Characteristics Of Motor Research Articles

Social Cognition in Parkinson's Disease after Focused Ultrasound Subthalamotomy: A Controlled Study.

Social cognition (SC) encompasses a set of cognitive functions that enable individuals to understand and respond appropriately to social interactions. Although focused ultrasound subthalamotomy (FUS-STN) effectively treats Parkinson's disease (PD) clinical motor features, its impact and safety on cognitive-behavioral interactions/interpersonal awareness are unknown. This study investigated the effects of unilateral FUS-STN on facial emotion recognition (FER) and affective and cognitive theory of mind (ToM) in PD patients from a randomized sham-controlled trial (NCT03454425). Subjects performed SC evaluation before and 4 months after the procedure while still under blind assessment conditions. The SC assessment included the Karolinska Directed Emotional Faces task for FER, the Reading the Mind in the Eyes (RME) test for affective ToM, and The Theory of Mind Picture Stories Task (ToM PST) (order, questions, and total score) for cognitive ToM. The active treatment group showed anecdotal-to-moderate evidence of no worsening in SC after FUS-STN. Anecdotal evidence for an improvement was recognized in the SC score changes, from baseline to post-treatment, for the active treatment group compared with sham for the RME, ToM PST order, ToM PST total, FER total, and recognition of fear, disgust, and anger. This study provides the first evidence that unilateral FUS-STN does not impair social cognitive abilities, indicating that it can be considered a safe treatment approach for this domain in PD patients. Furthermore, the results suggest FUS-STN may even lead to some improvement in social cognitive outcomes, which should be considered as a preliminary finding requiring further investigation with larger samples sizes. © 2024 International Parkinson and Movement Disorder Society.

The effect of a trampoline exercise program on motor and behavioral characteristics of adolescents with autism spectrum disorder

Purpose The aim of this study is to evaluate the impact of a trampoline intervention program on motor and behavioral characteristics of adolescents with autism spectrum disorder (ASD). Design/methodology/approach Twenty-six male students aged 15–18 years with ASD participated in the study. They were equally divided into two groups of 13 students. The intervention group followed an individualized trampoline exercise program that was applied three times a week for 30 min for 12 weeks. Before the intervention program and after the end of this, the motor performance was evaluated through the recording of the total number of jumps. The behavioral characteristics were evaluated with the Behavior Problem Inventory questionnaire. Findings According to the findings, there were statistically significant differences between the intervention group and the control group across all variables. In conclusion, it seems that an intervention program with the use of a trampoline, adapted to the needs and capabilities of adolescents with ASD can improve their motor performance and behavioral characteristics. Originality/value The authors report on the impact of a trampoline exercise program on motor skills and behavioral traits in adolescents diagnosed with ASD. This is significant because our research focused on investigating the effects of a structured trampoline exercise program both on motor skills and behavioral characteristics. The findings contribute to the growing body of evidence supporting targeted exercise programs as effective interventions for addressing motor and behavioral challenges in adolescents with ASD. The authors believe the research aligns closely with the aims and scope of research in developmental disabilities, and the authors hope that it will make a meaningful impact on the field of autism research and intervention.

A steady-state and dynamic response model for working characteristics prediction of the electromechanical transmission system including eddy-current magnetic coupler

In this paper, a steady-state and dynamic response model is proposed to predict the working characteristics of the electromechanical transmission system including eddy-current magnetic couplers. Considering the influence of mechanical characteristics of motor and load device on the performance of eddy-current magnetic couplers, the system is divided into input and output for kinematic analysis. The expressions of electromagnetic torque versus speed are obtained through layer analysis of the eddy-current magnetic coupler during steady-state and then, the dynamic response model of the system is established by combining the kinematic analysis with the motor, the eddy-current magnetic coupler and three typical loads (constant torque load, quadratic rate load and constant power load). The dynamic characteristics during starting and speed-regulation of the system under constant torque loads are analyzed using the proposed model. The variations of the torque and speed with time of input and output are calculated by numerical integration. Finally, the speed and torque during starting and speed-regulation computed with the dynamic response model are compared with those obtained from the experimental results. The result shows a good agreement.

Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

Pain in Multiple System Atrophy: A Community-Based Survey.

Pain is a frequent yet poorly characterized symptom of multiple system atrophy (MSA). Understanding the factors influencing pain and its burden is crucial for improving the symptomatic treatment and quality of life of MSA individuals. This study aimed at assessing the prevalence, characteristics, and current treatment strategies for pain in MSA. A community-based, online survey was conducted from February to May 2023. Invitations were extended to MSA individuals and informal MSA caregivers through patient advocacies and social media. We included 190 persons with MSA and 114 caregivers. Eighty-seven percent of MSA individuals reported pain, which was more prevalent among women (odds ratio [OR]: 6.38 [95% confidence interval, CI: 1.27-32.08], P = 0.025) and low-income groups (OR: 5.02 [95% CI: 1.32-19.08], P = 0.018). Neck and shoulders (58%), back (45%), and legs (45%) were mostly affected. In the neck and shoulders, pain was associated with MSA core features, like orthostatic intolerance (OR: 4.80 [95% CI: 1.92-12.02], P = 0.001) and antecollis (OR: 3.24 [95% CI: 1.54-6.82], P = 0.002). Seventy-six percent of individuals experiencing pain received treatment, mostly nonsteroidal anti-inflammatory drugs (47%), acetaminophen (39%), and opioids (28%). Only 53% of respondents reported at least partial satisfaction with their current pain management. Pain mostly impacted work, household activities, and hobbies of MSA individuals, and caregivers' social activities. Pain is more prevalent than previously reported in MSA and particularly affects women and low-income groups. Despite its frequency, pain management remains suboptimal, highlighting an urgent therapeutic need, likely entailing an optimized management of MSA core motor and non-motor features. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Analysis and suppression of the negative effect of electromagnetic characteristics of wheel hub motor drive system on vehicle dynamics performance

Analysis and suppression of the negative effect of electromagnetic characteristics of wheel hub motor drive system on vehicle dynamics performance

Galvanic vestibular stimulation modulates EEG markers of voluntary movement in Parkinson’s disease

We recently showed that vestibular stimulation can produce a long-lasting alleviation of motor features in Parkinson’s disease. Here we investigated whether components of the motor related cortical response that are commonly compromised in Parkinson’s – the Bereitschaftspotential and mu-rhythm event-related desynchronization – are modulated by concurrent, low frequency galvanic vestibular stimulation (GVS) during repetitive limb movement amongst 17 individuals with idiopathic Parkinson’s disease. Relative to sham, GVS was favourably associated with higher amplitudes during the late and movement phases of the Bereitschaftspotential and with a more pronounced decrease in spectral power within the mu-rhythm range during finger-tapping. These data increase understanding of how GVS interacts with the preparation and execution of voluntary movement and give added impetus to explore its therapeutic effects on Parkinsonian motor features.

The Effect of a Wearable Assistive Trunk Exoskeleton on the Motor Coordination of People with Cerebellar Ataxia

The motor features of people with cerebellar ataxia suggest that locomotion is substantially impaired due to incoordination of the head, trunk, and limbs. The purpose of this study was to investigate how well a wearable soft passive exoskeleton worked for motor coordination in these patients. We used an optoelectronic system to examine the gait of nine ataxic people in three different conditions: without an exoskeleton and with two variants of the exoskeleton, one less and the other more flexible. We investigated kinematics using trunk ranges of motion, the displacement of the center of mass in the medio-lateral direction, and the parameters of mechanical energy consumption and recovery. Furthermore, we investigated the lower limb and trunk muscle coactivation. The results revealed a reduction of the medio-lateral sway of the center of mass, a more efficient behavior of the body in the antero-posterior direction, an energy expenditure optimization, a reduction of muscle coactivation and a better coordination between muscle activations. As a result, the findings laid the groundwork for the device to be used in the rehabilitation of individuals with cerebellar ataxia.

Explaining dopamine through prediction errors and beyond.

The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic dopamine observed in behaving animals. This Perspective helps to resolve some of the conflicting interpretations of dopamine that currently exist in the literature. We focus on the following three empirical challenges to the RPE theory of dopamine: why does dopamine (1) ramp up as animals approach rewards, (2) respond to sensory and motor features and (3) influence action selection? We argue that the prediction error concept, once it has been suitably modified and generalized based on an analysis of each computational problem, answers each challenge. Nonetheless, there are a number of additional empirical findings that appear to demand fundamentally different theoretical explanations beyond encoding RPE. Therefore, looking forward, we discuss the prospects for a unifying theory that respects the diversity of dopamine signaling and function as well as the complex circuitry that both underlies and responds to dopaminergic transmission.

Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor.

Introduction: Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Results: Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Conclusion: Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.

An Advanced Diagnostic Approach for Broken Rotor Bar Detection and Classification in DTC Controlled Induction Motors by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT Methodology

This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions.

Prediction and Dynamic Simulation Verification of Output Characteristics of Radial Piston Motors Based on Neural Networks

Radial piston motors are executive components in hydraulic systems, tasked with providing appropriate torque and speed according to load requirements in practical applications. The purpose of this study is to predict the output torque of radial piston hydraulic motors and confirm their suitable operating conditions. Efficiency determination experiments were conducted on physical models, yielding thirty sets of performance data. Torque (output torque) and mechanical efficiency from the experimental data were selected as prediction targets and fitted using two methods: multiple linear regression and neural networks. A dynamic simulation model was built using Adams2020 software to obtain theoretical torque values, enabling the verification of the alignment between the predicted values and simulation results. The results indicate that the error between the theoretical torque of the dynamic model and the physical experiments is 1.9%, with the error of the neural network predictions being within 2%. The dynamic simulation model can yield highly accurate theoretical torque values, providing a reference for the external load of hydraulic motors; additionally, neural networks offer accurate predictions of output torque, thus reducing experimental testing costs.

Light wavelength modulates search behavior performance in zebrafish

Visual systems have evolved to discriminate between different wavelengths of light. The ability to perceive color, or specific light wavelengths, is important as color conveys crucial information about both biotic and abiotic features in the environment. Indeed, different wavelengths of light can drive distinct patterns of activity in the vertebrate brain, yet what remains incompletely understood is whether distinct wavelengths can invoke etiologically relevant behavioral changes. To address how specific wavelengths in the visible spectrum modulate behavioral performance, we use larval zebrafish and a stereotypic light-search behavior. Prior work has shown that the cessation of light triggers a transitional light-search behavior, which we use to interrogate wavelength-dependent behavioral modulation. Using 8 narrow spectrum light sources in the visible range, we demonstrate that all wavelengths induce motor parameters consistent with search behavior, yet the magnitude of search behavior is spectrum sensitive and the underlying motor parameters are modulated in distinct patterns across short, medium, and long wavelengths. However, our data also establishes that not all motor features of search are impacted by wavelength. To define how wavelength modulates search performance, we performed additional assays with alternative wavelengths, dual wavelengths, and variable intensity. Last, we also tested blind larvae to resolve which components of wavelength dependent behavioral changes potentially include signaling from non-retinal photoreception. These findings have important implications as organisms can be exposed to varying wavelengths in laboratory and natural settings and therefore impose unique behavioral outputs.

Optimal Electric Motor Designs of Light Electric Vehicles: A Review

This paper summarizes the results of numerous studies aimed at improving the operating characteristics of electric motors used in light electric vehicles (LEVs). This review focuses on four types of electric motors that can be installed in the drive wheel rims of LEVs. Due to the availability of new magnetic materials and the use of advanced techniques for optimizing the design of electric motors, new motor topologies have emerged. The latest generation motors have been shown to be more efficient, have higher torque density, and generate less torque ripple. This paper indicates and discusses current trends in the topology of electric motors designed for LEV drives. In this context, the effectiveness of the proposed design modifications in terms of selected motor operational characteristics was assessed. The proposed new topologies were compared with commercial solutions, also in terms of the possibility of improving their operational parameters.

МЕТОДИКА РАСЧЕТА ЭНЕРГЕТИЧЕСКИХ ХАРАКТЕРИСТИК И ТОКА КОРОТКОГО ЗАМЫКАНИЯ ЭЛЕКТРИЧЕСКИХ МАШИН ОТКРЫТОГО ИСПОЛНЕНИЯ

The article is devoted to the consideration of issues related to the design of electric motors and the use of submersible electric machines in the marine environment on drilling rigs and platforms. The article describes the calculation of parameters and energy characteristics of asynchronous electric motors of submersible electric machines. The mathematical apparatus for calculating the energy characteristics and short-circuit current of asynchronous electric motors of electric machines of open design is given. Some characteristic features of calculating the energy characteristics and short-circuit current of asynchronous electric motors of electric machines of open design, taking into account the efficiency coefficient in relation to the useful power consumed and the total losses in them, are also considered.

"Exercise… to Me, It's Freedom": Motivation, Support, and Self-Management to Keep Physically Active with Parkinson's Disease: A Qualitative Study.

The benefits of exercise have been widely explored among people with Parkinson's (PwP). Exercise can improve non-motor (fatigue, pain, sleep, etc.) and motor features (balance, muscle strength, gait speed, etc.), maintain function, as well as prevent disease progression. Although the benefits are well known, PwP continue to show difficulty adhering to physical activity and exercise. This study aims to explore motivation to exercise, support, and self-management needs among people with Parkinson's, their family members, and physiotherapists. Purposeful and maximum-variation sampling methods (age, sex, geographical setting, and disease severity) were employed. PwP and family members were recruited through physiotherapy services and local support groups. Twelve semi-structured interviews with PwP and two group interviews, one with family members (n = 4) and one with physiotherapists (n = 5), were conducted. Interview guides were informed by patient-public input and a recent systematic review. Interviews were recorded, transcribed, and analysed using thematic analysis informed by the Grounded Theory methodology. Four common themes emerged: (1) The value of an intrinsic connection with exercise, for which there are challenges. A greater connection to exercise led to long-term adherence. (2) Adapting exercise to the needs and preferences of a person is essential. Preferred exercises and environments were mixed, with differences emerging between sexes. (3) Physiotherapists' aim to only maintain physical function led to frustration. Limited self-management opportunities, stigma, and dehumanisation were discussed. (4) Non-motor symptoms, stigma, fear, and determination as well as apathy, pain, and low mood were discussed. Exercise provided physical, emotional, and social rewards. Supports are necessary; however, challenges arise when PwP's motivations are mismatched to family members' and physiotherapists' goals. Co-created goals, tailored to their preferences, and exercise plans with supported self-management are recommended.

Gait Analysis and Magnetic Resonance Imaging Characteristics in Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavioral disorder (iRBD) can precede neurodegenerative diseases. There is an urgent need for biomarkers to aid early intervention and neuroprotection. The aim is to assess quantitative motor, cognitive, and brain magnetic resonance imaging (MRI) characteristics in iRBD patients. Thirty-eight polysomnography-confirmed iRBD patients and 28 age- and sex-matched healthy controls underwent clinical, cognitive, and motor functional evaluations, along with brain MRI. Motor tasks included nine-hole peg test, five-times-sit-to-stand test, timed-up-and-go test, and 4-meter walking test with and without cognitive dual task. Quantitative spatiotemporal gait parameters were obtained using an optoelectronic system. Brain MRI analysis included functional connectivity (FC) of the main resting-state networks, gray matter (GM) volume using voxel-based morphometry, cortical thickness, and deep GM and brainstem volumes using FMRIB's Integrated Registration and Segmentation Tool and FreeSurfer. iRBD patients relative to healthy subjects exhibited a poorer performance during the nine-hole peg test and five-times-sit-to-stand test, and greater asymmetry of arm-swing amplitude and stride length variability during dual-task gait. Dual task significantly worsened the walking performance of iRBD patients more than healthy controls. iRBD patients exhibited nonmotor symptoms, and memory, abstract reasoning, and visuospatial deficits. iRBD patients exhibited decreased FC of pallidum and putamen within the basal ganglia network and occipital and temporal areas within the visuo-associative network, and a reduced volume of the supramarginal gyrus. Brain functional alterations correlated with gait changes. Subtle motor and nonmotor alterations were identified in iRBD patients, alongside brain structural and functional MRI changes. These findings may represent early signs of neurodegeneration and contribute to the development of predictive models for progression to parkinsonism. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Gaps and Controversies in Catatonia as a Movement Disorder.

The term "catatonia" was introduced by German psychiatrist Karl Kahlbaum in 1874. Although historically tied to schizophrenia, catatonia exhibits a diverse range of phenotypes and has been observed in various medical and neuropsychiatric conditions. Its intrinsic movement characteristics and association with hypokinetic and hyperkinetic phenomenologies place catatonia within the purview of movement disorders. Despite the presence of catatonia in psychiatry literature for over 150 years, many gaps and controversies persist regarding its etiopathogenesis, phenomenology, diagnostic criteria, and treatment. The current versions of the International Classification of Diseases (ICD-11) and the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) require clinicians to identify any three signs of 15 (ICD-11) or 12 (DSM-5) for the diagnosis of catatonia. Catalepsy and waxy flexibility are the only motor features with high specificity for the diagnosis. We highlight the gaps and controversies in catatonia as a movement disorder, emphasize the lack of a clear definition, and discuss the inconsistencies in the description of various catatonic signs. We propose the exploration of a bi-axial classification framework similar to that used for dystonia and tremor to encourage the evaluation of underlying etiologies and to guide therapeutic decisions to improve the outcome of these patients. © 2024 International Parkinson and Movement Disorder Society.

Interpretable video-based tracking and quantification of parkinsonism clinical motor states

Quantification of motor symptom progression in Parkinson’s disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical “black-box” ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive kinematic dataset and developing an interpretable video-based framework that predicts high versus low PD motor symptom severity according to MDS-UPDRS Part III metrics. This data driven approach validated and robustly quantified canonical movement features and identified new clinical insights, not previously appreciated as related to clinical severity, including pinkie finger movements and lower limb and axial features of gait. Our framework is enabled by retrospective, single-view, seconds-long videos recorded on consumer-grade devices such as smartphones, tablets, and digital cameras, thereby eliminating the requirement for specialized equipment. Following interpretable ML principles, our framework enforces robustness and interpretability by integrating (1) automatic, data-driven kinematic metric evaluation guided by pre-defined digital features of movement, (2) combination of bi-domain (body and hand) kinematic features, and (3) sparsity-inducing and stability-driven ML analysis with simple-to-interpret models. These elements ensure that the proposed framework quantifies clinically meaningful motor features useful for both ML predictions and clinical analysis.

The Most Effective VAF Threshold for Extracting the Optimum Number of Synergies for Reaching Movement in a Two-Link Arm Model with Two DoF

Purpose: Muscle synergy is a motor feature composed of synergy patterns and activation coefficients. This study aimed to combine the two-link arm model with synergy patterns and muscle activation coefficients, which in turn leads to selecting the optimum number of synergies by changing the best Variability Account For (VAF) criterion. Materials and Methods: In this paper, signals were recorded from six arm muscles involved in arm-reaching movement while carrying a certain weight (w=700 g) by 20 subjects. The synergy pattern and activation coefficient matrices were calculated by using the Non-negative Matrix Factorization method (NNMF) and VAF criterion. Subsequently, to find the best VAF threshold, the output of signal preprocessing and NNMF’s output were done on Hill’s model. Results: Average VAF% for 20 subjects in the mentioned movement was 97.34±2.0%, and four numbers of synergies were determined. Conclusion: The results of the study suggest that the output of the W*H matrix (W and H are equal to the synergy pattern matrix and the activation coefficient matrix, in turn) had harmony with the output of the signal matrix recorded from all 20 subjects (output means the endpoint position and theta 1 and theta 2 angles) when they were performed as input on the two-link arm model. This harmony can be seen when choosing the best VAF critical threshold (value≥96%) via the aforementioned procedure. This harmony in turn contributes to exerting a positive influence on optimal extracting synergy patterns and describing the arm-reaching space more clearly.