Movement Features Research Articles

Terra incognita of the uncontrolled manifold (UCM).

The review addresses the central concept of the uncontrolled manifold (UCM) hypothesis, which has become a major framework for analysis of performance-stabilizing motor synergies. The major goals are to summarize the status quo in the field and to ask new questions stimulating new studies. We focus on a few main questions: What is the UCM? What are the likely neural origins of the UCM? How is the UCM reflected in movement patterns? Are properties of the UCM similar in all directions? We contrast experience-based features of movements seen very soon after the movement initiation vs. those based on on-line sensory feedback signals. Further, we address a number of poorly explored issues such as the differences in characteristic times of processes within the UCM and orthogonal to the UCM space, the interplay between movement stability and optimality, the origin of preferred sharing patterns of performance variables across abundant sets of elements and of their inter-trial variability, problems with the UCM-based analysis in different spaces, and likely neurophysiological mechanisms contributing to the UCM formation. In particular, we focus on the UCM in spaces of hypothetical neural control variables, which we associate with the reciprocal and coactivation commands to the effectors. Analysis of performance-stabilizing synergies within the UCM framework in abundant spaces of kinetic, kinematic and electromyographic variables at the selected level of analysis may be practically useful. However, mapping findings in such studies onto neural control mechanisms has been challenging.

From Movement to Meaning: Real-time Detection of Behavioural Patterns in Human Psychology using Deep Learning and Time Series Sequence

Understanding human behaviour and psychology is complex and involves various fields, including cognitive science, neurology, and artificial intelligence (AI). In this study, we introduce a cutting-edge system that detects both manual (like hand gestures) and non-manual (such as facial expressions or body movements) features in real time, using advanced deep learning techniques and time-series analysis. This system translates these human movements into text, providing a powerful tool to analyse subtle behavioural and psychological signals. By processing real-time data, our system significantly advances psychology by detecting small expressions, involuntary gestures, and behaviour patterns often associated with psychological and neurological conditions. For instance, it can help identify repetitive actions or atypical gestures in people with autism spectrum disorder (ASD), which are critical for early diagnosis. It can also track signs of neurodegenerative diseases like Parkinson’s or Alzheimer’s, detecting tremors, posture changes, or facial expressions, and offer insights for early intervention

Embodiment of sexuality by Israeli Sign Language signers and hearing Hebrew speakers

AbstractLanguage is a key resource for speakers and signers to index different aspects of their social identities, such as their ethnicities and sexualities. Yet, for users of sign language – who exploit movements of the hands, face, head and torso for linguistic purposes – it is often assumed that any communicative movement of the body is part of a sign language rather than a general feature of the body's potential to communicate social meaning, shared by hearing and deaf individuals. In this study, we test this claim by comparing the movement features produced by gay and straight Israeli Sign Language signers to the gestural movements produced by gay and straight Hebrew speakers. The findings reveal that deaf gay signers and hearing gay gesturers exploit similar movements of the body. By incorporating the notion of embodiment into sign language sociolinguistics, we can better conceptualize the relationship between sign language and social identities.

TCEDN: A Lightweight Time-Context Enhanced Depression Detection Network.

The automatic video recognition of depression is becoming increasingly important in clinical applications. However, traditional depression recognition models still face challenges in practical applications, such as high computational costs, the poor application effectiveness of facial movement features, and spatial feature degradation due to model stitching. To overcome these challenges, this work proposes a lightweight Time-Context Enhanced Depression Detection Network (TCEDN). We first use attention-weighted blocks to aggregate and enhance video frame-level features, easing the model's computational workload. Next, by integrating the temporal and spatial changes of video raw features and facial movement features in a self-learning weight manner, we enhance the precision of depression detection. Finally, a fusion network of 3-Dimensional Convolutional Neural Network (3D-CNN) and Convolutional Long Short-Term Memory Network (ConvLSTM) is constructed to minimize spatial feature loss by avoiding feature flattening and to achieve depression score prediction. Tests on the AVEC2013 and AVEC2014 datasets reveal that our approach yields results on par with state-of-the-art techniques for detecting depression using video analysis. Additionally, our method has significantly lower computational complexity than mainstream methods.

Modified tai chi movement training based on sEMG and movement analysis on improving upper extremities motor function: a protocol for a clinical randomised controlled trial

IntroductionStroke survivors often face motor dysfunction, increasing fall risk. Lower extremity muscle weakness is a key factor affecting walking ability. Tai chi (TC) has been shown to improve muscle strength...

Mathematical Modeling Features of the Wheeled Vehicle Movement with an Automated Braking Control System

Mathematical Modeling Features of the Wheeled Vehicle Movement with an Automated Braking Control System

Embodiment of emotions in adolescents’ musical expression

Music has been actively studied from the perspectives of emotional expression and body movement, but not during adolescence. The current study addressed music as a forum for adolescent embodied emotion expression. Based on prior research, we hypothesised that adolescents would be able to differentiate between emotions in their music-related expressive body movements based on valence and arousal characteristics. Participants ( N = 60, 17 male, mean age 14.72 years) played djembe to express five basic emotions (happiness, tenderness, sadness, anger, fear) while body movements were motion captured. Correlations of movement features with emotion-regulation tendencies, as measured by the Emotion Regulation Questionnaire (ERQ), were additionally explored. Adolescents demonstrated great capacity to use all measured movement features to express emotions: movement speed, variance, area and length all differed significantly between emotions. In particular, the results confirm the hypothesised connection of high arousal to high speed and acceleration and further suggest that positive valence relates to wider area and longer performance. In addition, adolescents scoring high on cognitive reappraisal gave faster and more stable performances. We discuss creative body movement as part of youth emotional development.

Stress Assessment for Augmented Reality Applications Based on Head Movement Features.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Accurate recognition of human dynamic movement is essential for seamless human–machine interaction (HMI) across various domains. However, most of the existing methods are single‐modal movement recognition, which has inherent limitations, such as limited feature representation and instability to noise, which will affect its practical performance. To address these limitations, this article proposes a novel fusion approach that can integrate two biological signals, including electromyography (EMG) and bioelectrical impedance (BI). The fusion method combines EMG for capturing dynamic movement features and BI for discerning key postures representing discrete points within dynamic movements. In this method, the identification of key postures and their temporal sequences provide a guiding framework for the selection and weighted correction of probability prediction matrices in EMG‐based dynamic recognition. To verify the effectiveness of the method, six dynamic upper limb movements and nine key postures are defined, and a Universal Robot that can follow movements is employed for experimental validation. Experimental results demonstrate that the recognition accuracy of the dynamic movement reaches 96.2%, representing an improvement of nearly 10% compared with single‐modal signal. This study illustrates the potential of multimodal fusion of EMG and BI in movement recognition, with broad prospects for application in HMI fields.

Experimental investigation on the water-entry characteristics of the structure passing through the broken ice field

In this study, experiments are conducted on the process of a structure entering water under an ice-free environment and a broken ice field environment with three different broken ice densities based on a water-entry experimental platform. The influences of the broken ice field environment and different broken ice densities on the variations in the cavity morphology and movement features of water entry into the structure are investigated. The results show that the broken ice field environment postpones the surface closure of the cavity, leading to an increase in the size of the closed cavity, which, in turn, affects the cavity collapse process, slows down the overall collapse of the cavity, and is conducive to the cavity formation of the structure. In addition, when the density of broken ice is 30%, the influence of the broken ice field on the variations in the cavity morphology is not significant. When the density of broken ice increases to 70%, the expansion, cavity closure, and collapse are significantly different from those in an ice-free environment. Necking and deep closure occur. Some broken ice is drawn into the cavity, accelerating the cavity collapse. The effect of the broken ice field environment on the motion stability of the structure entering the water increases with an increase in broken ice density. When the density of broken ice increases to 50 and 70%, owing to the direct collision between the structure and broken ice, mutual forces and momentum transfer are generated. This results in a horizontal displacement of the trajectory of the structure, a sudden decrease in velocity, and an acceleration of velocity attenuation, ultimately affecting the motion stability and drag reduction characteristics of the structure.

Страх красоты

The article discusses the problem of rejecting the image of beauty in modern dance. It is indicated that the theme of despair, fear, violence and depression begins to dominate in modern dance since its inception in the 20s of the last century. The characteristic features of movements and plasticity in expressionist dance, in the school of M. Graham, are described: broken vertical, inwardly turned chest and feet, sharpness and convulsiveness of movements, blows aimed at oneself, etc. The article consistently examines the possible causes of this trend, such as technical ease and accessibility, lower energy cost of ‘sad dances’, as well as the opportunity to demonstrate originality, nonconformism, express social protest, etc. There is an opinion that the expression of pathological motives, negative feelings, fears in art contributes to the liberation from their destructive effect in real life. This approach is questioned, because there is also a reverse mechanism: as a result of viewing relevant plots (for example, scenes of violence), infection with emotions occurs, role models arise, and the thresholds of what is permissible decrease. Along with criticism of a number of trends in contemporary art, the importance of the concept of ugliness for culture is emphasized. Meeting one’s own infirmity and imperfection, be that physical, psychological or moral, plays an essential role in the spiritual development of a person. However, modern art, including the dance performances considered, follow the path of establishing ugliness as a new norm and cultivate the perverted reality of the ‘sick’ creativity of a disintegrating personality. The expression of beauty, on the contrary, is characteristic of an integral personality striving for perfection and praising life.

Detecting five-pattern personality traits using eye movement features for observing emotional faces.

The five-pattern personality traits rooted in the theory of traditional Chinese medicine (TCM) have promising prospects for clinical application. However, they are currently assessed using a self-report scale, which may have certain limitations. Eye tracking technology, with its non-intrusive, objective, and culturally neutral characteristics, has become a powerful tool for revealing individual cognitive and emotional processes. Therefore, applying this technology for personality assessment is a promising approach. In this study, participants observed five emotional faces (anger, happy, calm, sad, and fear) selected from the Chinese Facial Affective Picture System. Utilizing artificial intelligence algorithms, we evaluated the feasibility of automatically identifying different traits of the five-pattern personality traits from participants' eye movement patterns. Based on the analysis of five supervised learning algorithms, we draw the following conclusions: The Lasso feature selection method and Logistic Regression achieve the highest prediction accuracy for most of the traits (TYa, SYa, SYi, TYi). This study develops a framework for predicting five-pattern personality traits using eye movement behavior, offering a novel approach for personality assessment in TCM.

Construction of University Football Basic Teaching and Training System Based on Object Detection and Tracking Algorithm

Football, often referred to as soccer outside the UK and Europe, is a highly popular activity on many university campuses. As a result, football training programs are offered across many institutions, training students at a range of skill levels. Therefore, this paper investigates a deep learning (DL)-based algorithm for recognizing and tracking targets in sports training videos. The study specifically addresses those videos tailored to identifying small targets, by generating new multi-scale features and modifying anchor point generation rules. Experimental results demonstrate the algorithm's strong performance in tracking football targets. Compared to Histogram of Oriented Gradients, the DL-based model achieved a 29.58% increase in accuracy and a 39.68% decrease in error rates when recognizing football player movement features. This algorithm accurately locates the edge contours of a football player's movements, meaning that universities can, and should, actively reform football teaching and training to enhance teaching effectiveness by utilizing this powerful algorithm.

Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings.

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

Effort in Hindustani Dhrupad Singing: Using Interviews to Develop Effort Inference Models of Acoustic and Movement Features

Abstract This article sets out the results of research into effort in Dhrupad, a genre of Hindustani music. I examine gestural interactions with imaginary objects that vocalists outline when engaging with melodies. The study takes an embodied cognition stance and applies thematic analysis and inductive coding to original interviews with Dhrupad musicians in India, the UK, and the Netherlands. My findings demonstrate that from the singers’ cognitive perspectives, their hand movements are deeply connected to their voices. I also offer insights on the relationships between bodily movement, sound, and imagery that are used to inform the development of effort inference models.

Did Eustathius the Philosopher Become Eustathius of Sebastea? A Contribution to Federico Fatti’s Hypothesis

In 2009, Federico Fatti published a hypothesis that Eustathius the Philosopher, described by Eunapius, converted to Christianity and became bishop of Sebastea and master to Basil the Great. In my article I present Fatti’s hypothesis and solve three important problems that he left unclear: the place of birth of two Eustathiuses, the problem of Letter 35 by Julian and the mystery of the year 358, when Eustathius of Sebastea was already bishop and Eustathius the Philosopher is believed to have been sent by the emperor to Persia despite his Hellenic faith. When those issues that could challenge Fatti’s thesis have been clarified, his claim, I think, gains plausibility close to certainty. The identification of two Eustathiuses helps explain certain mysteries in the life of Eustathius the Philosopher and some peculiar features of the ascetic movement initiated by Eustathius of Sebastea. It is also a milestone in understanding the teaching of the Cappadocian Fathers as it provides us with a direct link between them and Neoplatonism and more specifically its Syrian branch initiated by Iamblichus.

Incorporating shape dependent power law in motion planning for drawing robots

Incorporating human natural features into the algorithmic functions of robots can enhance performance efficiency. One of the most popular features of human movements is the power law that defines the connection between movement speed and path curvature. To contribute to this area, by discussing how the power law can provide a reasonable balance between velocity and efficiency, we propose a novel method to design motion profiles based on the power law. The novelty of this solution lies in the adjustment approach for the power law. In this work, inspired by features of human hand movements, the overall curvature of non-circular shapes is considered as the shape-dependent criterion for motion planning. Also, a framework to apply the proposed approach to any open non-circular curvy contours is presented. To investigate the efficiency of the approach, we considered a simple drawing robot. The simulation and experimental results verify the efficacy of the proposed motion planning method.

Temporal localization of upper extremity bilateral synergistic coordination using wearable accelerometers.

The human upper extremity is characterized by inherent motor abundance, allowing a diverse array of tasks with agility and adaptability. Upper extremity functional limitations are a common sequela to Stroke, resulting in pronounced motor and sensory impairments in the contralesional arm. While many therapeutic interventions focus on rehabilitating the weaker arm, it is increasingly evident that it is necessary to consider bimanual coordination and motor control. Participants were recruited to two groups differing in age (Group 1 (n=10): 23.4 ±2.9 years, Group 2 (n=10): 55.9 ±10.6 years) for an exploratory study on the use of accelerometry to quantify bilateral coordination. Three tasks featuring coordinated reaching were selected to investigate the acceleration of the upper arm, forearm, and hand during activities of daily living (ADLs). Subjects were equipped with acceleration and inclination sensors on each upper arm, each forearm, and each hand. Data was segmented in MATLAB to assess inter-limb and intra-limb coordination. Inter-limb coordination was indicated through dissimilarity indices and temporal locations of congruous movement between upper arm, forearm, or hand segments of the right and left limbs. Intra-limb coordination was likewise assessed between upper arm-forearm, upper arm-hand, and forearm-hand segment pairs of the dominant limb. Acceleration data revealed task-specific movement features during the three distinct tasks. Groups demonstrated diminished similarity as task complexity increased. Groups differed significantly in the hand segments during the buttoning task, with Group 1 showing no coordination in the hand segments during buttoning, and strong coordination in reaching each button with the upper arm and forearm guiding extension. Group 2's dissimilarity scores and percentages of similarity indicated longer periods of inter-limb coordination, particularly towards movement completion. Group 1's dissimilarity scores and percentages of similarity indicated longer periods of intra-limb coordination, particularly in the coordination of the upper arm and forearm segments. The Expanding Procrustes methodology can be applied to compute objective coordination scores using accessible and highly accurate wearable acceleration sensors. The findings of task duration, angular velocity, and peak roll angle are supported by previous studies finding older individuals to present with slower movements, reduced movement stability, and a reduction of laterality between the limbs. The theory of a shift towards ambidexterity with age is supported by the finding of greater inter-limb coordination in the group of subjects above the age of thirty-five. The group below the age of thirty was found to demonstrate longer periods of intra-limb coordination, with upper arm and forearm coordination emerging as a possible explanation for the demonstrated greater stability.

Auxiliary diagnostic method of Parkinson’s disease based on eye movement analysis in a virtual reality environment

Eye movement dysfunction is one of the non-motor symptoms of Parkinson’s disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, there are only a few assistive methods available to help physicians conveniently and consistently assess patients suspected of having PD. To solve this problem, we proposed a novel visual behavioral analysis method using eye tracking to evaluate eye movement dysfunction in PD patients automatically. This method first provided a physician task simulation to induce PD-related eye movements in Virtual Reality (VR). Subsequently, we extracted eye movement features from recorded eye videos and applied a machine learning algorithm to establish a PD diagnostic model. Then, we collected eye movement data from 66 participants (including 22 healthy controls and 44 PD patients) in a VR environment for training and testing during visual tasks. Finally, on this relatively small dataset, the results reveal that the Support Vector Machine (SVM) algorithm has better classification potential.

Accelerometer-derived movement features as predictive biomarkers for muscle atrophy in neurocritical care: a prospective cohort study

BackgroundPhysical inactivity and subsequent muscle atrophy are highly prevalent in neurocritical care and are recognized as key mechanisms underlying intensive care unit acquired weakness (ICUAW). The lack of quantifiable biomarkers for inactivity complicates the assessment of its relative importance compared to other conditions under the syndromic diagnosis of ICUAW. We hypothesize that active movement, as opposed to passive movement without active patient participation, can serve as a valid proxy for activity and may help predict muscle atrophy. To test this hypothesis, we utilized non-invasive, body-fixed accelerometers to compute measures of active movement and subsequently developed a machine learning model to predict muscle atrophy.MethodsThis study was conducted as a single-center, prospective, observational cohort study as part of the MINCE registry (metabolism and nutrition in neurointensive care, DRKS-ID: DRKS00031472). Atrophy of rectus femoris muscle (RFM) relative to baseline (day 0) was evaluated at days 3, 7 and 10 after intensive care unit (ICU) admission and served as the dependent variable in a generalized linear mixed model with Least Absolute Shrinkage and Selection Operator regularization and nested-cross validation.ResultsOut of 407 patients screened, 53 patients (age: 59.2 years (SD 15.9), 31 (58.5%) male) with a total of 91 available accelerometer datasets were enrolled. RFM thickness changed − 19.5% (SD 12.0) by day 10. Out of 12 demographic, clinical, nutritional and accelerometer-derived variables, baseline RFM muscle mass (beta − 5.1, 95% CI − 7.9 to − 3.8) and proportion of active movement (% activity) (beta 1.6, 95% CI 0.1 to 4.9) were selected as significant predictors of muscle atrophy. Including movement features into the prediction model substantially improved performance on an unseen test data set (including movement features: R2 = 79%; excluding movement features: R2 = 55%).ConclusionActive movement, as measured with thigh-fixed accelerometers, is a key risk factor for muscle atrophy in neurocritical care patients. Quantifiable biomarkers reflecting the level of activity can support more precise phenotyping of ICUAW and may direct tailored interventions to support activity in the ICU. Studies addressing the external validity of these findings beyond the neurointensive care unit are warranted.Trial registrationDRKS00031472, retrospectively registered on 13.03.2023.