Journal of NeuroEngineering and Rehabilitation最新文献

Objective: This study aimed to develop and validate a machine learning-based predictive model for gait recovery in patients with acute anterior circulation ischemic stroke.

Methods: Between May and November 2023, 237 patients with acute anterior circulation ischemic stroke were enrolled. Patients were randomly divided into training and validation sets at a 7:3 ratio. Thirty-one medical characteristics were collected, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to screen predictor variables. Predictive models were developed using the Random Survival Forest (RSF) and COX regression methods. The optimal model was identified based on C-index values. The SHapley Additive exPlanations (SHAP) method was employed to interpret the RSF model globally and locally.

Results: Ten predictors were identified through LASSO regression, including age, gender, periventricular white matter hyperintensities (PVWMH), Montreal Cognitive Assessment (MoCA), National Institutes of Health Stroke Scale (NIHSS), enlarged perivascular spaces in basal ganglia (BG-EPVS), lacunes, parietal infarction, basal ganglia infarction, and Timed Up & Go (TUG) test score. The C-index values of the COX regression and RSF models were 0.741 and 0.761 in the training set and 0.705 and 0.725 in the validation set, respectively. SHAP analysis of the RSF model identified BG-EPVS, TUG, MoCA, age, and PVWMH as the top five most influential predictors of gait recovery.

Conclusion: The RSF model demonstrated superior performance to the COX regression model in predicting gait recovery, offering a reliable tool for clinical decision-making regarding stroke patients' prognoses.

Background: The relative timing of transcranial direct current stimulation (tDCS) and motor practice holds potential importance in modulating cortical activity and facilitating behavioral performance.

Method: A single-blind, randomized, cross-over experiment was conducted. Twenty healthy participants engaged in a sequential finger-tapping task with their left hand. High-definition anodal tDCS (1 mA, 20 min) was administered over the right primary motor cortex (M1) either during (concurrent-tDCS) or before the motor practice (prior-tDCS). A sham tDCS condition was also employed. The three tDCS conditions were separated by one-week intervals. Cortical hemodynamic activity in the prefrontal cortex (PFC), supplementary motor area (SMA), and M1 measured by functional near-infrared spectroscopy, as well as motor performance assessed by number of correct sequences were examined before (T1), immediately after (T2), and 24 h after the practice (T3). The data was subjected to a two-way repeated measures analysis of variance.

Results: No significant interaction or main effect of condition were found on motor performance. Regarding cortical hemodynamic activity, none of the regions of interest or channels exhibited a significant interaction effect or main effect of condition. No significant correlation between cortical activity and motor performance was found.

Conclusion: Our results cannot support the timing effect of single-session anodal tDCS on facilitating brain activity or improving motor performance. These results contribute to the growing body of evidence challenging the efficacy of a single session of exogenous stimulation as an adjunct to motor practice for promoting motor acquisition. Further research should explore alternative tDCS parameters, multiple sessions and various age groups.

Background: A promising approach to improving motor recovery during rehabilitation is the use of robotic rehabilitation devices. These robotic devices provide tools to monitor the patient's recovery progress while providing highly standardized and intensive therapy. A major challenge in using these robotic devices is the ability to decide when to assist the user. In this context, we propose a Deep Learning-based solution that can learn from a therapist's criteria when a patient needs assistance during robot-aided rehabilitation therapy.

Methods: An experimental session was conducted with diverse people who suffered from neurological conditions. The participants used an upper limb rehabilitation robot to play a point-to-point game. A therapist supervised the robot-aided rehabilitation exercises and assisted the participants when considered necessary. This assistance provided by the therapist was detected to label those trajectories that were assisted to train a Deep Learning model that learns from the therapist when to assist. A series of transformations have been applied to the trajectories performed by the participants to generalize the method. Furthermore, the trajectory data was divided into sequences to be introduced to the model and continuously infer whether the user needs assistance. The data acquired during the experimental sessions have been divided into two datasets to train and evaluate the model: intra-participants (80% training, 20% validation) and test participants. The architecture of the Deep Learning model is conceived to perform time-series classification. It consists of diverse one-dimensional convolutional layers, a convolutional attention mechanism, and a Global Average Pooling layer. In addition, the output layer has one neuron with the sigmoid activation function, whose output can be interpreted as a probability of assistance. The model proposed in this study has been evaluated according to different metrics. In addition, the impact of applying fine-tuning to adapt the assistance to each user has been evaluated with the test participants.

Results: The proposed model achieved an accuracy of 91.39% and an F1-Score of 75.15% with the validation dataset during a sequence-to-sequence evaluation, surpassing other state-of-the-art architectures. When evaluating the trajectories collected in the test dataset, the method proposed achieved an accuracy of 76.09% and an F1-Score of 74.42% after applying fine-tuning to each participant.

Conclusions: The results achieved by our Deep Learning-based method show the feasibility of learning assistance decision-making from experimented therapists. Furthermore, fine-tuning can be applied to personalize the assistance to each user and improve the accuracy of the method presented when deciding whether to assist with the rehabilitation robot.

Background: Although transcutaneous spinal cord stimulation (tSCS) has been suggested as a safe and feasible intervention for gait rehabilitation, no studies have determined its effectiveness compared to sham stimulation.

Objective: To determine the effectiveness of tSCS combined with robotic-assisted gait training (RAGT) on lower limb muscle strength and walking function in incomplete spinal cord injury (iSCI) participants.

Methods: A randomized, double-blind, sham-controlled clinical trial was conducted. Twenty-seven subacute iSCI participants were randomly allocated to tSCS or sham-tSCS group. All subjects conducted a standard Lokomat walking training program of 40 sessions (5 familiarization sessions, followed by 20 sessions combined with active or sham tSCS, and finally the last 15 sessions with standard Lokomat). Primary outcomes were the lower extremity motor score (LEMS) and dynamometry. Secondary outcomes included the 10-Meter Walk Test (10MWT), the Timed Up and Go test (TUG), the 6-Minute Walk test (6MWT), the Spinal Cord Independence Measure III (SCIM III) and the Walking Index for Spinal Cord Injury II (WISCI-II). Motor evoked potential (MEP) induced by transcranial magnetic stimulation (TMS) were also assessed for lower limb muscles. Assessments were performed before and after tSCS intervention and after 3-weeks follow-up.

Results: Although no significant differences between groups were detected after the intervention, the tSCS group showed greater effects than the sham-tSCS group for LEMS (3.4 points; p = 0.033), 10MWT (37.5 s; p = 0.030), TUG (47.7 s; p = 0.009), and WISCI-II (3.4 points; p = 0.023) at the 1-month follow-up compared to baseline. Furthermore, the percentage of subjects who were able to walk 10 m at the follow-up was greater in the tSCS group (85.7%) compared to the sham group (43.1%; p = 0.029). Finally, a significant difference (p = 0.049) was observed in the comparison of the effects in the amplitude of the rectus femoris MEPs of tSCS group (- 0.97 mV) and the sham group (- 3.39 mV) at follow-up.

Conclusions: The outcomes of this study suggest that the combination of standard Lokomat training with tSCS for 20 sessions was effective for LEMS and gait recovery in subacute iSCI participants after 1 month of follow-up. Trial registration ClinicalTrials.gov (NCT05210166).

Background: Motor module (a.k.a. muscle synergy) analysis has frequently been used to provide insight into changes in muscle coordination associated with declines in walking performance, to evaluate the effect of different rehabilitation interventions, and more recently, to control exoskeletons and prosthetic devices. However, it remains unclear whether changes in muscle coordination revealed via motor module analysis stem from abnormal walking biomechanics or neural control. This distinction has important implications for the use of motor module analysis for rehabilitation interventions and device design. Thus, this study aims to elucidate the extent to which motor modules emerge from pathological walking biomechanics, i.e. abnormal walking biomechanics commonly observed in individuals with neurological disease and/or injury.

Methods: We conducted a series of computer simulations using OpenSim Moco to simulate pathological walking biomechanics by manipulating speed, asymmetry, and step width in a three-dimensional musculoskeletal model. We focused on these spatiotemporal metrics because they are commonly altered in individuals with Parkinson's disease, stroke survivors, etc. and have been associated with changes in motor module number and structure. We extracted motor modules using nonnegative matrix factorization from the muscle activations from each simulation. We then examined how alterations in walking biomechanics influenced the number and structure of extracted motor modules and compared the findings to previous experimental studies.

Results: The motor modules identified from our simulations were similar to those identified from previously published experiments of non-pathological walking. Moreover, our findings indicate that the same motor modules can be used to generate a range of pathological-like waking biomechanics by modulating their recruitment over the gait cycle. These results contrast with experimental studies in which pathological-like walking biomechanics are accompanied by a reduction in motor module number and alterations in their structure.

Conclusions: This study highlights that pathological walking biomechanics do not necessarily require abnormal motor modules. In other words, changes in number and structure of motor modules can be a valuable indicator of alterations in neuromuscular control and may therefore be useful for guiding rehabilitation interventions and controlling exoskeletons and prosthetic devices in individuals with impaired walking function due to neurological disease or injury.

Background: Construct validity and responsiveness of upper limb outcome measures are essential to interpret motor recovery poststroke. Evaluating the associations between clinical upper limb measures and sensor-based arm use (AU) fosters a coherent understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias from walking-related activities. Here, we investigate the measurement properties of a comprehensive set of clinical measures and sensor-based AU metrics when gait and non-functional upper limb movements are excluded.

Methods: In this prospective, longitudinal cohort study, individuals with motor impairment were measured at days 3 ± 2 (D3), 10 ± 2 (D10), 28 ± 4 (D28), 90 ± 7 (D90), and 365 ± 14 (D365) after their first stroke. Using clinical measures, upper limb motor function (Fugl-Meyer Assessment), capacity (Action Research Arm Test, Box & Block Test), and perceived performance (14-item Motor Activity Log) were assessed. Additionally, individuals wore five movement sensors (trunk, wrists, and ankles) for three days. Thirteen AU metrics were computed based on functional movements during non-walking periods. Construct validity across clinical measures and AU metrics was determined by Spearman's rank correlations for each time point. Criterion responsiveness was examined by correlating patient-reported Global Rating of Perceived Change (GRPC) scores and observed change in upper limb measures and AU metrics. Optimal cut-off values for minimal important change (MIC) were estimated by ROC curve analysis.

Results: Ninety-three individuals participated. At D3 and D10, correlations between clinical measures and AU metrics showed variability (range r_s: 0.44-0.90). All following time points showed moderate-to-high positive correlations between clinical measures and affected AU metrics (range r_s: 0.57-0.88). Unilateral nonaffected AU duration was negatively correlated with clinical measures (range r_s: -0.48 to -0.77). Responsiveness across outcomes was highest between D10-D28 within moderate to strong relations between GRPC and clinical measures (r_s: range 0.60-0.73), whereas relations were weaker for AU metrics (range r_s: 0.28-0.43) Eight MIC values were estimated for clinical measures and nine for AU metrics, showing moderate to good accuracy (66-87%).

Conclusions: We present reference data on the construct validity and responsiveness of clinical upper limb measures and specified sensor-based AU metrics within the first year after stroke. The MIC values can be used as a benchmark for clinical stroke rehabilitation.

Trial registration: This trial was registered on clinicaltrials.gov; registration number NCT03522519.

Duchenne Muscular Dystrophy (DMD) progressively leads to loss of limb function due to muscle weakness. The incurable nature of the disease shifts the focus to improving quality of life, including assistive supports to improve arm function. Over time, the passive joint impedance (Jimp) of people with DMD increases. Force-based controlled motorised arm supports require a clear distinction between the user's movement intention and passive forces, such as passive Jimp. Therefore, Jimp identification is essential. This review aims to define Jimp, identify factors influencing it, and outline experimental methods used for quantification, with a focus on the upper extremities in DMD. A literature review was performed in May 2021 and updated in March 2024 using SCOPUS, PubMed, IEEEXplore, and WebOfScience. The results reveal confusion in definitions and show various Jimp measuring practices for both DMD and individuals without muscle weakness. This study presents an overview and lists important parameters affecting passive Jimp, such as the joint's position, velocity and the multi-articular nature of the upper arm muscles. For personalised passive Jimp compensation in arm supports, ramp-type perturbations with constant velocity across the full joint range appear most optimal for identifying the elevated and non-linear nature of the passive Jimp in DMD.

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models.

Background: Advanced age brings a loss of plantar sensation, represented, for example, as higher sensation thresholds in standardized testing. This is thought to contribute to an increased risk of falls among older adults - an intuitive premise that has yet to be fully investigated, especially in the context of walking balance. The purpose of this study was to quantify the association between plantar sensation and the instability elicited by a suite of walking balance perturbations that differ in direction and context in a cohort of n = 28 older adults (73.0 ± 5.9 yrs).

Methods: We measured plantar sensation using Semmes-Weinstein monofilaments and quantified margins of stability (MoS) and whole-body angular momentum (WBAM) during habitual walking and in response to optical flow perturbations, lateral waist-pull perturbations, and treadmill-induced slips.

Results: Our two major results were that higher monofilament thresholds (i.e., worse plantar sensation) in older adults associated with: (1) larger anterior-posterior (AP) and mediolateral (ML) MoS and increased transverse plane WBAM (p ≤ 0.031) during habitual walking, and (2) larger decreases in MoS_AP, MoS_ML and larger increases in transverse plane WBAM in response to lateral waist pull perturbations (p ≤ 0.018). We found no associations between plantar sensation and responses to other perturbation contexts.

Conclusions: We conclude that there is an association between worse plantar sensation and gait instability, both during habitual unperturbed walking and in response to some perturbation contexts. These results should build confidence that interventions designed to improve plantar sensation for older adults, possibly through insoles or footwear modifications, could be critical for reducing gait-related falls in at-risk populations.

Background: Intensive rehabilitation through challenging and individualized tasks are recommended to enhance upper limb recovery after stroke. Robot-assisted therapy (RAT) and serious games could be used to enhance functional recovery by providing simultaneous motor and cognitive rehabilitation.

Objective: The aim of this study is to clinically validate the dynamic difficulty adjustment (DDA) mechanism of ROBiGAME, a robot serious game designed for simultaneous rehabilitation of motor impairments and hemispatial neglect.

Methods: A proof of concept, with 24 participants in subacute and chronic stroke, was conducted using a 5-day protocol (two days were dedicated to assessment and three days to consecutive training sessions). Participants performed three consecutive ROBiGAME sessions during which overall task difficulty was determined through simultaneous DDA of motor and attentional parameters. Relationships between clinical and robotic assessment scores with respective task-difficulty parameters were analyzed using a multivariate regression model and a principal component analysis.

Results: Game difficulty rapidly (within approximately thirty minutes) auto-adapted to match individual impairment levels. The relationship between task-difficulty parameters with motor (Fugl Meyer Assessment: r = 0.84 p < 0.05) and with attentional impairments (Bells test total omissions: r = 0.617 p < 0.05) showed that task-difficulty during RAT adapted to each participant's degree of impairment. Principal component analysis identified two data subsets determining overall task-difficulty, one subset for motor and the other for cognitive functional evaluation scores with respective task-difficulty parameters.

Conclusions: This proof of concept clinically validated a DDA mechanism and showed how task-difficulty adequately adapted to match individual degrees of impairment during RAT after stroke. ROBiGAME provided simultaneous motor and attentional exercises with parameters determining task-difficulty strongly related with respective clinical and robotic evaluation scores. Individualized levels of game difficulty and rapid adjustment of the system suggest implementation in clinical practice. Registry number This study was registered at ClinicalTrials.gov (NCT02543424).