Muscle synergies are shared across fundamental subtasks in complex movements of skateboarding.

A common theory of motor control posits that movement is controlled by muscle synergies. However, the behavior of these synergies during highly complex movements remains largely unexplored. Skateboarding is a hardly researched sport that requires rapid motor control to perform tricks. The objectives of this study were to investigate three key areas: (i) whether motor complexity differs between skateboard tricks, (ii) the inter-participant variability in synergies, and (iii) whether synergies are shared between different tricks. Electromyography data from eight muscles per leg were collected from seven experienced skateboarders performing three different tricks (Ollie, Kickflip, 360°-flip). Synergies were extracted using non-negative matrix factorization. The number of synergies (NoS) was determined using two criteria based on the total variance accounted for (tVAF > 90% and adding an additional synergy does not increase tVAF > 1%). In summary: (i) NoS and tVAF did not significantly differ between tricks, indicating similar motor complexity. (ii) High inter-participant variability exists across participants, potentially caused by the low number of constraints given to perform the tricks. (iii) Shared synergies were observed in every comparison of two tricks. Furthermore, each participant exhibited at least one synergy vector, which corresponds to the fundamental 'jumping' task, that was shared through all three tricks.

An EEG channel selection method for motor imagery based on Fisher score and local optimization.

Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

Artificial and biological supports are different for pea plants.

Previous studies on the kinematics of pea plants' ascent and attach behavior have demonstrated that the signature of their movement varies depending on the kind of support. So far, these studies have been confined to artificial supports (e.g. wooden sticks). Little is known regarding the conditions under which pea plants could rely on biological supports (e.g. neighboring plants) for climbing toward the light. In this study, we capitalize on the 3D kinematic analysis of movement to ascertain whether pea plants scale their kinematics differently depending on whether they aim for artificial or biological support. Results suggest that biological support determines a smoother and more accurate behavior than that elicited by the artificial one. These results shed light on pea plants' ability to detect and classify the properties of objects and implement a movement plan attuned to the very nature of the support. We contend that such differences depend on the augmented multisensory experience elicited by the biological support.

Jump performance and movement quality in 7- to 15-year-old competitive alpine skiers: a cross-sectional study.

INTRODUCTION: Injury rates in competitive alpine skiing are high. With current methods, identifying people at risk is expensive and thus often not feasible at the youth level. The aims of this study were (1) to describe the jump performance and movement quality of youth competitive alpine skiers according to age and sex, (2) to compare the jump distance among skiers of different sexes and movement quality grades, and (3) to assess the inter-rater grading reliability of the qualitative visual movement quality classification of such jumps and the agreement between live and video-based post-exercise grading. MATERIALS AND METHODS: This cross-sectional study is based on an anonymized dataset of 301 7- to 15-year-old competitive alpine skiers. The skiers performed two-legged forward triple jumps, whereby the jump distance was measured, and grades were assigned by experienced raters from the frontal and sagittal perspectives depending on the execution quality of the jumps. Furthermore, jumps were filmed and ultimately rated post-exercise. Differences in jump distance between various groups were assessed by multivariate analyses of variance (MANOVAs). Reliability was determined using Kendall's coefficient of concordance. RESULTS: The jump distance was significantly greater in U16 skiers than in U11 skiers of both sexes and in skiers with good execution quality than in those with reduced or poor execution quality. Overall, jump distance in U16 skiers significantly differed between female (5.37 m with 95% CI [5.21, 5.53]) and male skiers (5.90 m with 95%CI [5.69, 6.10]). Slightly better inter-rater grading reliability was observed for video-based post-exercise (strong agreement) ratings than for live ratings (moderate agreement). CONCLUSION: In competitive alpine skiers aged 7 to 15 years, jump performance increases with age, and around puberty, sex differences start to manifest. Our results highlight the importance of evaluating both jump distance and movement quality in youth skiers. To improve test-retest reliability, however, a video-based post-exercise evaluation is recommended.

In youth competitive alpine skiers, jump performance and movement quality matter, and both should be trained and tested.A qualitative assessment of movement quality while jumping by experts is a highly scalable and cost-effective approach; however, to ensure sufficient test-retest reliability, the assessment criteria need to be standardised and an additional video-based post-exercise assessment is recommended.

Simulating worm feeding patterns with computational models.

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.

Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics.

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management.

BACKGROUND: Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture. This study aimed to develop and evaluate a machine learning model (i.e., 'BACK-to-MOVE') for NSLBP classification trained with expert clinical classification, spinal motion data from a standard video alongside patient-reported outcome measures (PROMs). METHODS: Synchronised video and three-dimensional (3D) motion data was collected during forward spinal flexion from 83 NSLBP patients. Two physiotherapists independently classified them as motor control impairment (MCI) or movement impairment (MI), with conflicts resolved by a third expert. The Convolutional Neural Networks (CNNs) architecture, HigherHRNet, was chosen for effective pose estimation from video data. The model was validated against 3D motion data (subset of 62) and trained on the freely available MS-COCO dataset for feature extraction. The Back-to-Move classifier underwent fine-tuning through feed-forward neural networks using labelled examples from the training dataset. Evaluation utilised 5-fold cross-validation to assess accuracy, specificity, sensitivity, and F1 measure. RESULTS: Pose estimation's Mean Square Error of 0.35 degrees against 3D motion data demonstrated strong criterion validity. Back-to-Move proficiently differentiated MI and MCI classes, yielding 93.98% accuracy, 96.49% sensitivity (MI detection), 88.46% specificity (MCI detection), and an F1 measure of .957. Incorporating PROMs curtailed classifier performance (accuracy: 68.67%, sensitivity: 91.23%, specificity: 18.52%, F1: .800). CONCLUSION: This study is the first to demonstrate automated clinical classification of NSLBP using computer vision and machine learning with standard video data, achieving accuracy comparable to expert consensus. Automated classification of NSLBP based on altered movement patters video-recorded during routine clinical examination could expedite personalised NSLBP rehabilitation management, circumventing existing healthcare constraints. This advancement holds significant promise for patients and healthcare services alike.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Motor learning in multijoint virtual arm movements with novel kinematics.

Humans move their hands toward precise positions, a skill supported by the coordination of multiple joint movements, even in the presence of inherent redundancy. However, it remains unclear how the central nervous system learns the relationship between redundant joint movements and hand positions when starting from scratch. To address this question, a virtual-arm reaching task was performed in which participants were required to move a cursor corresponding to the hand of a virtual arm to a target. The joint angles of the virtual arm were determined by the heights of the participants' fingers. The results demonstrated that the participants moved the cursor to the target straighter and faster in the late phase than they did in the initial phase of learning. This improvement was accompanied by a reduction in the amount of angular changes in the virtual limb joint, predominantly characterized by an increased reliance on the virtual shoulder joint as opposed to the virtual wrist joint. These findings suggest that the central nervous system selects a combination of multijoint movements that minimize motor effort while learning novel upper-limb kinematics.

Using a golf specific functional movement screen to predict golf performance in collegiate golfers.

Background: This study aims to examine the relationship between functional movements and golf performance using the Golf Specific Functional Movement Screen (GSFMS). Methods: This cross-sectional study included a total of 56 collegiate golfers (aged 20.89 ± 0.99 years, height of 174.55 ± 7.76 cm, and weight 68.48 ± 9.30 kg) who met the criteria, and were recruited from Hainan Normal University in June 2022. The participants' golf motor skills (1-yard putt, 10-yard putt, 25-yard chip, 130/100-yard set shot, driver, and 9-hole stroke play) were tested and the GSFMS (e.g., pelvic tilt, pelvic rotation, and torso rotation) was used. Results: There were significant weak or moderate correlations between the variables. Furthermore, a multiple linear regression analysis found that pelvic rotation and lower-body rotation abilities can significantly predict golf skill levels, which collectively explain 31.2% of the variance in golf skill levels among collegiate golfers (Adjusted R2 = 0.312, F = 2.663, p < 0.05). Standardised ß values indicate that pelvic rotation (ß = 0.398) has a more substantial impact on golf skill levels than lower-body rotation (ß = 0.315). Conclusions: This study found the weak to moderate correlations between the GSFMS and golf performance, and pelvic rotation and lower-body rotation abilities, thus predicting golf skills. Our findings provide novel insights into the relationship between functional abilities and comprehensive skill performance within the context of the Gray Cook's Movement Pyramid model, and provide theoretical support and practical reference for collegiate golf motor-skill learning and sports injury prevention.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke.

BACKGROUND: Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics. METHODS: This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ). RESULTS: At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (rSpearman=0.64) and to a lesser extent to changes in movement duration (rSpearman=0.51) while TDSM were very responsive to changes in movement duration (rSpearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (rSpearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations). CONCLUSIONS: Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke. TRIAL REGISTRATION: NCT01383512, https://clinicaltrials.gov/ , June 27, 2011.

Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers.

BACKGROUND: Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke. METHODS: Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables. RESULTS: Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048). CONCLUSION: The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy.

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner's reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.

Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.

Mechanism of the Pulvinus-Driven Leaf Movement: An Overview.

Leaf movement is a manifestation of plant response to the changing internal and external environment, aiming to optimize plant growth and development. Leaf movement is usually driven by a specialized motor organ, the pulvinus, and this movement is associated with different changes in volume and expansion on the two sides of the pulvinus. Blue light, auxin, GA, H+-ATPase, K+, Cl-, Ca2+, actin, and aquaporin collectively influence the changes in water flux in the tissue of the extensor and flexor of the pulvinus to establish a turgor pressure difference, thereby controlling leaf movement. However, how these factors regulate the multicellular motility of the pulvinus tissues in a species remains obscure. In addition, model plants such as Medicago truncatula, Mimosa pudica, and Samanea saman have been used to study pulvinus-driven leaf movement, showing a similarity in their pulvinus movement mechanisms. In this review, we summarize past research findings from the three model plants, and using Medicago truncatula as an example, suggest that genes regulating pulvinus movement are also involved in regulating plant growth and development. We also propose a model in which the variation of ion flux and water flux are critical steps to pulvinus movement and highlight questions for future research.

Wearable Motion Analysis System for Thoracic Spine Mobility With Inertial Sensors.

This study presents a wireless wearable portable system designed for the automatic quantitative spatio-temporal analysis of continuous thoracic spine motion across various planes and degrees of freedom (DOF). This includes automatic motion segmentation, computation of the range of motion (ROM) for six distinct thoracic spine movements across three planes, tracking of motion completion cycles, and visualization of both primary and coupled thoracic spine motions. To validate the system, this study employed an Inter-days experimental setting to conduct experiments involving a total of 957 thoracic spine movements, with participation from two representatives of varying age and gender. The reliability of the proposed system was assessed using the Intraclass Correlation Coefficient (ICC) and Standard Error of Measurement (SEM). The experimental results demonstrated strong ICC values for various thoracic spine movements across different planes, ranging from 0.774 to 0.918, with an average of 0.85. The SEM values ranged from 0.64° to 4.03°, with an average of 1.93°. Additionally, we successfully conducted an assessment of thoracic spine mobility in a stroke rehabilitation patient using the system. This illustrates the feasibility of the system for actively analyzing thoracic spine mobility, offering an effective technological means for non-invasive research on thoracic spine activity during continuous movement states.

Role of tumbling in bacterial scattering at convex obstacles.

Active propulsion, as performed by bacteria and Janus particles, in combination with hydrodynamic interaction results in the accumulation of bacteria at a flat wall. However, in microfluidic devices with cylindrical pillars of sufficiently small radius, self-propelled particles can slide along and scatter off the surface of a pillar, without becoming trapped over long times. This nonequilibrium scattering process has been predicted to result in large diffusivities, even at high obstacle density, unlike particles that undergo classical specular reflection. Here, we test this prediction by experimentally studying the nonequilibrium scattering of pusherlike swimmers in microfluidic obstacle lattices. To explore the role of tumbles in the scattering process, we microscopically tracked wild-type (run and tumble) and smooth-swimming (run only) mutants of the bacterium Escherichia coli scattering off microfluidic pillars. We quantified key scattering parameters and related them to previously proposed models that included a prediction for the diffusivity, discussing their relevance. Finally, we discuss potential interpretations of the role of tumbles in the scattering process and connect our work to the broader study of swimmers in porous media.

Three-dimensional motion analysis of pre- and postoperative thumb movement in trapeziometacarpal joint osteoarthritis-Comparison of arthrodesis and trapeziectomy with suspensionplasty.

Trapeziometacarpal osteoarthritis (TMC-OA) reduces the range of motion (ROM) of the thumb. However, the kinematic change achieved through surgical treatment remains unclear. Therefore, to quantify the kinematic change following TMC-OA surgery, we performed a three-dimensional motion analysis of the thumb using an optical motion capture system preoperatively and 1 year postoperatively in 23 patients with TMC-OA scheduled for arthrodesis (AD) or trapeziectomy with suspensionplasty (TS). Eighteen hands of nine healthy volunteers were also included as controls. Both procedures improved postoperative pain and Disability of the Arm, Shoulder and Hand scores, and AD increased pinch strength. The ROM of the base of the thumb was preserved in AD, which was thought to be due to the appearance of compensatory movements of adjacent joints even if the ROM of the TMC joint was lost. TS did not improve ROM. Quantifying thumb kinematic changes following TMC-OA surgery can improve our understanding of TMC-OA treatment and help select surgical procedures and postoperative assessment.

EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms.

Objective.The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact.Approach.We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms.Results.Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture.Significance.Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment.

Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapist-patient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients' training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts' knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-of-the-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.