UNASSIGNED: Attaining movement proficiency under various constraints is well-researched; of particular interest here is how conscious processing and self-consciousness influence learning and performance. Current research relevant to these variables e.g., reinvestment, tends to utilize quantitative methods and thus overlooks a potentially rich source of understanding. Therefore, the purpose of this study was to apply a qualitative approach to explore the cognitive processes and self-consciousness within a less practiced population of adults participating in a non-competitive leisure activity.
UNASSIGNED: To achieve this, eleven semi-structured interviews were conducted with adult women learning or relearning to ride a bicycle.
UNASSIGNED: Using thematic analysis, two distinct themes were evident and corroborated those illustrated in scales such as the movement-specific reinvestment scale. These themes included Conscious Processing, Recalling Experiences and Movement Analysis, in addition to Style of Movement, and Self-Consciousness. Anxiety also emerged as an additional and relevant theme to learning a new complex movement skill.
UNASSIGNED: The findings indicated that participating women attuned to their movements to the extent that there was a conscious attempt to control their movements, but less so regarding movements being altered by self-awareness. Whilst further research is required, qualitative methods provide a promising basis for exploring the cognitive process involved with the conscious process involved in learning.

KOJI AWARENESS is a newly developed self-rating whole-body movement assessment system that includes 11 domains and 22 tests. The primary aim of this study was to investigate the intersession reliability of KOJI AWARENESS, and the secondary aim was to determine whether a fixed bias existed between self-rating and external examiner rating. Fifty university students rated their movement ability in two separate sessions; an external examiner also rated the students\' movement ability. Participants were blinded to their scores at the first session as well as the external examiner\'s rating scores. The primary analysis included examining the intersession reliability of the total score with intraclass correlation coefficients (ICCs). ICC values were interpreted as follows: insufficient, < .7 and sufficient, ≥ .7. To achieve the secondary aim, Bland-Altman analysis was performed. ICC for the intersession reliability was .86 with a 95% confidence interval (CI) of .77 to .92 and a minimum detectable change (MDC) of 5.15. Bland-Altman analysis revealed fixed bias as the 95% CIs of the mean difference between the two different rating scores (-3.49 to -2.43 and -3.94 to -2.98 in the first and second sessions, respectively) did not include 0 in the data of each session. However, no proportional bias was identified because no statistically significant Pearson\'s correlation (P > .05) was noted between the means of the two methods and the mean difference between the two different methods in each session. This study identified that KOJI AWARENESS has sufficient intersession reliability among relatively young and healthy participants. External examiner rating tended to have lower total scores than self-rating; however, the bias was below the MDC and seemed not to be clinically significant.

During reaching, neurons in motor cortex exhibit complex, time-varying activity patterns. Though single-neuron activity correlates with movement parameters, movement correlations explain neural activity only partially. Neural responses also reflect population-level dynamics thought to generate outputs. These dynamics have previously been described as \"rotational,\" such that activity orbits in neural state space. Here, we reanalyze reaching datasets from male Rhesus macaques and find two essential features that cannot be accounted for with standard dynamics models. First, the planes in which rotations occur differ for different reaches. Second, this variation in planes reflects the overall location of activity in neural state space. Our \"location-dependent rotations\" model fits nearly all motor cortex activity during reaching, and high-quality decoding of reach kinematics reveals a quasilinear relationship with spiking. Varying rotational planes allows motor cortex to produce richer outputs than possible under previous models. Finally, our model links representational and dynamical ideas: representation is present in the state space location, which dynamics then convert into time-varying command signals.

Motor contagions refer to implicit effects induced by the observation of actions made by others on one\'s own actions. A plethora of studies conducted over the last two decades have demonstrated that both observed and predicted actions can induce various kinds of motor contagions in a human observer. However, motor contagions have always been investigated with regard to different features of an observed action, and it remains unclear whether the background environment in which an observed action takes place modulates motor contagions as well. Here, we investigated participant movements in an empirical hand steering task during which the participants were required to move a cursor through a visual channel after being presented with videos of an actor performing the same task. We manipulated the congruency between the actions shown in the video and the background channels and examined whether and how they affected the participants\' own movements. We observed a clear interaction between the observed action and its background. The movement time of the participants\' actions tended to increase or decrease depending on whether they observed a faster or slower movement, respectively, and these changes were amplified if the background was not congruent with the action contained within it. These results suggest that background information can modulate motor contagions in humans.

This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning\'s potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.

Understanding the movement patterns of an invasive species can be a powerful tool in designing effective management and control strategies. Here, we used a Bayesian multistate model to investigate the movement of two invasive carp species, silver carp (Hypophthalmichthys molitrix) and bighead carp (H. nobilis), using acoustic telemetry. The invaded portions of the Illinois and Des Plaines Rivers, USA, are a high priority management zone in the broader efforts to combat the spread of invasive carps from reaching the Laurentian Great Lakes. Our main objective was to characterize the rates of upstream and downstream movements by carps between river pools that are maintained by navigation lock and dam structures. However, we also aimed to evaluate the efficacy of the available telemetry infrastructure to monitor carp movements through this system. We found that, on a monthly basis, most individuals of both species remained within their current river pools: averaging 76.2% of silver carp and 75.5% of bighead carp. Conversely, a smaller proportion of silver carp, averaging 14.2%, and bighead carp, averaging 13.9%, moved to downstream river pools. Movements towards upstream pools were the least likely for both species, with silver carp at an average of 6.7% and bighead carp at 7.9%. The highest probabilities for upstream movements were for fish originating from the three most downstream river pools, where most of the population recruitment occurs. However, our evaluation of the telemetry array\'s effectiveness indicated low probability to detect fish in this portion of the river. We provide insights to enhance the placement and use of these monitoring tools, aiming to deepen our comprehension of these species\' movement patterns in the system.

BACKGROUND: Mixed reality (MR) is helpful in hand training for patients with stroke, allowing them to fully submerge in a virtual space while interacting with real objects. The recognition of individual finger movements is required for MR rehabilitation. This study aimed to assess the effectiveness of updated MR-board 2, adding finger training for patients with stroke.
METHODS: Twenty-one participants with hemiplegic stroke (10 with left hemiplegia and 11 with right hemiplegia; nine female patients; 56.7 ± 14.2 years of age; and onset of stroke 32.7 ± 34.8 months) participated in this study. MR-board 2 comprised a board plate, a depth camera, plastic-shaped objects, a monitor, a palm-worn camera, and seven gamified training programs. All participants performed 20 self-training sessions involving 30-min training using MR-board 2. The outcome measurements for upper extremity function were the Fugl-Meyer assessment (FMA) upper extremity score, repeated number of finger flexion and extension (Repeat-FE), the thumb opposition test (TOT), Box and Block Test score (BBT), Wolf Motor Function Test score (WMFT), and Stroke Impact Scale (SIS). One-way repeated measures analysis of variance and the post hoc test were applied for the measurements. MR-board 2 recorded the fingers\' active range of motion (AROM) and Dunnett\'s test was used for pairwise comparisons.
RESULTS: Except for the FMA-proximal score (p = 0.617) and TOT (p = 0.005), other FMA scores, BBT score, Repeat-FE, WMFT score, and SIS stroke recovery improved significantly (p < 0.001) during MR-board 2 training and were maintained until follow-up. All AROM values of the finger joints changed significantly during training (p < 0.001).
CONCLUSIONS: MR-board 2 self-training, which includes natural interactions between humans and computers using a tangible user interface and real-time tracking of the fingers, improved upper limb function across impairment, activity, and participation. MR-board 2 could be used as a self-training tool for patients with stroke, improving their quality of life.
BACKGROUND: This study was registered with the Clinical Research Information Service (CRIS: KCT0004167).

Low back pain (LBP) is a major contributor to lifting-related disabilities. To minimize the risk of back pain, emerging technologies known as lifting exoskeletons were designed to optimize lifting movements. However, it is currently unknown whether a minimally supportive exoskeleton can alter the lifting movement in people without LBP. This study aims to investigate if wearing a novel lightweight exoskeleton that minimally supports the back, hip, and knee can alter the lifting range of motion and movement variations in people without LBP. This study also aims to investigate if wearing this novel exoskeleton can result in a reliable between-day lifting movement. In two separate sessions (each one week apart), fourteen participants lifted a box (that weighed 10% of their body weight) ten times, once while wearing an exoskeleton and once while not wearing an exoskeleton. Wearing the novel exoskeleton during lifting produced moderate-high, test-retest reliability (Trunk: ICC3,1 = 0.89, 95% CI [0.67, 0.96], SEM = 9.34°; Hip: ICC3,1 = 0.63, 95% CI [0.22, 0.88], SEM = 2.57°; Knee: ICC3,1 = 0.61, 95% CI [0.23, 0.87], SEM = 2.50°). Wearing an exoskeleton significantly decreased the range of motion of the knee (F1,4 = 4.83, p = 0.031, ηp2 = 0.06). Additionally, wearing an exoskeleton significantly decreased hip (diff = 8.38, p = 0.045) and knee (diff = -8.57, p = 0.038) movement variability; however, wearing an exoskeleton did not decrease the movement variability of the body\'s trunk (diff = 0.60, p = 1.00). Therefore, minimally supported lifting through the use of exoskeletons can modify movement in people without LBP and produce reliable lifting movements. Wearing the novel exoskeleton is also desirable for monitoring lifting movements. Future studies should investigate the use of sensors and IMU to monitor lifting movement at work with the least amount of intrusion on an individual\'s movement.

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as \"brush teeth\" and \"brush hair\" are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results.

Hoof care providers are pivotal for implementing biomechanical optimizations of the musculoskeletal system in the horse. Regular visits allow for the collection of longitudinal, quantitative information (\"normal ranges\"). Changes in movement symmetry, e.g., after shoeing, are indicative of alterations in weight-bearing and push-off force production. Ten Warmblood show jumping horses (7-13 years; 7 geldings, 3 mares) underwent forelimb re-shoeing with rolled rocker shoes, one limb at a time (\"limb-by-limb\"). Movement symmetry was measured with inertial sensors attached to the head, withers, and pelvis during straight-line trot and lunging. Normalized differences pre/post re-shoeing were compared to published test-retest repeatability values. Mixed-model analysis with random factors horse and limb within horse and fixed factors surface and exercise direction evaluated movement symmetry changes (p < 0.05, Bonferroni correction). Withers movement indicated increased forelimb push-off with the re-shod limb on the inside of the circle and reduced weight-bearing with the re-shod limb and the ipsilateral hind limb on hard ground compared to soft ground. Movement symmetry measurements indicate that a rolled rocker shoe allows for increased push-off on soft ground in trot in a circle. Similar studies should study different types of shoes for improved practically relevant knowledge about shoeing mechanics, working towards evidence-based preventative shoeing.