigate muscle fatigue in the neck and shoulders during prolonged flexed neck posture. The efficacy of the exosuit was confirmed through human experiments and user surveys.

Methods: The preliminary feasibility experiment was conducted with five subjects for 15 min to verify the effect of supporting the weight of the head with a wire on reducing neck muscle fatigue. The prime experiment was conducted with 26 subjects for 15 min to quantitatively evaluate the reduction in muscle fatigue achieved by wearing the exosuit and to assess its qualitative usability from the user's perspective. For all experiments, surface electromyography (sEMG) data was measured from upper trapezius (UT) and splenius capitis (SC) muscles, the two representative superficial muscles responsible for sustaining flexed neck posture. The analysis of the device's efficiency utilized two parameters: the normalized root mean square value (nRMS), which was employed to assess muscle activity, and the normalized median frequency (nMDF), which was utilized to gauge the extent of muscle fatigue. These parameters were statistically analyzed with the IBM SPSS statistic program.

Results: When wearing the exosuit, the nMDF of UT and SC increased by 7.18% (p < 0.05) and 5.38% (p < 0.05), respectively. For the nRMS, no significant differences were observed in either muscle. The nMDF slope of UT and SC increased by 0.63%/min (p < 0.01) and 0.34%/min (no significance). In the context of the nRMS slope, UT exhibited a reduction of 0.021% MVC/min (p < 0.05), while SC did not demonstrate any statistically significant outcomes. The exosuit received an average system usability scale score of 66.83.

Conclusions: Based on both qualitative and quantitative evaluations, our proposed assistive exosuit demonstrated that it promises the significant reduction of muscle fatigue in the neck and shoulders.