Search Results (1,424)

To achieve precise in-hand manipulation and feedback control using soft robotic fingers, it is essential to accurately measure their deformable structures. In particular, estimating the three-dimensional shape of a soft finger under contact conditions is a critical challenge, as the deformation state directly affects manipulation reliability. However, nonlinear deformations and occlusions arising from interactions with external objects make the estimation difficult. To address these issues, we propose a soft finger structure that integrates small magnets and magnetic sensors inside the body, enabling the acquisition of rich deformation information in both contact and non-contact states. The design provides a 15-dimensional time-series signal composed of motor angles, motor currents, and magnetic sensor outputs as inputs for shape estimation. Built on the sensing signals, we propose a mode-selection-based learning approach that outputs multiple candidate shapes and selects the correct one. The proposed network predicts the three-dimensional positions of four external markers attached to the finger, which serve as a proxy representation of the finger’s shape. The network is trained in a supervised manner using ground-truth marker positions measured by a motion capture system. The experimental results under both contact and non-contact conditions demonstrate that the proposed method achieves an average estimation error of approximately 4 mm, outperforming conventional one-shot regression models that output coordinates directly. The integration of magnetic sensing is demonstrated to be able to enable accurate recognition of contact states and significantly improve stability in shape estimation. Full article

Background: Irisin, an exercise-induced myokine, has emerged as a potent neuroprotective factor, though a systematic synthesis of its role across neurological disorders is lacking. This review systematically evaluates clinical and preclinical evidence on irisin’s association with neurological diseases and its underlying mechanisms. Methods: Following PRISMA 2020 guidelines, a systematic search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library was conducted. The review protocol was prospectively registered in PROSPERO. Twenty-one studies were included, comprising predominantly preclinical evidence (n = 14), alongside clinical observational studies (n = 6), and a single randomized controlled trial (RCT) investigating irisin in cerebrovascular diseases, Parkinson’s disease (PD), Alzheimer’s disease (AD), and other neurological conditions. Eligible studies were original English-language research on irisin or FNDC5 and their neuroprotective effects, excluding reviews and studies without direct neuronal outcomes. Risk of bias was independently assessed using SYRCLE, the Newcastle–Ottawa Scale, and RoB 2, where disagreements between reviewers were resolved through discussion and consensus. Results were synthesized narratively, integrating mechanistic, pre-clinical, and clinical evidence to highlight consistent neuroprotective patterns of irisin across disease categories. Results: Clinical studies consistently demonstrated that reduced circulating irisin levels predict poorer outcomes. Lower serum irisin was associated with worse functional recovery and post-stroke depression after ischemic stroke, while decreased plasma irisin in PD correlated with greater motor severity, higher α-synuclein, and reduced dopamine uptake. In AD, cerebrospinal fluid irisin levels were significantly correlated with global cognitive efficiency and specific domain performance, and correlation analyses within studies suggested a closer association with amyloid-β pathology than with markers of general neurodegeneration. However, diagnostic accuracy metrics (e.g., AUC, sensitivity, specificity) for irisin as a standalone biomarker are not yet established. Preclinical findings revealed that irisin exerts neuroprotection through multiple mechanisms: modulating microglial polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotype, suppressing NLRP3 inflammasome activation, enhancing autophagy, activating integrin αVβ5/AMPK/SIRT1 signaling, improving mitochondrial function, and reducing neuronal apoptosis. Irisin administration improved outcomes across models of stroke, PD, AD, postoperative cognitive dysfunction, and epilepsy. Conclusions: Irisin represents a critical mediator linking exercise to brain health, with consistent neuroprotective effects across diverse neurological conditions. Its dual ability to combat neuroinflammation and directly protect neurons, demonstrated in preclinical models, positions it as a promising therapeutic candidate for future investigation. Future research must prioritize the resolution of fundamental methodological challenges in irisin measurement, alongside investigating pharmacokinetics and sex-specific effects, to advance irisin toward rigorous clinical evaluation. Full article

Background/objective: Despite the fact that almost 87% of children with Autism Spectrum Disorder (ASD) have physical coordination issues, motor skills are not the primary concern when ASD is diagnosed. An aquatic environment can provide multisensory stimuli that might assist these children; however, studies related to hydrotherapy with children with ASD have not yet examined whether this environment has an effect on balance and coordination. Methods: A control vs. research group examined the effect of a weekly, three-month hydrotherapy program on the balance and coordination abilities of male children and adolescents diagnosed with high-functioning ASD. Children (N = 22) between the ages of 6 and 17 years (mean: 8.4 ± 2.4), participated in this study. Each participant’s coordination and balance abilities were evaluated using the Movement Assessment Battery for Children-Second Edition (M-ABC-II). The initial evaluation (test one) was repeated (test two) after two months to establish improvement prior to intervention. The final evaluation (test three) was conducted at intervention termination. Individual functional goals were set for each patient using the Goal Attainment Scale (GAS). Results: No improvement was noted within the pre-intervention period (between tests one and two), yet there was a statistically significant improvement in the M-ABC-II Total Test score (p = 0.0133), in Manual Dexterity (p = 0.0181), and balance (p = 0.0053) post-intervention, between tests two and three. The mean GAS score for this study was 52.1, suggesting the achievement of prespecified functional goals. Conclusions: This study demonstrated a positive impact of a 12-week hydrotherapy program on balance and coordination and manual dexterity among children with ASD. A positive impact was also noted in patients’ individual functional abilities. Full article

To satisfy the stringent requirements of ultra-precision systems for high accuracy and rapid dynamic response, the chatter and system delay of voice coil motor (VCM) under operating conditions have become key bottlenecks restricting overall performance enhancement. To address these challenges, this paper proposes an adaptive convergence rate fractional-order sliding mode control strategy based on a Smith predictor (AFOSMC-SP). The strategy constructs a fractional-order sliding mode controller using an improved Oustaloup method, introduces a novel adaptive reaching law based on a saturation function with dynamic gain, and integrates a Smith predictor to compensate for the current-loop delay in the VCM system. This approach reduces algorithmic complexity and position-tracking error while enabling the adaptive adjustment of the system convergence rate to enhance robustness. In addition, the parameter boundary conditions and the global asymptotic stability of the closed-loop system are analyzed using Lyapunov stability theory. The simulation results show that, compared to conventional sliding mode control methods, the proposed AFOSMC-SP strategy provides superior parameter adaptability, higher tracking accuracy, and stronger suppression of external disturbances. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

Integral Sliding Mode Control (ISMC) is widely employed in motor position control systems due to its robustness against uncertainties. However, its control performance is critically dependent on the selection of the switching gain. Although Disturbance Observer-Based Control (DOBC) is commonly adopted as an effective alternative for uncertainty compensation, it may exhibit limitations when high gains are required, potentially leading to system instability. To address these issues, this study proposes a Radial Basis Function Neural Network (RBF-NN)-based supervisory learning approach designed to minimize switching gain requirements. The effectiveness of the proposed scheme is validated through comparative simulations and laboratory experiments, specifically under scenarios involving system parameter uncertainties and sinusoidal disturbances with unknown offsets. Both simulation and experimental results demonstrate the superior performance of the proposed RBF-NN approach in terms of switching gain reduction and tracking error norms compared to a conventional ISMC and a DOBC-based cascade P–PI controller. Full article

Droplet microfluidics enables high-throughput, compartmentalized reactions using minimal reagent volumes, but most implementations rely on precision-fabricated chips and external pumping systems that limit portability and accessibility. Here, we present a handheld vibrational droplet generator that repurposes a consumer electric toothbrush and a modified disposable pipette tip to produce nearly monodisperse water-in-oil droplets without microfluidic channels or syringe pumps. The device is powered by the toothbrush’s built-in motor and controlled by a simple 3D-printed adapter and adjustable counterweight that tune the vibration amplitude transmitted to the pipette tip. By varying the aperture of the pipette tip, droplets with diameters from ~100–300 µm were generated at rates of ~100 droplets s⁻¹. Image analysis revealed narrow size distributions with coefficients of variation below 5% in typical operating conditions. We further demonstrate proof-of-concept applications in digital loop-mediated isothermal amplification (LAMP) and microbiological colony-forming unit (CFU) assays. A commercial feline parvovirus (FPV) kit manufactured by Beyotime Biotechnology Co., Ltd. (Shanghai, China), three template concentrations yielded emulsified reaction droplets that remained stable at 65 °C for 45 min and produced distinct fractions of fluorescent-positive droplets, allowing estimation of template concentration via a Poisson model. In a second set of experiments, the device was used as a droplet-based spreader to dispense diluted Escherichia coli suspensions onto LB agar plates, achieving uniform colony distributions across the plate at different dilution factors. The proposed handheld vibrational generator is inexpensive, easy to assemble from off-the-shelf components, and minimizes dead volume and cross-contamination because only the pipette tip contacts the sample. Although the current prototype still exhibits device-to-device variability and moving droplets in open containers complicate real-time imaging, these results indicate that toothbrush-based vibrational actuation can provide a practical and scalable route toward “lab-in-hand” droplet assays in resource-limited or educational settings. Full article

Purpose: To evaluate short-term motor and sensory–motor outcomes following postoperative OT in children with IXT after strabismus surgery. Methods: This prospective before–after observational study included children with IXT who underwent bilateral lateral rectus recession and were referred for postoperative OT based on predefined clinical criteria. A structured 12-week OTplan was initiated approximately six months after surgery. Outcome measures included angle of deviation (prism diopters, PD), near point of convergence (cm), positive fusional vergence amplitudes (PD), and convergence amplitudes at distance and near (PD). Pre- and post-therapy changes were analysed using paired-samples t-tests with effect sizes calculated using Cohen’s d. Final postoperative alignment was additionally compared cross-sectionally between children who underwent OT and those managed without OT. Results: Eighty-eight children had complete paired motor and sensory–motor data and were included in the analyses. Changes in static ocular alignment were small, with mean residual deviation improving from −7.02 ± 6.91 PD to −5.22 ± 6.60 PD after OT (mean change +1.80 PD; p < 0.01; d ≈ 0.30). No significant difference in final postoperative alignment was observed between the OT and non-OT groups (p = 0.827). In contrast, marked improvements were observed in sensory–motor outcomes. Positive fusional vergence amplitude increased from 7.30 ± 8.33 PD to 22.19 ± 9.26 PD (p < 0.001; d ≈ 1.5). Distance convergence amplitude improved from 7.30 ± 8.33 PD to 22.19 ± 9.26 PD, and near convergence amplitude from 10.95 ± 12.50 PD to 33.29 ± 13.89 PD (both p < 0.001; d ≈ 1.5). Near point of convergence showed a modest but significant improvement. Conclusions: Postoperative OT was associated with substantial short-term improvements in sensory–motor function, particularly fusional and convergence capacities, while changes in static ocular alignment were small and of limited clinical relevance. These findings support the role of OT as a functional adjunct to surgery, aimed at enhancing binocular control and postoperative sensory–motor stability in children with IXT. Full article

This work introduces a logic resource-efficient Artificial Neural Network (ANN) controller for embedded control applications on low-density Field-Programmable Gate Array (FPGA) platforms. The proposed design relies on 32-bit fixed-point arithmetic and incorporates an online learning mechanism, enabling the controller to adapt to system variations while maintaining low hardware complexity. Unlike conventional artificial intelligence solutions that require high-performance processors or Graphics Processing Units (GPUs), the proposed approach targets platforms with limited logic, memory, and computational resources. The ANN controller was described using a Hardware Description Language (HDL) and validated via cosimulation between ModelSim and Simulink. A practical comparison was also made between Proportional-Integral-Derivative (PID) control and an ANN for motor position control. The results confirm that the architecture efficiently utilizes FPGA resources, consuming approximately 50% of the available Digital Signal Processor (DSP) units, less than 40% of logic cells, and only 6% of embedded memory blocks. Owing to its modular design, the architecture is inherently scalable, allowing additional inputs or hidden-layer neurons to be incorporated with minimal impact on overall resource usage. Additionally, the computational latency can be precisely determined and scales with $(16n+39)m+31$ clock cycles, enabling precise timing analysis and facilitating integration into real-time embedded control systems. Full article

Model predictive control (MPC) ensures stable motor operation provided that accurate motor parameters and state information are available. However, in certain environments, direct sensor measurement of rotor position and speed is infeasible, and sensorless methods are required to estimate the rotor position and speed. Sensorless methods utilizing a sliding mode observer (SMO) and a quadrature phase-locked loop (QPLL) are widely adopted, but it may encounter issues such as inaccurate motor parameters and delayed measurement results. To address these challenges, this paper proposes an integrated method that employs a nonlinear extended state observer (NLESO) to reduce observation delays in rotor position estimation. Additionally, a model reference adaptive system (MRAS)-based inductance observer is utilized to correct parameter inaccuracies. This combined approach achieves robust motor control with low delay. Simulation results validate the effectiveness and robustness of the proposed method. Full article

Objectives: Innovative, evidence-based interventions for developmental dyslexia (DD) are necessary. While traditional methods remain valuable, newer approaches, such as cognitive–motor training, show the potential to improve literacy skills for those with DD. Verbal Working Memory–Balance (VWM-B) is a novel cognitive–motor training program that has demonstrated positive effects on reading, cognitive functions, and motor skills in children with DD. This extension study explored the neural mechanisms of VWM-B through voxel-to-voxel intrinsic functional connectivity (FC) analysis in children with DD. Methods: Resting-state fMRI data from 16 participants were collected in a quasi-double-blind randomized clinical trial with control and experimental groups, pre- and post-intervention measurements, and 15 training sessions over 5 weeks. Results: The mixed ANOVA interaction was significant for the right and left postcentral gyrus, bilateral precuneus, left superior frontal gyrus, and left posterior division of the supramarginal and angular gyri. Decreased FC in the postcentral gyri indicates reduced motor task engagement due to automation following VWM-B training. Conversely, increased FC in the bilateral precuneus, left superior frontal gyrus, and left posterior divisions of the supramarginal and angular gyri suggests a shift of cognitive resources from motor tasks to the cognitive functions associated with VWM-B. Conclusions: In conclusion, the study highlights that cognitive–motor dual-task training is more effective than single-task cognitive training for improving cognitive and motor functions in children with DD, emphasizing the importance of postural control and automaticity in dyslexia. The trial for this study was registered on 8 February 2018 with the Iranian Registry of Clinical Trials (IRCT20171219037953N1). Full article

Brushless DC motors are widely used for their high power density and efficiency. However, sensorless control remains challenging due to the difficulty of accurate rotor position detection, especially at low speeds. This paper proposes a novel sensorless trapezoidal control method based on Maximum Likelihood Estimation (MLE) for rotor sector detection. Unlike conventional back-EMF zero-crossing techniques, the proposed method uses a statistical algorithm to generate a probability map from prior motor state data, enabling accurate rotor position estimation without sensors. The MLE method operates with a typical computation time of 50–100 $\mathrm{\mu }$s, offering a balanced tradeoff between speed and accuracy. It is significantly faster than Kalman filter-based approaches (200–1000 $\mathrm{\mu }$s) and comparable to observer-based methods (20–80 $\mathrm{\mu }$s), while being more robust than zero-crossing techniques (<5 $\mathrm{\mu }$s). This makes it a practical and cost-effective solution for applications demanding high efficiency and reliability, such as electric mobility systems. Full article

Background/Objectives: A review of the current literature does not provide a clear answer regarding the effectiveness of incorporating stretching exercises into warm-ups on performance and improving motor skills. The aim of this study was to compare the effects of a single application of sciatic neuromobilization and static stretching of the hamstring muscles on lower limb explosiveness, expressed by height of countermovement jump (CMJ) test. Methods: The study included 39 physically active men aged 20 to 26 (mean age 21.4 ± 2.2 years). Participants were randomly divided into 3 groups: 1. neuromobilization, 2. static stretching, 3. control group—no intervention. Immediately after the intervention, a CMJ test was performed. Jump height was measured at four timings: 1. before stretching (Pre), 2. immediately after (Post_0), 3. after 5 min (Post_5), 4. and after 10 min (Post_10). Results: Statistical analysis revealed a statistically significant difference in CMJ height between the neuromobilization and static groups and between the neuromobilization and control groups (p < 0.001). No statistically significant differences were observed between the static and control groups (p = 0.073). Post hoc comparisons revealed substantially higher vertical jump height in the neuromobilization group compared with the static group. Hedges’ g indicated a very large magnitude of effect, with values ranging from 3.73 to above 4.10. Conclusions: Neuromobilization induces short-term activation of lower limb muscles, resulting in increased explosive strength, whereas hamstrings static stretching of them does not positively impact short-term power generation. Full article

Objectives: The aim of this study was to investigate the effectiveness of Cryoflow cooling on forearm skin temperature and nerve conduction velocity (NCV) in normal subjects. Methods: Thirty male volunteers participated in this study, with a mean age of 20.8 ± 0.74 years. A Cryoflow hose with a nozzle was positioned approximately 10 cm from the forearm and scanned the anterior surface of the non-dominant forearm for 10 min, with temperatures adjusted to −10 °C. Participants’ average skin temperature was measured by using an infrared camera. Motor and sensory NCV for both the median and ulnar nerves were measured from both forearms. The dominant side served as a control side. The level of significance was set at p value ≤ 0.05. Results: Following treatment, the experimental group experienced a reduction in average skin temperature, dropping from 32.94 ± 1.11 °C to 16.92 ± 1.68 °C, while the control group showed no significant change. Both the median and ulnar nerves exhibited significant decreases in motor NCV (−10.37 m/s and −8.79 m/s, respectively), alongside slight increases in distal motor latency. Sensory NCV of the median and ulnar nerves decreased significantly (−5.20 m/s and −8.40 m/s, respectively), accompanied by increased onset latency. No significant changes were found in the control group. Conclusions: Cryoflow air-based cryotherapy to the forearm causes a substantial reduction in local skin temperature and significant slowing of peripheral nerve conduction. Both motor and sensory fibers of the median and ulnar nerves exhibited decreased conduction velocities and increased latencies following cooling. Full article

Background and Clinical Significance: Hemiplegia following a cerebrovascular accident (CVA) disrupts gait symmetry and efficiency, compromising functional independence. The integration of surface electromyography (sEMG) and inertial measurement units (IMU) enables quantitative assessment of muscle activation and segmental dynamics, providing objective data for therapeutic planning. Case presentation: A 57-year-old male with chronic right hemiplegia, eight years post-ischemic stroke of the left middle cerebral artery. The patient ambulated independently without assistive devices, exhibiting right lower-limb circumduction. Clinical assessment revealed the following scores: Barthel Index 85/100, Tinetti Performance-Oriented Mobility Assessment (POMA) 16/28, Timed Up and Go (TUG) test 13 s, and Modified Ashworth Scale (MAS) scores of 1 (upper limb) and 1+ (lower limb). Methods: Multichannel sEMG (Miotool 800^®, 8 channels) was recorded form the lumbar erectors, gluteus medius and maximus, vastus medialis, vastus intermedius, vastus lateralis, biceps femoris, tibialis anterior, medial gastrocnemius, and lateral gastrocnemius. Ag/AgCI electrodes were positioned according to SENIAM recommendations: sampling rate: 1000 Hz; band-pass filter: 20–500 Hz; notch filter: 60 Hz; normalization to %MVC. Simultaneously, IMU signals (Xsens DOT^®, 60 Hz) were collected from both ankles during slow, medium and fast walking (20 s each) and compared with a healthy control subject. Results: The patient exhibited reduced sEMG amplitude and increased peak irregularity on the affected side, particularly in the gluteus medius, tibialis anterior, and gastrocnemius, along with agonist desynchronication. IMU data revealed decreased range of motion and angular pattern irregularity, with inconsistent acceleration peaks in the right ankle compared to the control, confirming neuromuscular and kinematic asymmetry. Conclusions: The combined sEMG-IMU analysis identified deficits in selective motor control and propulsion on the affected hemibody, providing essential information to guide physiotherapeutic interventions targeting pelvic stability, dorsiflexion, and propulsive phase training, enabling objective follow-up beyond specialized laboratory settings. Full article