Journal of NeuroEngineering and Rehabilitation最新文献

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).

Background: Biomechanical gait impairments, such as reduced paretic propulsion, are common post-stroke. Studies have used biofeedback to increase paretic propulsion and reduce propulsion asymmetry, but it is unclear if these changes impact overall gait asymmetry. There is an implicit assumption that reducing propulsion asymmetry will improve overall gait symmetry, as paretic propulsion has been related to numerous biomechanical impairments. However, no work has investigated the impact of reducing propulsion asymmetry on overall gait asymmetry. We aimed to understand how within-session changes in propulsion asymmetry affect overall gait asymmetry in individuals post-stroke, operationalized as the combined gait asymmetry metric (CGAM). We hypothesized that decreasing propulsion asymmetry would reduce CGAM.

Methods: Participants completed twenty minutes of biofeedback training designed to increase paretic propulsion. We calculated the change in propulsion asymmetry magnitude (Δ|PA|) and the change in CGAM (ΔCGAM) during biofeedback relative to baseline. Then, we fit a robust linear mixed-effects model with ΔCGAM as the outcome and a fixed effect for Δ|PA|.

Results: We found a positive association between Δ|PA| and ΔCGAM (β = 2.6, p = 0.002). The average Δ|PA| was -0.09, suggesting that, on average, we would expect a CGAM change of 0.2, which is 0.5% of the average baseline CGAM value.

Conclusions: Reducing propulsive asymmetry using biofeedback is unlikely to produce substantial reductions in overall gait asymmetry, suggesting that biofeedback-based approaches to reduce propulsion asymmetry may need to be combined with other interventions to improve overall gait asymmetry.

Clinical trial registration: NCT04411303.

There is a consensus that motor recovery post-stroke primarily depends on the degree of the initial connectivity of the ipsilesional corticospinal tract (CST). Indeed, if the residual CST connectivity is sufficient to convey motor commands, the neuromotor system continues to use the CST predominantly, and motor function recovers up to 80%. In contrast, if the residual CST connectivity is insufficient, hand/arm dexterity barely recovers, even as the phases of stroke progress. Instead, the functional upregulation of the reticulospinal tract (RST) often occurs. In this study, we construct a computational model that reproduces the dependence of post-stroke motor recovery on the initial CST connectivity. The model emulates biologically plausible evolutions of primary motor descending tracts, based on activity-dependent or use-dependent plasticity and the preferential use of more strongly connected neural circuits. The model replicates several elements of the empirical evidence presented by the Fugl-Meyer Assessment (FMA) subscores, which evaluate the capabilities for out-of-synergy and in-synergy movements. These capabilities presumably change differently depending on the degree of the initial CST connectivity post-stroke, providing insights into the interactive dynamics of the primary descending motor tracts. We discuss findings derived from the proposed model in relation to the well-known proportional recovery rule. This modeling study aims to present a way to differentiate individuals who can achieve 70 to 80% recovery in the chronic phase from those who cannot by examining the interactive evolution of out-of-synergy and in-synergy movement capabilities during the subacute phase, as assessed by the FMA.

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.