Journal of NeuroEngineering and Rehabilitation最新文献

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.

Wearable robots are often powered by elastic actuators, which can mimic the intrinsic compliance observed in human joints, contributing to safe and seamless interaction. However, due to their increased complexity, when compared to direct drives, elastic actuators are susceptible to faults, which pose significant challenges, potentially compromising user experience and safety during interaction. In this article, we developed a fault-tolerant control strategy for torque assistance in a knee exoskeleton and investigated user experience during a walking task while emulating faults. We implemented and evaluated the torque control scheme, based on impedance control, for a mechanically adjustable compliance actuator with nonlinear torque-deflection characteristics. Conducted functional evaluation experiments showed that the control strategy is capable of providing support during gait based on a torque profile. A user study was conducted to evaluate the impact of fault severity and compensation on the perception of support, stiffness, comfort, and trust while walking with the exoskeleton. Results from the user study revealed significant differences in participants' responses when comparing support and stiffness levels without fault compensation. In contrast, no significant differences were found when faults were compensated, indicating that fault tolerance can be achieved in practice. Meanwhile, comfort and trust measurements do not seem to depend directly on torque support levels, pointing to other influencing factors that could be considered in future research.

Background: Arm-lifting movements (shoulder flexion) are essential for upper extremity rehabilitation after a stroke. Abnormal flexor synergy (elbow flexion) is frequently observed during shoulder flexion, impeding functional improvement. However, no quantitative method exists for assessing abnormal flexor synergy. This study investigated the validity and responsiveness of a newly developed index to quantitatively evaluate abnormal flexor synergy.

Methods: Participants included 103 patients (mean age: 58.0 ± 10.1 years; 64 men, 39 women) with stroke. Using three-dimensional coordinate data during shoulder flexion obtained from a depth sensor camera, we calculated the abnormal flexor synergy based on our developed index. The abnormal flexor synergy index decreases with increasing flexion of the elbow joint during shoulder flexion (the maximum value is 100% without abnormal flexor synergy). The validity of the abnormal flexor synergy index was assessed by analyzing the correlation between the index and both the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) four-category scores and the Modified Ashworth Scale (MAS) scores for elbow, wrist, and finger flexors, using Pearson's and Spearman's correlation coefficients. Responsiveness was studied in 17 inpatients (mean age: 59.5 ± 8.1 years; 7 men, 10 women) who underwent proximal upper extremity intervention for approximately 3 weeks, evaluating change from admission to discharge using the standardized response mean (SRM).

Results: Significant correlations were observed between the abnormal flexor synergy index and FMA-UE scores: A (r = 0.625, p < 0.001), B (r = 0.433, p < 0.001), C (r = 0.418, p < 0.001), and D (r = 0.411, p < 0.001), as well as MAS scores for elbow flexors (r = -0.283, p = 0.004) and proximal interphalangeal flexors (r = -0.201, p = 0.042). The highest responsiveness was observed in the FMA-UE A score (SRM = 0.81), followed by the abnormal flexor synergy index (SRM = 0.79).

Conclusions: The newly developed index for assessing abnormal flexor synergy demonstrated superior validity and high responsiveness. These results suggest the potential for using this index to evaluate upper extremity function in patients with stroke.

Objective: Cognitive-motor dual-tasking training (CMDT) might improve limb function and motor performance in stroke patients. However, is there enough evidence to prove that it is more effective compared with conventional physical single-task training? This meta-analysis and Trial Sequential Analysis of randomized clinical trials (RCTs) aimed to evaluate the effectiveness of CMDT on balance and gait for treating hemiplegic stroke patients.

Methods: The databases were searched in PubMed, Web of Science, Ovid Database and The Cochrane Library, SinoMed database, Chinese National Knowledge Infrastructure (CNKI), Wan Fang database, and VIP database up to December 8, 2023. The Cochrane-recommended risk of bias (RoB) 2.0 tool was employed to assess risk of bias in trials. The statistical analysis was employed using R version 4.3.2. In addition, subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The evidence for each outcome was evaluated according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. The Copenhagen Trial Unit's Trial Sequential Analysis (version 0.9.5.10 Beta) was used for sequential analysis.

Results: Seventeen randomized clinical trials (RCTs) (n = 751 patients) were included. The results demonstrated that cognitive-motor dual-task training (CMDT) might be beneficial on stroke patients on Berg Balance Scale (BBS) (MD = 4.26, 95% CI 1.82, 6.69, p < 0.0001) (low-quality evidence). However, CMDT might not affect Time Up and Go test (TUG) (MD = -1.28, 95% CI -3.63, 1.06, p = 0.284); and single-task walking speed (MD = 1.35, 95% CI -1.56, 4.27, p = 0.413) in stroke patients (low-quality evidence). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) results indicated that all findings were very low to low certainty. Trial Sequential Analyses demonstrated larger sample sizes are required for confirming our findings.

Conclusion: Cognitive-motor dual-task training (CMDT) compared with conventional physical single-task training might be an effective intervention for improving static balance function in stroke patients (low-quality evidence), which should be interpreted cautiously due to heterogeneity and potential biases. Nevertheless, further research is required to support the abovementioned findings. Trial Registration This protocol was registered in PROSPERO (CRD42023490530).

Background: Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

Methods: Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.

Results: Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.

Conclusion: This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, characterized by impairments in social interaction and communication with restricted and repetitive behavior. Postural and motor disturbances occur more often in ASD, in comparison to typically developing subjects, affecting the quality of life. Linear and non-linear indexes derived from the trajectory of the center of pressure (COP) while subjects stand on force platforms are commonly used to assess postural stability. The aim of the present feasibility study was to investigate whether combining linear and non-linear parameters of the COP during stance in subjects with ASD, could provide insight on specific features of motor dysfunction possibly linked to ASD cognition and clinical characteristics.

Methods: Twenty-two males, aged 10-15 years, including subjects with ASD and healthy controls (N = 11, respectively), were studied. They all had normal cognitive level and independent walking ability. A piezoelectric force platform was used to evaluate posture over three feet positions, with eyes open, closed and during visually-guided saccades. Linear (sway path, total area and root mean square) and non-linear parameters (fractal dimension and sample entropy) of the COP were measured to determine postural stability and the complexity and regularity of the COP signals. GLMM analyses were performed to assess COP parameter changes across experimental conditions and subject groups. Finally, Spearman correlations evaluated the significance of potential relationships between linear and non-linear measures as well as between non-linear parameters and clinical data in patients with ASD.

Results: Compared to controls, subjects with ASD showed reduced postural stability and complexity, with higher regularity of COP trajectories, particularly in the most unstable feet positions, during visually-guided saccades and in the medial-lateral direction. Spearman correlations indicated that, in the patients' group, postural instability was associated with a decrease in the geometric complexity and an increase in the regularity of the COP trajectory. Moreover, the increase in regularity of the COP trajectory was associated to the severity of restricted and repetitive behavior.

Conclusions: The results of this study highlight the importance of combining linear and non-linear measures in evaluating postural control in patients with ASD, also with respect to the outcome of interventions on these patients targeting postural balance.

Background: This research aims to improve the control of assistive devices for individuals with hemiparesis after stroke by providing intuitive and proportional motor control. Stroke is the leading cause of disability in the United States, with 80% of stroke-related disability coming in the form of hemiparesis, presented as weakness or paresis on half of the body. Current assistive exoskeletonscontrolled via electromyography do not allow for fine force regulation. Current control strategies provide only binary, all-or-nothing control based on a linear threshold of muscle activity.

Methods: In this study, we demonstrate the ability of participants with hemiparesis to finely regulate their muscle activity to proportionally control the position of a virtual bionic arm. Ten stroke survivors and ten healthy, aged-matched controls completed a target-touching task with the virtual bionic arm. We compared the signal-to-noise ratio (SNR) of the recorded electromyography (EMG) signals used to train the control algorithms and the task performance using root mean square error, percent time in target, and maximum hold time within the target window. Additionally, we looked at the correlation between EMG SNR, task performance, and clinical spasticity scores.

Results: All stroke survivors were able to achieve proportional EMG control despite limited or no physical movement (i.e., modified Ashworth scale of 3). EMG SNR was significantly lower for the paretic arm than the contralateral nonparetic arm and healthy control arms, but proportional EMG control was similar across conditions for hand grasp. In contrast, proportional EMG control for hand extension was significantly worse for paretic arms than healthy control arms. The participants' age, time since their stroke, clinical spasticity rate, and history of botulinum toxin injections had no impact on proportional EMG control.

Conclusions: It is possible to provide proportional EMG control of assistive devices from a stroke survivor's paretic arm. Importantly, information regulating fine force output is still present in muscle activity, even in extreme cases of spasticity where there is no visible movement. Future work should incorporate proportional EMG control into upper-limb exoskeletons to enhance the dexterity of stroke survivors.

Background: Upper limb impairment post-stroke often leads to a predominant use of the less affected arm and consequent learned disuse of the affected side, hindering upper limb outcome. Wearable sensors such as accelerometers, combined with smart reminders (i.e., based on the amount of arm activity), offer a potential approach to promote increased use of the affected arm to improve upper limb use during daily life. This study aimed to evaluate the efficacy of wrist vibratory reminders during a six-week home-based intervention in chronic stroke survivors.

Methods: We evaluated the impact of the home-based intervention on the primary outcome, the Motor Activity Log-14 Item Version scores Amount of Use (MAL-14 AOU), and the secondary outcomes MAL-14 Quality of Movement (QOM) and sensor-derived activity metrics from the affected arm. A randomized controlled trial design was used for the study: the intervention group received personalized reminders based on individualized arm activity goals, while the control group did not receive any feedback. Mixed linear models assessed the influence of the group, week of the intervention period, and initial impairment level on MAL-14 and arm activity metrics.

Results: Forty-two participants were enrolled in the study. Overall, participants exhibited modest but not clinically relevant increases in MAL-14 AOU (+ 0.2 points) and QOM (+ 0.2 points) after the intervention period, with no statistically significant differences between the intervention and control group. Feasibility challenges were noted, such as adherence to wearing the trackers and sensor data quality. However, in participants with sufficiently available sensor data (n = 23), the affected arm use extracted from the sensor data was significantly higher in the intervention group (p < 0.05). The initial impairment level strongly influenced affected arm use and both MAL-14 AOU and QOM (p < 0.01).

Conclusions: The study investigated the effectiveness of incorporating activity trackers with smart reminders to increase affected arm activity among stroke survivors during daily life. While the results regarding the increased arm use at home are promising, patient-reported outcomes remained below clinically meaningful thresholds and showed no group differences. Further, it is essential to acknowledge feasibility issues such as adherence to wearing the trackers during the intervention and missing sensor data.

Trial registration: NCT03294187.