Search Results (24)

Parkinson’s disease (PD) is a heterogeneous clinical syndrome representing the final stage of a complex and long-lasting neurodegenerative process that involves not only dysfunction of the dopaminergic system but also impairments in other neurotransmitter systems. The diversity of the clinical presentation of PD, together with the existence of Parkinsonian syndromes and atypical Parkinsonism—such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB)—has important implications for rehabilitation outcomes and underscores the need for individualized, stage-dependent therapeutic approaches. Juggling is a complex motor activity that integrates cognitive, visuomotor, and balance processes, requiring a high level of concentration, precision, and motor adaptation. In recent years, there has been growing interest in this form of activity as a potential tool for supporting neuroplasticity, cognitive functions, and neurological rehabilitation. The aim of this review was to summarize current scientific evidence on the effects of juggling training on cognitive functions, visuomotor coordination, and balance, as well as to discuss the potential benefits of combining it with controlled hypoxia in patients with Parkinson’s disease (PD). This narrative review additionally considers how disease heterogeneity and stage of progression may influence the effectiveness of such multimodal interventions. This paper reviews the literature concerning the neurophysiological basis of learning to juggle and the mechanisms of brain plasticity, including increases in gray matter volume, improvements in white matter integrity, and reorganization of neuronal networks in motor and associative regions. Attention is drawn to the synergistic potential of combining juggling training with exposure to moderate, controlled hypoxia, which may induce an adaptive response involving the transcription factor HIF-1α, enhance the expression of brain-derived neurotrophic factor (BDNF), and promote angiogenesis and mitochondrial biogenesis. Although juggling and hypoxia are not directly related to training stimuli, both interventions activate overlapping and complementary neuroplastic pathways, providing a conceptual rationale for their parallel consideration and potential integration within future rehabilitation protocols. Juggling delivers task-specific motor–cognitive learning, whereas hypoxia may amplify molecular plasticity signaling, potentially enhancing responsiveness to motor interventions, particularly in patients at early stages of PD when compensatory mechanisms and neuroplastic capacity are relatively preserved. Findings from existing studies suggest that juggling under controlled hypoxic conditions may represent an innovative, safe, and multimodal form of training that supports both cognitive and motor components. Such effects may be particularly relevant in patients at early stages of PD, when compensatory mechanisms and neuroplastic potential are relatively preserved. Such an intervention may contribute to improvements in balance, attention, executive functions, and cognitive flexibility, which is particularly relevant in the context of rehabilitation for patients with neurodegenerative diseases. Importantly, to date, no randomized clinical trials have directly examined juggling performed under controlled hypoxic conditions in PD. Therefore, the present concept should be regarded as translational and exploratory, integrating evidence from juggling-induced neuroplasticity and hypoxia-related physiological adaptations. In this context, the proposed approach represents a proof-of-concept framework for future multimodal interventions rather than an established therapeutic strategy. Available evidence suggests that combining complex sensorimotor skill training with physiological modulation of the internal environment may constitute a novel direction in PD rehabilitation, extending beyond conventional exercise-based models. Despite promising reports, further well-designed clinical studies are needed to determine the optimal training parameters (frequency, intensity, duration, and degree of hypoxia), to evaluate the long-term sustainability of therapeutic effects, and to account for the heterogeneity of PD and related Parkinsonian disorders. Full article

The growing demand for resources for production in intensive livestock farming requires research to operate with an environmentally sustainable perspective and respect for animal welfare, promoting circularity in the livestock industry. In this context, animal monitoring plays a key role in livestock management, not only to ensure their well-being but also to preserve the balance of the territory. In particular, early detection of oestrus events is one of the crucial elements in livestock monitoring. This study presents the development and on-farm validation of a low-power oestrus detection system for dairy cows, based on stand-alone smart pedometers (SASPs) connected through a Low-Power Wide-Area Network (LPWAN). The system implements an upgradeable, threshold-based algorithm that analyzes cow motor activity using a 24 h moving-mean approach and three behavioral indicators related to oestrus expression. Data are processed on board and transmitted to a cloud platform for visualization through a farmer-oriented WebApp, without requiring any fixed installation in the barn. The system was tested on a commercial free-stall dairy farm over three experimental campaigns (2021–2023). Oestrus events were validated through farmer visual observation and milk progesterone analysis, used as the reference method. A total of 22 confirmed oestrus events were analyzed. The system achieved a detection rate of 72.7% for certain oestrus events and 86.4% when including probable detections, with a mean oestrus duration of 18.1 ± 2.5 h, consistent with values reported in the literature. The proposed solution demonstrates the feasibility of a transparent, low-computational-cost oestrus detection approach compatible with LPWAN constraints. Its plug-and-play design, reduced infrastructure requirements, and upgradable firmware, although not able to self-update, limiting its potential compared to the machine learning-based methods present in the literature, make it suitable for practical adoption, particularly in farms where conventional connectivity and high-cost commercial systems are limiting factors. Full article

Introduction: Cerebral palsy (CP) affects motor function development, requiring intensive rehabilitation. Virtual reality (VR) interventions show promise for improving motor learning through immersive, engaging experiences. This systematic review and meta-analysis evaluated VR effectiveness for motor function improvement in children with CP. Methods: Following PRISMA 2020 guidelines, we searched six electronic databases from inception to 15 June 2025. Included studies compared VR interventions versus control conditions in children with CP (ages 4–18 years), measuring motor function outcomes. Sixteen studies (n = 397 participants) met the inclusion criteria for qualitative synthesis. Random-effects models, subgroup analyses, and meta-regression were performed. Evidence certainty was evaluated using GRADE methodology. Results: Five randomized controlled trials with complete extractable data (N = 190 participants, 40 effect sizes) were included in the primary quantitative meta-analysis. The primary meta-analysis demonstrated moderate overall effects favoring VR interventions (standardized mean difference [SMD] = 0.41, 95% CI [0.16, 0.66], p = 0.001; I² = 74%); however, GRADE quality was rated LOW due to risk of bias and imprecision. Technology type critically moderated outcomes: robotic exoskeleton systems showed large effects (SMD = 1.00, p = 0.002), commercial gaming platforms showed small-to-moderate effects (SMD = 0.38, p = 0.013), while custom VR systems showed no significant benefit (SMD = 0.01, p = 0.905; Q = 29.00, p < 0.001). Age emerged as the strongest moderator: children (<6 years) demonstrated significant benefits (SMD = 0.98, p < 0.001), whereas school-age children (6–12 years) showed no effect (SMD = −0.01, p = 0.903; meta-regression slope = −0.236 per year, p < 0.001). Dose–response was non-linear, with optimal benefits at 30–40 intervention hours and diminishing returns beyond 50 h. VR proved superior to standard care (SMD = 0.83) but not to active intensive therapies (SMD = 0.09). The safety profile was favorable (1.3% adverse event rate, no serious events). No publication bias was detected. Conclusions: VR interventions demonstrated moderate, technology-dependent motor function improvements in children with CP, with benefits concentrated in young children using robotic systems. Evidence certainty is low, requiring further high-quality trials. Implementation should prioritize robotic VR for children with 30–40 h protocols. Full article

Typical fertilizer applicators are often restricted in performance due to non-uniformity in distribution, required labor and time intensiveness, high discharge rate, chemical input wastage, and fostering weed proliferation. To address this gap in production agriculture, an automated variable-rate fertilizer applicator was developed for the cotton crop that is based on deep learning-initiated electronic control unit (ECU). The applicator comprises (a) plant recognition unit (PRU) to capture and predict presence (or absence) of cotton plants using the YOLOv7 recognition model deployed on-board Raspberry Pi microprocessor (Wale, UK), and relay decision to a microcontroller; (b) an ECU to control stepper motor of fertilizer metering unit as per received cotton-detection signal from the PRU; and (c) fertilizer metering unit that delivers precisely metered granular fertilizer to the targeted cotton plant when corresponding stepper motor is triggered by the microcontroller. The trials were conducted in the laboratory on a custom testbed using artificial cotton plants, with the camera positioned 0.21 m ahead of the discharge tube and 16 cm above the plants. The system was evaluated at forward speeds ranging from 0.2 to 1.0 km/h under lighting levels of 3000, 5000, and 7000 lux to simulate varying illumination conditions in the field. Precision, recall, F1-score, and mAP of the plant recognition model were determined as 1.00 at 0.669 confidence, 0.97 at 0.000 confidence, 0.87 at 0.151 confidence, and 0.906 at 0.5 confidence, respectively. The mean absolute percent error (MAPE) of 6.15% and 9.1%, and mean absolute deviation (MAD) of 0.81 g/plant and 1.20 g/plant, on application of urea and Diammonium Phosphate (DAP), were observed, respectively. The statistical analysis showed no significant effect of the forward speed of the conveying system on fertilizer application rate (p > 0.05), thereby offering a uniform application throughout, independent of the forward speed. The developed fertilizer applicator enhances precision in site-specific applications, minimizes fertilizer wastage, and reduces labor requirements. Eventually, this fertilizer applicator placed the fertilizer near targeted plants as per the recommended dosage. Full article

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 10³ arithmetic operations per cycle and less than 10² kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. Full article

The aim of this study is to analyze the signal generated in the brain for a specific motor task and to identify the region where it occurs. For this purpose, electroencephalography (EEG) signals were divided into delta, theta, alpha, and beta frequency sub-bands, and feature extraction was performed by looking at the time-frequency characteristics of the signals belonging to the obtained sub-bands. The epoch corresponding to motor imagery or action and the signal source in the brain were determined by power spectral density features. This study focused on a hand open–close motor task as an example. A machine learning structure was used for signal recognition and classification. The highest accuracy of 92.9% was obtained with the neural network in relation to signal recognition and action realization. In addition to the classification framework, this study also incorporated advanced preprocessing and energy analysis techniques. Eye blink artifacts were automatically detected and removed using independent component analysis (ICA), enabling more reliable spectral estimation. Furthermore, a detailed channel-based and sub-band energy analysis was performed using fast Fourier transform (FFT) and power spectral density (PSD) estimation. The results revealed that frontal electrodes, particularly Fp1 and AF7, exhibited dominant energy patterns during both real and imagined motor tasks. Delta band activity was found to be most pronounced during rest with T1 and T2, while higher-frequency bands, especially beta, showed increased activity during motor imagery, indicating cognitive and motor planning processes. Although 30 s epochs were initially used, event-based selection was applied within each epoch to mark short task-related intervals, ensuring methodological consistency with the 2–4 s windows commonly emphasized in the literature. After artifact removal, motor activity typically associated with the C3 region was also observed with greater intensity over the frontal electrode sites Fp1, Fp2, AF7, and AF8, demonstrating hemispheric symmetry. The delta band power was found to be higher than that of other frequency bands across T0, T1, and T2 conditions. However, a marked decrease in delta power was observed from T0 to T1 and T2. In contrast, beta band power increased by approximately 20% from T0 to T2, with a similar pattern also evident in gamma band activity. These changes indicate cognitive and motor planning processes. The novelty of this study lies in identifying the electrode that exhibits the strongest signal characteristics for a specific motor activity among 64-channel EEG recordings and subsequently achieving high-performance classification of the corresponding motor activity. Full article

Introduction: Basketball performance requires not only intermittent high-intensity movements—such as sprinting, jumping, and rapid directional changes—but also rapid decision-making under cognitive and psychological stress. Transcranial direct current stimulation (tDCS) has emerged as a potential modality to enhance both physical and mental performance due to its capacity to modulate cortical excitability and promote synaptic plasticity. Although the broader literature suggests that tDCS can benefit motor performance and endurance across various sports, its specific impact on basketball remains underexplored. Methods: This scoping review aimed to summarize current evidence on the effects of tDCS in basketball. A comprehensive literature search was conducted across databases including PubMed/Medline, Google Scholar, and Cochrane, identifying studies published between January 2008 and February 2025. Only clinical trials investigating tDCS interventions in basketball players were included. Eleven articles met the inclusion criteria and were synthesized narratively, with a focus on stimulation parameters (site, duration, intensity) and performance outcomes (shooting accuracy, dribbling, sprinting, decision-making, fatigue). Results: The reviewed studies indicated that tDCS—particularly when applied over the motor cortex—was associated with moderate improvements in shooting accuracy, dribbling time, repeated-sprint performance, and decision-making under fatigue. Some studies reported delayed rather than immediate benefits, suggesting that tDCS may prime neural networks for enhanced learning and retention. However, not all findings were consistent; certain interventions produced minimal or no significant effects, especially regarding subjective mental fatigue and cognitive workload. The variability in electrode placements and stimulation protocols highlights the need for methodological standardization. Conclusions: Current evidence partially supports the potential of tDCS to improve specific performance domains in basketball, particularly in skill acquisition, neuromuscular efficiency, and decision-making. Nevertheless, the findings are limited by small sample sizes, heterogeneous protocols, and a lack of long-term follow-up. Future research should prioritize larger, multisite studies with standardized tDCS parameters and ecologically valid outcome measures to confirm the efficacy and practical relevance of tDCS in competitive basketball settings. Full article

Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of a Permanent Magnet Synchronous Motor (PMSM). While proportional–integral (PI) controllers remain a widely adopted choice for FOC due to their simplicity, their performance can degrade significantly under high-frequency speed transitions, where nonlinear dynamics introduce notable inaccuracies. The TinyFC model complements the PI controller by learning the intrinsic dependencies within the control loops and generating corrective signals to alleviate these inaccuracies. To ensure practical implementation, TinyFC underwent extensive optimization procedures, incorporating advanced techniques such as hyperparameter tuning, pruning, and 8-bit quantization. These measures successfully reduced the model’s computational overhead while preserving predictive accuracy. Simulation results demonstrated that embedding TinyFC within the FOC framework substantially reduced overshoot, with the pruned TinyFC entirely eliminating overshoot when integrated into the speed control unit. These findings highlight the feasibility of employing lightweight neural networks for real-time motor control applications, establishing a foundation for more efficient and precise control strategies in edge automotive and industrial systems. Full article

This study aimed to promote a coherent pedagogical framework for integrating mobile exergames into physical education (PE) as a strategy to reduce sedentary behavior. The intervention was grounded in the game-based learning methodology, assessing the impact of exergame practice on the physical and mental well-being of prospective PE teachers. The Borg Rating of Perceived Exertion (RPE) scale and a mental effort scale were used to evaluate perceived exertion across various mini-games, measuring physical intensity, motor engagement, and mental effort with participation from 130 undergraduate students in Physical Activity and Sport Sciences. Additionally, the pedagogical and motivational aspects of the Active Arcade v3.11 video game were analyzed to support its future integration into secondary education PE classes. Participants reported high levels of motor engagement throughout the program, accompanied by moderate physical intensity. They also emphasized the user-friendly nature of these augmented reality exergames and expressed enjoyment during the sessions. The findings suggest that mobile exergames hold considerable potential for enhancing skill acquisition and fundamental motor skills while promoting healthy habits among students in PE classes. Full article

Falls are a leading cause of injury and mortality among older adults, placing significant physical, emotional, and financial burdens on individuals, families, and healthcare systems. The early identification of fall risk and frequent reassessments during rehabilitation are essential for prevention and recovery. However, conventional assessments are time-intensive, rely on multiple motor tasks, and are typically conducted in specialized facilities, limiting their accessibility. This study introduces a novel machine learning-based computerized adaptive testing algorithm that personalizes testing to individual capabilities. The adaptive approach reduces task sequences by over 50% while maintaining high predictive accuracy. It also enables remote testing, predicting performance on complex tasks using as few as 2–3 simpler, accessible tasks. This innovation supports scalable online fall risk screening and frequent balance assessments during rehabilitation, offering a practical and efficient solution for both personalized and community-wide healthcare needs. Full article

Background: Malnutrition has extensive consequences, affecting multiple levels of functioning, including motor skill impairments. However, current interventions have mainly focused on dietary treatment, often neglecting motor impairments and relying solely on clinical and anthropometric indicators to assess treatment response. This study aims to bridge this gap by examining the combined effect of ready-to-use supplementary food (RUSF) and high-intensity motor learning (HiML) on motor skill-related physical fitness in children with moderate thinness (MT). Methods: A cluster randomized controlled trial was conducted among children 5–7 years old with MT in Jimma Town. Three schools were randomized to three intervention arms, including a total of 69 children: RUSF (n = 23), RUSF + HiML (n = 25), and no intervention (n = 21). The HiML training was applied for 12 weeks, and RUSF was distributed daily for 12 weeks. HiML was given daily (1 h/day, 5 days/week). The primary outcome was motor skill-related physical fitness assessed at baseline and endline using the performance and fitness test battery (PERF-FIT). The changes from baseline to endline measurements were calculated as differences, and the mean difference in these changes/differences (DID) was then computed as the outcome measure. AN(C)OVA was used to directly investigate differences between groups. Statistical significance was declared at p-value ≤ 0.05. Results: There was a significantly greater and comparable improvement in both the RUSF and RUSF + HiML groups compared to the control group for the ‘stepping’ item (p < 0.001), the ‘side jump’ item (p < 0.001), the ‘standing long jump’ (p < 0.001) and the ‘jumping and hopping’ total (p = 0.005). The RUSF + HiML group showed significantly greater improvements in the ‘bounce and catch’ (p = 0.001) and ‘throw and catch’ (p < 0.001) items compared to the RUSF group, which, in turn, demonstrated greater improvement than the control group in both items (p < 0.01). Conclusions: A 12-week combination of RUSF + HiML was proven to be safe in children with MT and caused clear improvements in motor skill-related physical fitness. When the children received RUSF with HiML training, similar gains in stepping, side jump, standing long jump, and jumping and hopping were observed, except for the ball skills where the HiML training group performed better. Full article

Background: In Ethiopia, moderate thinness (MT) is a persistent issue among children. Yet, evidence on the effects of dietary supplementation and motor skills training in these children is limited. Objective: This study aimed to assess the effect of Ready-to-Use Supplementary Food (RUSF), whether or not combined with high-intensity motor learning (HiML), on weight, height, body composition, and muscle strength in children 5–7 years old with MT living in Jimma Town, Ethiopia. Methods: A cluster-randomized controlled trial was carried out among 69 children (aged 5–7) with MT assigned to receive RUSF (n = 23), RUSF + HiML (n = 25), or no intervention (control group, n = 21). A multivariable Generalized Estimating Equations model was used and the level of significance was set at alpha < 0.05. Results:At baseline, there were no significant differences in the outcome measurements between the RUSF, RUSF + HiML, and control groups. However, after 12 weeks of intervention, there were significant mean differences in differences (DIDs) between the RUSF group and the control arm, with DIDs of 1.50 kg for weight (p < 0.001), 20.63 newton (N) for elbow flexor (p < 0.001), 11.00 N for quadriceps (p = 0.023), 18.95 N for gastrocnemius sup flexor of the leg (p < 0.001), and 1.03 kg for fat-free mass (p = 0.022). Similarly, the mean difference in differences was higher in the RUSF + HiML group by 1.62 kg for weight (p < 0.001), 2.80 kg for grip strength (p < 0.001), 15.93 for elbow flexor (p < 0.001), 16.73 for quadriceps (p < 0.001), 9.75 for gastrocnemius sup flexor of the leg (p = 0.005), and 2.20 kg for fat-free mass (p < 0.001) compared the control arm. Conclusion: RUSF alone was effective, but combining it with HiML had a synergistic effect. Compared to the control group, the RUSF and RUSF + HiML interventions improved the body composition, height, weight, and muscle strength of the studied moderately thin children. The findings of this study suggest the potential that treating moderately thin children with RUSF and combining it with HiML has for reducing the negative effects of malnutrition in Ethiopia. Future research should explore these interventions in a larger community-based study. This trial has been registered at the Pan African Clinical Trials Registry (PACTR) under trial number PACTR202305718679999. Full article

Event-related optical signals (EROS) measure fast modulations in the brain’s optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain–computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications. Full article

Motor impairment is a common issue in stroke patients, often affecting the upper limbs. To this standpoint, robotic neurorehabilitation has shown to be highly effective for motor function recovery. Notably, Machine learning (ML) may be a powerful technique able to identify the optimal kind and intensity of rehabilitation treatments to maximize the outcomes. This retrospective observational research aims to assess the efficacy of robotic devices in facilitating the functional rehabilitation of upper limbs in stroke patients through ML models. Specifically, clinical scales, such as the Fugl-Meyer Assessment (A-D) (FMA), the Frenchay Arm Test (FAT), and the Barthel Index (BI), were used to assess the patients’ condition before and after robotic therapy. The values of these scales were predicted based on the patients’ clinical and demographic data obtained before the treatment. The findings showed that ML models have high accuracy in predicting the FMA, FAT, and BI, with R-squared (R²) values of 0.79, 0.57, and 0.74, respectively. The findings of this study suggest that integrating ML into robotic therapy may have the capacity to establish a personalized and streamlined clinical practice, leading to significant improvements in patients’ quality of life and the long-term sustainability of the healthcare system. Full article

Background: Exercise has shown beneficial effects on neuronal neuroplasticity; therefore, we want to analyze the influence of high-intensity interval training (HIIT) on neuroplasticity markers in post-stroke patients. Methods: A systematic review of RCTs including studies with stroke participants was conducted using the following databases (PubMed, LILACS, ProQuest, PEDro, Web of Science). Searches lasted till (20/11/2023). Studies that used a HIIT protocol as the main treatment or as a coadjutant treatment whose outcomes were neural plasticity markers were used and compared with other exercise protocols, controls or other kinds of treatment. Studies that included other neurological illnesses, comorbidities that interfere with stroke or patients unable to complete a HIIT protocol were excluded. HIIT protocol, methods to assess intensity, neuroplasticity markers (plasmatic and neurophysiological) and other types of assessments such as cognitive scales were extracted to make a narrative synthesis. Jadad and PEDro scales were used to assess bias. Results: Eight articles were included, one included lacunar stroke (less than 3 weeks) and the rest had chronic stroke. The results found here indicate that HIIT facilitates neuronal recovery in response to an ischemic injury. This type of training increases the plasma concentrations of lactate, BDNF and VEGF, which are neurotrophic and growth factors involved in neuroplasticity. HIIT also positively regulates other neurophysiological measurements that are directly associated with a better outcome in motor learning tasks. Conclusions: We conclude that HIIT improves post-stroke recovery by increasing neuroplasticity markers. However, a limited number of studies have been found indicating that future studies are needed that assess this effect and include the analysis of the number of intervals and their duration in order to maximize this effect. Full article