Journal articles on the topic 'Stance prediction'
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LI, Yang, and Rui QI. "Heterogeneous Graph Contrastive Learning for Stance Prediction." IEICE Transactions on Information and Systems E105.D, no. 10 (October 1, 2022): 1790–98. http://dx.doi.org/10.1587/transinf.2022edp7065.
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Kamble, Aditya, Prathamesh Badgujar, Anuj Kadam, Dhruv Shah, and A. J. Kadam. "Stance Prediction of Tweets on Farmers Protests in India." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4288–95. http://dx.doi.org/10.22214/ijraset.2022.43070.
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Abstract: Protests are an integral part of democracy and are a vital tool for the general public to convey their demands and/or discontentment to the ruling government. As voters return to term with any new rules, there are an increasing range of protests everywhere in the world for numerous socio-political reasons. With the advancement of technology, there has additionally been an exponential rise within the use of social media for the exchange of data and ideas. During this research, knowledge was gathered from the web site “twitter.com”, regarding farmers’ protest to know the feelings that the public shared on a global level. Sadly now since the Farm Laws are repealed, we have a tendency to aim to use this knowledge to know the general public stance on these laws, and whether or not it affected the government’s decision. This paper proposes a stance prediction deep learning model achieved after fine tuning the well known ULMFiT (Universal Language Model Fine-tuning) model by Howard and Ruder. Categories to be classified into are For (F), Against (A) and Neutral (N). Proposed model achieved an F1 score of 0.67 on our training and test data, which is essentially a labeled subset of the actual data. Keywords: Dataset, ULMFiT, deep learning, text classification, Language Model (LM)
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Huang, Kuo-Yu, Hen-Hsen Huang, and Hsin-Hsi Chen. "HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 13045–54. http://dx.doi.org/10.1609/aaai.v35i14.17542.
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Argument structure elaborates the relation among claims and premises. Previous works in persuasiveness prediction do not consider this relation in their architectures. To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural network model, called heterogeneous argument attention network (HARGAN). By jointly training on the persuasiveness and stance of the replies, our model achieves the state-of-the-art performance on the ChangeMyView (CMV) dataset for the persuasiveness prediction task. Experimental results show that the graph setting enables our model to aggregate information across multiple paragraphs effectively. In the meanwhile, our stance prediction auxiliary task enables our model to identify the viewpoint of each party, and helps our model perform better on the persuasiveness prediction.
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Simaki, Vasiliki, Carita Paradis, and Andreas Kerren. "A two-step procedure to identify lexical elements of stance constructions in discourse from political blogs." Corpora 14, no. 3 (November 2019): 379–405. http://dx.doi.org/10.3366/cor.2019.0179.
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The Brexit Blog Corpus (bbc) is a collection of texts extracted from political blogs, which, in a recent study, was annotated according to a cognitive–functional stance framework by two independent annotators (Annotator A and B) using semantic criteria ( Simaki et al., 2017 ). The goal was to label the stance or stances taken based on the overall meaning of a set of utterances. The annotators were not instructed to identify the lexical forms that were used to express the stances. In this study, we make use of those stance-labelled utterances as a springboard to approach stance-taking in text from the opposite point of view, namely from how stance is realised through language. Our aim is to provide a description of the specific lexical elements used to express six stance categories (i.e., contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty). To this end, we followed a two-step experimental procedure. First, we performed a quantitative analysis of the stance-labelled utterances in order to identify the lexical realisations of each stance category. Second, we carried out a meta-annotation of the data. Annotator B was instructed to single out the actual lexical forms of the constructions that triggered his semantic stance category decisions. This meta-annotation procedure made it possible for us to sift out the most salient lexical realisations of the constructions of each of the six category types on the basis of the qualitative assessments made by Annotator B. We then compared the results of the quantitative and the qualitative approaches, and we present a list of shared stance expressions for each stance category type.
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Bhati, Piali, Theodore C. K. Cheung, Gobika Sithamparanathan, and Mark A. Schmuckler. "Striking a balance in sports: the interrelation between children's sports experience, body size, and posture." AIMS Neuroscience 9, no. 2 (2022): 288–302. http://dx.doi.org/10.3934/neuroscience.2022016.
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<abstract> <p>This study investigated the relation between sports participation, body size, and postural control in children between 3 and 11 years of age. To explore this question, children's body sway was measured across multisensory conditions manipulating visual input (the presence versus absence of visual information) and proprioceptive input (varying stance widths), with postural sway in these conditions then related to reports of children's sports participation, and anthropometric measures. Corroborating well-known findings, postural sway was systematically influenced by multisensory factors, with the removal of visual information and narrower stance widths decreasing postural stability. Of more novelty, postural sway in the most stable stance, but without vision, was significantly predicted by measures of sports participation and body size variables, with these factors contributing independently to this prediction. Moreover, the impact on postural sway of having visual input, relative to removing visual input in unstable stances, was significantly predicted by sports participation in activities stressing changing stances and bases of support (e.g., dance, martial arts). Generally, these findings support multisensory and dynamic systems theories of perceptual-motor behavior, and also support sports specificity effects in assessments of the relation between posture and sports.</p> </abstract>
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Simaki, Vasiliki, Eleni Seitanidi, and Carita Paradis. "Evaluating stance annotation of Twitter data." Research in Corpus Linguistics 11, no. 1 (2022): 53–80. http://dx.doi.org/10.32714/ricl.11.01.03.
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Taking stance towards any topic, event or idea is a common phenomenon on Twitter and social media in general. Twitter users express their opinions about different matters and assess other people’s opinions in various discursive ways. The identification and analysis of the linguistic ways that people use to take different stances leads to a better understanding of the language and user behaviour on Twitter. Stance is a multidimensional concept involving a broad range of related notions such as modality, evaluation and sentiment. In this study, we annotate data from Twitter using six notional stance categories ––contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty––¬¬ following a comprehensive annotation protocol including inter-coder reliability measurements. The relatively low agreement between annotators highlighted the challenges that the task entailed, which made us question the inter-annotator agreement score as a reliable measurement of annotation quality of notional categories. The nature of the data, the difficulty of the stance annotation task and the type of stance categories are discussed, and potential solutions are suggested
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Ahmad, Muhammad, Muhammad Asim Mahmood, and Ammara Farukh. "Use of Modals as Stance Markers: A Corpus-Based Study on Pakistani English Newspaper Editorials." Asia Pacific Media Educator 30, no. 1 (June 2020): 108–25. http://dx.doi.org/10.1177/1326365x20945424.
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This study analyses the use of modals as stance markers in newspaper editorials. Corpora of the study comprised of 500 editorials published in Pakistani English newspapers, that is, The Daily Dawn and The Daily News (250 editorials from each newspaper) which were analysed with the help of AntConc 3.4.4.0. Results show Pakistani editorial writers use all types of modals (i.e., prediction, possibility, necessity, modal adverbs, reporting verbs, knowledge verbs and generic phrases). The editorial writers use these modals to mark personal stance while commenting, reporting or informing about the state of affairs. Among these modals the use of prediction markers is the most frequent which indicates that prediction is a characteristic feature as well as function in the said newspaper editorials. Results also reveal that there is a difference in the use of modal adverbs in the editorials of both newspapers. In addition, the results reveal that the editorial writers of The Daily Dawn make less use of prediction markers as compared to the editorial writers of The Daily News. The study creates awareness of the stance of editorial writers’ and how it might affect readers’ opinions.
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Wang, Heyuan, Tengjiao Wang, and Yi Li. "Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 971–78. http://dx.doi.org/10.1609/aaai.v34i01.5445.
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Investment messages published on social media platforms are highly valuable for stock prediction. Most previous work regards overall message sentiments as forecast indicators and relies on shallow features (bag-of-words, noun phrases, etc.) to determine the investment opinion signals. These methods neither capture the time-sensitive and target-aware characteristics of stock investment reviews, nor consider the impact of investor's reliability. In this study, we provide an in-depth analysis of public stock reviews and their application in stock movement prediction. Specifically, we propose a novel framework which includes the following three key components: time-sensitive and target-aware investment stance detection, expert-based dynamic stance aggregation, and stock movement prediction. We first introduce our stance detection model named MFN, which learns the representation of each review by integrating multi-view textual features and extended knowledge in financial domain to distill bullish/bearish investment opinions. Then we show how to identify the validity of each review, and enhance stock movement prediction by incorporating expert-based aggregated opinion signals. Experiments on real datasets show our framework can effectively improve the performance of both investment opinion mining and individual stock forecasting.
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Aftab, Zohaib, and Rizwan Shad. "Estimation of gait parameters using leg velocity for amputee population." PLOS ONE 17, no. 5 (May 13, 2022): e0266726. http://dx.doi.org/10.1371/journal.pone.0266726.
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Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.
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Su, Binbin, and Elena M. Gutierrez-Farewik. "Gait Trajectory and Gait Phase Prediction Based on an LSTM Network." Sensors 20, no. 24 (December 12, 2020): 7127. http://dx.doi.org/10.3390/s20247127.
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Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.
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Zhu, Lixing, Yulan He, and Deyu Zhou. "Neural opinion dynamics model for the prediction of user-level stance dynamics." Information Processing & Management 57, no. 2 (March 2020): 102031. http://dx.doi.org/10.1016/j.ipm.2019.03.010.
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Huang, Weidong, Yuan Wang, Jinyuan Yang, and Yijun Xu. "Stance Detection Based on User Feature Fusion." Computational Intelligence and Neuroscience 2022 (March 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/5738404.
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Rapid development of the Internet has contributed to the widespread adoption of social network platforms. Network media plays an important role in the process of public opinion dissemination and bears significant social responsibility. Public opinion mining is of great significance for online media to improve the quality of content provision and enhance media credibility. How to make full use of user-generated content is the key to improving the accuracy of position detection tasks. In this paper, we proposed a stance detection model based on user feature fusion by using comments of netizens in false news events on Weibo as research content. The method of feature fusion is adopted to integrate vectors including user sentiment, cognitive features, and text feature at the feature layer for model training and position prediction. The model is evaluated on a dataset of related microblog comments in false news. The result shows that our proposed method has a certain improvement in the effect of stance detection.
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Hudson, Matthew, Katrina L. McDonough, Rhys Edwards, and Patric Bach. "Perceptual teleology: expectations of action efficiency bias social perception." Proceedings of the Royal Society B: Biological Sciences 285, no. 1884 (August 8, 2018): 20180638. http://dx.doi.org/10.1098/rspb.2018.0638.
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Primates interpret conspecific behaviour as goal-directed and expect others to achieve goals by the most efficient means possible. While this teleological stance is prominent in evolutionary and developmental theories of social cognition, little is known about the underlying mechanisms. In predictive models of social cognition, a perceptual prediction of an ideal efficient trajectory would be generated from prior knowledge against which the observed action is evaluated, distorting the perception of unexpected inefficient actions. To test this, participants observed an actor reach for an object with a straight or arched trajectory on a touch screen. The actions were made efficient or inefficient by adding or removing an obstructing object. The action disappeared mid-trajectory and participants touched the last seen screen position of the hand. Judgements of inefficient actions were biased towards the efficient prediction (straight trajectories upward to avoid the obstruction, arched trajectories downward towards the target). These corrections increased when the obstruction's presence/absence was explicitly acknowledged, and when the efficient trajectory was explicitly predicted. Additional supplementary experiments demonstrated that these biases occur during ongoing visual perception and/or immediately after motion offset. The teleological stance is at least partly perceptual, providing an ideal reference trajectory against which actual behaviour is evaluated.
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Alipasa, Clark Dominic Lilia. "Attitudinal/Stance Lexical Bundles in Secondary School Students’ Narratives: A Comparative Analysis of Native and Non-Native Speakers’ Written Register." International Multidisciplinary Research Journal 2, no. 2 (June 19, 2020): 72–80. http://dx.doi.org/10.54476/iimrj368.
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Analyzing multi-word expressions aids in understanding how native and non-native English speakers in secondary schools differ in expressing their attitude and stance, especially in written compositions. Thus, this study sought to compare and analyze the attitudinal/stance lexical bundles used by students in their narratives, specifically in terms of epistemicity, desire, obligation/directive, intention/prediction, and ability to conclude which between the native and non-native speakers used attitudinal/stance lexical bundles more frequently. Results showed that non-native English learners demonstrated a greater number of attitudinal/stance lexical bundles compared to their native counterparts, except in terms of desire. Non-native speakers employed more indirect and impersonal variations, unlike the native speakers whose expressions were more direct and straightforward. In line with these findings, further studies on the implications of the observed rarity and higher frequency of attitudinal/stance bundles use by non-native learners are hereby recommended.
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Rabin, Ely, Simone B. Bortolami, Paul DiZio, and James R. Lackner. "Haptic Stabilization of Posture: Changes in Arm Proprioception and Cutaneous Feedback for Different Arm Orientations." Journal of Neurophysiology 82, no. 6 (December 1, 1999): 3541–49. http://dx.doi.org/10.1152/jn.1999.82.6.3541.
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Postural sway during quiet stance is attenuated by actively maintained contact of the index finger with a stationary surface, even if the level of applied force (<1 N) cannot provide mechanical stabilization. In this situation, changes in force level at the fingertip lead changes in center of foot pressure by ∼250 ms. These and related findings indicate that stimulation of the fingertip combined with proprioceptive information about the hand and arm can serve as an active sensor of body position relative to the point of contact. A geometric analysis of the relationship between hand and torso displacement during body sway led to the prediction that arm and hand proprioceptive and finger somatosensory information about body sway would be maximized with finger contact in the plane of body sway. Therefore, the most postural stabilization should be possible with such contact. To test this analysis, subjects touched a laterally versus anteriorly placed surface while in each of two stances: the heel-to-toe tandem Romberg stance that reduces medial-lateral stability and the heel-to-heel, toes-outward, knees-bent, “duck stance” that reduces fore-aft stability. Postural sway was always least with finger contact in the unstable plane: for the tandem stance, lateral fingertip contact was significantly more effective than frontal contact, and, for the duck stance, frontal contact was more effective than lateral fingertip contact. Force changes at the fingertip led changes in center of pressure of the feet by ∼250 ms for both fingertip contact locations for both test stances. These results support the geometric analysis, which showed that 1) arm joint angles change by the largest amount when fingertip contact is maintained in the plane of greatest sway, and 2) the somatosensory cues at the fingertip provide both direction and amplitude information about sway when the finger is contacting a surface in the unstable plane.
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Jeka, John, Tim Kiemel, Robert Creath, Fay Horak, and Robert Peterka. "Controlling Human Upright Posture: Velocity Information Is More Accurate Than Position or Acceleration." Journal of Neurophysiology 92, no. 4 (October 2004): 2368–79. http://dx.doi.org/10.1152/jn.00983.2003.
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The problem of how the nervous system fuses sensory information from multiple modalities for upright stance control remains largely unsolved. It is well established that the visual, vestibular, and somatosensory modalities provide position and rate (e.g., velocity, acceleration) information for estimation of body dynamics. However, it is unknown whether any particular property dominates when multisensory information is fused. Our recent stochastic analysis of postural sway during quiet stance suggested that sensory input provides more accurate information about the body's velocity than its position or acceleration. Here we tested this prediction by degrading major sources of velocity information through removal/attenuation of sensory information from vision and proprioception. Experimental measures of postural sway were compared with model predictions to determine whether sway behavior was indicative of a deficit in velocity information rather than position or acceleration information. Subjects stood with eyes closed on a support surface that was 1) fixed, 2) foam, or 3) sway-referenced. Six measures characterizing the stochastic structure of postural sway behaved in a manner consistent with model predictions of degraded velocity information. Results were inconsistent with the effect of degrading only position or acceleration information. These findings support the hypothesis that velocity information is the most accurate form of sensory information used to stabilize posture during quiet stance. Our results are consistent with the assumption that changes in sway behavior resulting from commonly used experimental manipulations (e.g., foam, sway-referencing, eyes closed) are primarily attributed to loss of accurate velocity information.
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Simaki, Vasiliki, Carita Paradis, and Andreas Kerren. "Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis." ICAME Journal 42, no. 1 (March 1, 2018): 133–66. http://dx.doi.org/10.1515/icame-2018-0007.
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AbstractThis paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to identify features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledgeanduncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features, such as punctuation, words and grammatical categories that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine stance similarities in the data set. We show that among the six stance categories in the corpus,contrarietyandnecessityare the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances.necessityhas longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters.
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Gales, Tammy. "The stance of stalking: a corpus-based analysis of grammatical markers of stance in threatening communications." Corpora 10, no. 2 (August 2015): 171–200. http://dx.doi.org/10.3366/cor.2015.0073.
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Threats made by stalkers have tremendous negative effects on the more than one million victims who receive them every year – from experiencing ‘psychological terrorism’ to undergoing physical harm ( Burgess and Marchetti, 2009 ). However, stalking, a criminal offence within the law, is ill-defined and difficult to prosecute, since the victim of stalking must demonstrate the stalker's intent to intimidate or cause ‘substantial emotional distress’ ( Black et al., 1990 : 717). Linguistically, such indicators of emotion and intent are manifested by markers of ‘stance’, a speaker/writer's culturally organised feelings, judgments or assessments about a recipient or proposition ( Biber et al., 1999 ). Through a corpus analysis of 397 authentic threats, I examine variation in the manifestation and function of overt grammatical markers of stance (adverbials, modals, and that- and to-complement clauses) between threats to stalk, harass and defame. Specifically, certainty verbs + that-complement clause constructions and prediction modals occurred at a significantly higher rate (p<0.001) in stalking threats – those in which the victims reported feeling intense fear or distress. In addition, strong co-occurrence patterns were found between these stance markers and pronouns. And, when using a qualitative social constructionist approach to examine distinctions in stance meaning and reveal underlying functional patterns, ( Precht, 2003 : 255), three distinctive functional patterns were revealed with the trigrams ‘I will be’, ‘I will have’ and ‘you know that’, wherein the stalking threatener is in complete volitional control of his or her own actions, demonstrates possession over the victim or an object related to the threat, and accuses the victim of a behaviour thought to be wrong, respectively. Therefore, while it has been demonstrated that there is not a one-to-one correspondence between linguistic markers and threateners' actions ( Gales, 2010 ; and Lord et al., 2008 ), through a closer examination of overt grammatical markers of stance – in combination with social psychology theories connecting speaker role, power and fear (e.g., Burgess and Marchetti, 2009 ) – linguists can help hone an understanding of stance in stalking threats and potentially contribute to the demonstration of a victim's claim of feeling fear.
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Keijsers, N. L. W., N. M. Stolwijk, G. J. Renzenbrink, and J. Duysens. "Prediction of walking speed using single stance force or pressure measurements in healthy subjects." Gait & Posture 43 (January 2016): 93–95. http://dx.doi.org/10.1016/j.gaitpost.2015.09.027.
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Lotz, J. C., E. J. Cheal, and W. C. Hayes. "Fracture Prediction for the Proximal Femur Using Finite Element Models: Part I—Linear Analysis." Journal of Biomechanical Engineering 113, no. 4 (November 1, 1991): 353–60. http://dx.doi.org/10.1115/1.2895412.
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Over 90 percent of the more than 250,000 hip fractures that occur annually in the United States are the result of falls from standing height. Despite this, the stresses associated with femoral fracture from a fall have not been investigated previously. Our objectives were to use three-dimensional finite element models of the proximal femur (with geometries and material properties based directly on quantitative computed tomography) to compare predicted stress distributions for one-legged stance and for a fall to the lateral greater trochanter. We also wished to test the correspondence between model predictions and in vitro strain gage data and failure loads for cadaveric femora subjected to these loading conditions. An additional goal was to use the model predictions to compare the sensitivity of several imaging sites in the proximal femur which are used for the in vivo prediction of hip fracture risk. In this first of two parts, linear finite element models of two unpaired human cadaveric femora were generated. In Part II, the models were extended to include nonlinear material properties for the cortical and trabecular bone. While there was poor correspondence between strain gage data and model predictions, there was excellent agreement between the in vitro failure data and the linear model, especially using a von Mises effective strain failure criterion. Both the onset of structural yielding (within 22 and 4 percent) and the load at fracture (within 8 and 5 percent) were predicted accurately for the two femora tested. For the simulation of one-legged stance, the peak stresses occurred in the primary compressive trabeculae of the subcapital region. However, for a simulated fall, the peak stresses were in the intertrochanteric region. The Ward’s triangle (basicervical) site commonly used for the clinical assessment of osteoporosis was not heavily loaded in either situation. These findings suggest that the intertrochanteric region may be the most sensitive site for the assessment of fracture risk due to a fall and the subcapital region for fracture risk due to repetitive activities such as walking.
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Burleigh, A., and F. Horak. "Influence of instruction, prediction, and afferent sensory information on the postural organization of step initiation." Journal of Neurophysiology 75, no. 4 (April 1, 1996): 1619–28. http://dx.doi.org/10.1152/jn.1996.75.4.1619.
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1. Our previous study showed that two distinct postural modifications occurred when subjects were instructed to step, rather than maintain stance, in response to a backward surface translation: 1) the automatic postural responses to the surfaces perturbation were reduced in magnitude and 2) the anticipatory postural adjustments promoting foot-off were shortened in duration. This study investigates the extent to which task instruction, prediction of perturbation velocity, and afferent sensory information related to perturbation velocity are responsible for these postural modification. 2. Eleven human subjects were instructed in advance, to either maintain stance or step forward in response to a backward surface translation. Four different velocities of translation were used to perturb equilibrium. To assess the influence of predicted versus actual velocity information, the surface translations were presented in both a blocked order of increasing perturbation velocity (predictable) and a random order (unpredictable). Lower-extremity electromyographs (EMGs), ground reaction forces, and movement kinematics were quantified for both the automatic postural responses to perturbation and the anticipatory postural adjustments for step initiation. 3. The instruction to step was not solely responsible for the suppression of the automatic postural response. Prediction of perturbation velocity was required for significant suppression of the early automatic postural response when subjects stepped in response to the perturbation. When compared with the stance condition, the magnitude of the initial 50 ms of the automatic response in bilateral soleus and the left limb gastrocnemius (initial stance limb) was significantly reduced only when the perturbation velocities were presented in a blocked order. The magnitude of the automatic response was not reduced in the gastrocnemius of the right limb, which was always the initial swing limb and recruited for heel-off in the step conditions. This asymmetrical reduction of the gastrocnemius suggests that modification of the response was specific to the instruction, rather than a general decrease in the extensor muscle excitability. 4. The suppression of the early automatic postural response involved a change in the bias of the response. Despite the reduced magnitude during the predictable velocity step condition, the slope (i.e., gain) of the response with increasing velocities was not different from that of the stance condition. Thus the excitability of the automatic response was reduced by a relatively constant amount for each velocity when the perturbation velocity was predictable. 5. In contrast to the importance of velocity prediction for modification of the automatic postural response, actual velocity information was used for modification of the anticipatory postural adjustments when step was initiated in response to the surface perturbation. Regardless of whether the perturbation velocities were presented in a blocked or random order, the anticipatory postural adjustments were rapidly initiated and the duration of the postural adjustments for step initiation was shortened as the velocity of perturbation increased. 6. We conclude that the CNS uses prediction of perturbation velocity to modify the excitability of early automatic postural responses when the postural goal changes. In contrast, actual afferent velocity information can be used to modify the duration of the anticipatory postural adjustments for a voluntary step in response to perturbation. Thus the CNS utilizes feed-forward prediction to modify peripherally triggered postural responses, and utilizes immediate afferent information to modify the centrally initiated postural adjustments associated with voluntary movement.
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Malandrino, Andrea, Fulvia Taddei, Enrico Schileo, Mateusz Juszczyk, Luca Cristofolini, and Marco Viceconti. "PREDICTION OF FAILURE LOAD AND LOCATION ON PROXIMAL FEMUR UNDER A SINGLE STANCE LOADING CONDITION." Journal of Biomechanics 41 (July 2008): S201. http://dx.doi.org/10.1016/s0021-9290(08)70201-x.
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Ganglmayer, Kerstin, Marleen Haupt, Kathrin Finke, and Markus Paulus. "Adults, but not preschoolers or toddlers integrate situational constraints in their action anticipations: a developmental study on the flexibility of anticipatory gaze." Cognitive Processing 22, no. 3 (March 24, 2021): 515–28. http://dx.doi.org/10.1007/s10339-021-01015-8.
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AbstractRecent theories stress the role of situational information in understanding others’ behaviour. For example, the predictive coding framework assumes that people take contextual information into account when anticipating other’s actions. Likewise, the teleological stance theory assumes an early developing ability to consider situational constraints in action prediction. The current study investigates, over a wide age range, whether humans flexibly integrate situational constraints in their action anticipations. By means of an eye-tracking experiment, 2-year-olds, 5-year-olds, younger and older adults (together N = 181) observed an agent repeatedly taking one of two paths to reach a goal. Then, this path became blocked, and for test trials only the other path was passable. Results demonstrated that in test trials younger and older adults anticipated that the agent would take the continuous path, indicating that they took the situational constraints into account. In contrast, 2- and 5-year-olds anticipated that the agent would take the blocked path, indicating that they still relied on the agent’s previous observed behaviour and—contrary to claims by the teleological stance theory—did not take the situational constraints into account. The results highlight developmental changes in human’s ability to include situational constraints in their visual anticipations. Overall, the study contributes to theories on predictive coding and the development of action understanding.
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Pamoukdjian, Frederic, Thomas Aparicio, Sonia Zebachi, Laurent Zelek, Elena Paillaud, and Florence Canoui-Poitrine. "Comparison of Mobility Indices for Predicting Early Death in Older Patients With Cancer: The Physical Frailty in Elderly Cancer Cohort Study." Journals of Gerontology: Series A 75, no. 1 (February 4, 2019): 189–96. http://dx.doi.org/10.1093/gerona/glz024.
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Abstract Background To assess and compare the ability of five mobility indices to predict 6-month mortality in older patients with cancer. Methods All consecutive ambulatory older patients with cancer referred for a geriatric assessment before a cancer treatment decision were included in a prospective two-center cohort study (Physical Frailty in Elderly Cancer) between 2013 and 2017. The mobility indices compared were the short physical performance battery, gait speed, hand grip strength, the one-leg stance balance test, and repeated falls. The primary endpoint was 6-month overall mortality. The adjusted hazard ratio (95% confidence interval [CI]) for each mobility index was estimated using a multivariate Cox proportional hazard model adjusted for sex, the Cumulative Illness Rating Scale for Geriatrics, the body mass index, cancer site/extension, and the provision of supportive care alone. The models’ predictive performances were assessed in terms of Harrell’s C index, net reclassification improvement, and the standardized net benefit. Results A total of 603 patients included (mean age: 81.2 ± 6.1 years; women: 54%; metastatic cancer: 45%). In multivariate analyses, an impairment in any of the mobility indices (with the exception of repeated falls) was independently associated with 6-month mortality following a geriatric assessment; the adjusted hazard ratio [95% CI] ranged from 2.35 [1.34–4.13] for the one-leg stance balance (C index: 0.74) to 3.03 [1.93–4.76] for the short physical performance battery (C index: 0.77). For each mobility index, inclusion in the multivariate model improved significantly the latter’s prediction of 6-month mortality. Conclusions Among mobility tests, short physical performance battery had the best discriminative value for predicting 6-month mortality in older patients with cancer.
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Angın, Salih. "Tek ayak üzerinde duruşta postural salınım hızının ayak postur indeksi, ayak büyüklüğü ve plantar basınç değerleri ile tahmini." Joint Diseases and Related Surgery 24, no. 3 (November 25, 2013): 144–48. http://dx.doi.org/10.5606/ehc.2013.32.
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Gładziejewski, Paweł. "Mechanistic unity of the predictive mind." Theory & Psychology 29, no. 5 (July 31, 2019): 657–75. http://dx.doi.org/10.1177/0959354319866258.
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It has recently been argued that cognitive scientists should embrace explanatory pluralism rather than pursue the search for a unificatory framework or theory. This stance dovetails with the mechanistic view of cognitive-scientific explanation. However, one recently proposed theory—based on an idea that the brain is a predictive engine—opposes pluralism with its unificatory ambitions. My aim here is to investigate those pretentions to elucidate what sort of unification is on offer. I challenge the idea that explanatory unification of cognitive science follows from the Free Energy Principle. I claim that if the predictive story is to provide a unification, it is by proposing that many distinct cognitive mechanisms fall under a single prediction-error-minimization schema. I also argue that even though unification is not an absolute evaluative criterion for mechanistic explanations, it may play an epistemic role in evaluating the relative credibility of an explanation.
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Lotz, J. C., E. J. Cheal, and W. C. Hayes. "Fracture Prediction for the Proximal Femur Using Finite Element Models: Part II—Nonlinear Analysis." Journal of Biomechanical Engineering 113, no. 4 (November 1, 1991): 361–65. http://dx.doi.org/10.1115/1.2895413.
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In Part I we reported the results of linear finite element models of the proximal femur generated using geometric and constitutive data collected with quantitative computed tomography. These models demonstrated excellent agreement with in vitro studies when used to predict ultimate failure loads. In Part II, we report our extension of those finite element models to include nonlinear behavior of the trabecular and cortical bone. A highly nonlinear material law, originally designed for representing concrete, was used for trabecular bone, while a bilinear material law was used for cortical bone. We found excellent agreement between the model predictions and in vitro fracture data for both the onset of bone yielding and bone fracture. For bone yielding, the model predictions were within 2 percent for a load which simulated one-legged stance and 1 percent for a load which simulated a fall. For bone fracture, the model predictions were within 1 percent and 17 percent, respectively. The models also demonstrated different fracture mechanisms for the two different loading configurations. For one-legged stance, failure within the primary compressive trabeculae at the subcapital region occurred first, leading to load transfer and, ultimately, failure of the surrounding cortical shell. However, for a fall, failure of the cortical and trabecular bone occurred simultaneously within the intertrochanteric region. These results support our previous findings that the strength of the subcapital region is primarily due to trabecular bone whereas the strength of the intertrochanteric region is primarily due to cortical bone.
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Tamayo, Antonio, Julián Arias Londoño, Diego Burgos, and Gabriel Quiroz. "Sentiment Analysis of News Articles in Spanish using Predicate Features." Lenguaje 47, no. 2 (July 29, 2019): 235–67. http://dx.doi.org/10.25100/lenguaje.v47i2.7937.
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The automatic prediction of the course of action of agents involved in social or economic trends is an imperative challenge nowadays. However, it is a difficult task because stance or opinion is often spread throughout long, complex texts, such as news articles. The current study tests sentence predicates as features to automatically determine the writer’s stance in news articles. We capture the semantics and stance of the text by encoding features such as the attribute of copulative sentences, the predicate of transitive sentences, adjectival phrases, and the section of the article. Under the assumption that these features are informative enough to model the semantics of the text, each word sequence is disambiguated and assigned a sentiment value using weighting rules. Different experiments were run using either SentiWordNet and ML-Senticon to determine words’ sentiment. Feature vectors are automatically built to populate a database that is tested using two machine learning algorithms. An efficiency of 69% was achieved using a SVM with Gaussian kernel along with a feature selection strategy. This score outperformed the bag-of-words baseline in 12%. These results are promising considering that the sentiment analysis is performed on very complex texts written in Spanish.
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Kennedy, C., C. Higginson, M. Valenti, K. Ibrahim, B. Knarr, R. Ryan, and J. Higginson. "A-12 Neuroticism and Extraversion are Related to Dual Task Postural Stability in Healthy Young Adults." Archives of Clinical Neuropsychology 34, no. 6 (July 25, 2019): 871. http://dx.doi.org/10.1093/arclin/acz034.12.
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Abstract Objective The relation between personality and postural stability has received little attention. This study addressed whether neuroticism and extraversion are related to changes in postural stability and cognitive functioning during a standing balance task. Method Thirty-two healthy young adults completed a personality measure and two cognitive tasks, a 2-back task and a weather prediction task (WPT), both while seated and in tandem stance on a foam mat. Sway was quantified via normalized path lengths, and correlation coefficients were calculated between neuroticism, extraversion and dual task changes in postural stability and cognitive functioning. Results Consistent with predictions, higher neuroticism was related to increased dual task sway during the 2-back task, r = 0.40, p = 0.023, and lower extraversion was related to increased dual task sway during the WPT, r = -0.43, p = 0.013. Conclusions The results suggest that personality is related to postural stability in healthy young adults and that personality should be considered in the prediction of individuals at risk for falling or in the treatment of individuals with balance difficulties. The task-specific nature of the relation is discussed and may be due to differences in anxiety or underlying brain mechanisms between high neuroticism and low extraversion.
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Hallett, Andrew Hughes, Ansgar Rannenberg, and Sven Schreiber. "Reassessing the Impact of the US Fiscal Stimulus: The Role of the Monetary Policy Stance." International Business Research 10, no. 4 (March 3, 2017): 12. http://dx.doi.org/10.5539/ibr.v10n4p12.
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Cogan et al. (2009, 2010) claimed that the stimulus package passed by the United States Congress in February 2009 had a multiplier far below one. However, the stimulus’ multiplier strongly depends on the assumed monetary policy response. Based on official statements from the Fed chairman, the general economic outlook, past behavior of the FOMC, optimal policy considerations, and from financial market expectations, we find that in February 2009, a reasonable prediction of the period of monetary accommodation would have exceeded 9 quarters. This implies that a plausible real time assessment of the stimulus’ effects would have been more optimistic than Cogan et al.’s.
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Badgujar, Mr Prathamesh, Mr Aditya Kamble, Mr Anuj Kadam, Mr Dhruv Shah, and Mr Anilkumar Kadam. "A Survey Paper on Stance Detection of Tweets on Farmers Protests in India." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2771–74. http://dx.doi.org/10.22214/ijraset.2022.42959.
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Abstract: Protests are a critical part of democracy and a crucial medium for people to deliver their needs and/or dissatisfaction to the authorities. As farmers felt a threat to their rights, there were more and more protests all around the nation. With the development of this technology, additionally there has been a sudden rise in the use of social network sites to trade facts and ideas. In this study, we collected information from the social networking internet site Twitter regarding the Farmers’ protest to apprehend the feelings that the twitter users shared on a worldwide platform. In the midst of this protest, social media users had been very lively in voicing their opinion about the matter using the "#FarmersProtest". With lots of people tweeting with the hashtag daily. Through the Stance Prediction of over 850,000 tweets and over 150,000 Users, We intend to decide the Inclination of Common Citizen in addition to Influential People on the now repealed Laws. Keywords: Stance Detection, ULMFIT, Language Model, Farmers, Twitter
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KIM, YoungEun, and SooTaek KIM. "Stress Sensors Driving a Feedback Mechanism for the Prediction of Paraspinal Muscle Forces during Upright Stance Posture." Journal of Biomechanical Science and Engineering 3, no. 3 (2008): 419–30. http://dx.doi.org/10.1299/jbse.3.419.
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Huang, Chao, Fuping Zhang, Zhengyi Xu, and Jianming Wei. "Adaptive Pedestrian Stride Estimation for Localization: From Multi-Gait Perspective." Sensors 22, no. 8 (April 7, 2022): 2840. http://dx.doi.org/10.3390/s22082840.
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Accurate and reliable stride length estimation modules play a significant role in Pedestrian Dead Reckoning (PDR) systems, but the accuracy of stride length calculation suffers from individual differences. This paper presents a stride length prediction strategy for PDR systems that can be adapted across individuals and broad walking velocity fields. It consists of a multi-gait division algorithm, which can divide a full stride into push-off, swing, heel-strike, and stance based on multi-axis IMU data. Additionally, based on the acquired gait phases, the correlation between multiple features of distinct gait phases and the stride length is analyzed, and multi regression models are merged to output the stride length value. In experimental tests, the gait segmentation algorithm provided gait phases division with the F-score of 0.811, 0.748, 0.805, and 0.819 for stance, push-off, swing, heel-strike, respectively, and IoU of 0.482, 0.69, 0.509 for push-off, swing, heel-strike, respectively. The root means square error (RMSE) of our proposed stride length estimation was 151.933, and the relative error for total distance in varying walking speed tests was less than 2%. The experimental results validated that our proposed gait phase segmentation algorithm can accurately recognize gait phases for individuals with wide walking speed ranges. With no need for parameter modification, the stride length method based on the fusion of multiple predictions from different gait phases can provide better accuracy than the estimations based on the full stride.
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Su, Binbin, Yi-Xing Liu, and Elena M. Gutierrez-Farewik. "Locomotion Mode Transition Prediction Based on Gait-Event Identification Using Wearable Sensors and Multilayer Perceptrons." Sensors 21, no. 22 (November 10, 2021): 7473. http://dx.doi.org/10.3390/s21227473.
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People walk on different types of terrain daily; for instance, level-ground walking, ramp and stair ascent and descent, and stepping over obstacles are common activities in daily life. Movement patterns change as people move from one terrain to another. The prediction of transitions between locomotion modes is important for developing assistive devices, such as exoskeletons, as the optimal assistive strategies may differ for different locomotion modes. The prediction of locomotion mode transitions is often accompanied by gait-event detection that provides important information during locomotion about critical events, such as foot contact (FC) and toe off (TO). In this study, we introduce a method to integrate locomotion mode prediction and gait-event identification into one machine learning framework, comprised of two multilayer perceptrons (MLP). Input features to the framework were from fused data from wearable sensors—specifically, electromyography sensors and inertial measurement units. The first MLP successfully identified FC and TO, FC events were identified accurately, and a small number of misclassifications only occurred near TO events. A small time difference (2.5 ms and −5.3 ms for FC and TO, respectively) was found between predicted and true gait events. The second MLP correctly identified walking, ramp ascent, and ramp descent transitions with the best aggregate accuracy of 96.3%, 90.1%, and 90.6%, respectively, with sufficient prediction time prior to the critical events. The models in this study demonstrate high accuracy in predicting transitions between different locomotion modes in the same side’s mid- to late stance of the stride prior to the step into the new mode using data from EMG and IMU sensors. Our results may help assistive devices achieve smooth and seamless transitions in different locomotion modes for those with motor disorders.
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Palmieri, Rudi, and Johanna Miecznikowski. "Predictions in economic-financial news." Argumentation in Journalism 5, no. 1 (March 24, 2016): 48–73. http://dx.doi.org/10.1075/jaic.5.1.03pal.
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Compared to other domains of media discourse, economic-financial news contain a considerable amount of speech acts regarding future events, in particular predictions. This can be explained by their specific institutional context, financial markets, where investors constantly seek to single out gain opportunities and to correctly assess their risk. One of the crucial factors making economic-financial predictions worthy of being considered in investment decisions is argumentation, in particular the extent to which the predicted proposition follows from a plausible and acceptable reasoning. Starting from a corpus of 50 articles of the Italian economic-financial press, we consider the inferential dimension of prediction-oriented arguments, focusing on the locus, i.e. the ontological relation on which the connection between the argument(s) and the predictive conclusion rests. All predictions found in the corpus were manually annotated with the software UAM Corpus Tool. For each of them we identified the source, which could be either the journalist him/herself or a third party, typically financial analysts or corporate actors. We distinguished mere predictive opinions from predictive standpoints, i.e. predictions for which the journalist advances one or more supportive arguments (either confirmatory of refutatory). For the latter category, we identified the locus referring to an adaptation of the taxonomy outlined by Rigotti (2009). The findings highlight in particular the following three interesting aspects: (1) in predictions, journalists reinforce their stance by plausible justifications, but weaken it at the same time by marking it as uncertain and/or by using reported speech or evidential means to reduce their responsibility for the predictive speech act; (2) the justification of a predictive standpoint, by the journalist or by third parties, is mostly based on loci of causality, in particular on the locus from efficient cause, the locus from final cause and complex forms of causality where the involvement of rational agents is implied but defocused; (3) moreover, journalists refer to the predictive opinions of experts or corporate insiders to activate the locus from authority, either by explicit argumentation or implicitly, by reporting speech from reliable sources. Our study suggests that the role of predictions in financial news is not so much that of giving any straightforward advice to investors, but rather that of providing chunks of sound argumentative reasoning, including both supportive evidence and rebuttals or refutatory moves, that the investor-reader might apply and combine in the highly uncertain context of financial markets. Overall, our findings shed light on how financial journalists fulfil the function of information intermediaries in finance.
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Putra, A. M. S., Muhamad Noor Harun, and Syahrom Ardiyansyah. "Study of Wear Prediction on Total Ankle Replacement." Advanced Materials Research 845 (December 2013): 311–15. http://dx.doi.org/10.4028/www.scientific.net/amr.845.311.
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Pre-clinical experimental wear testing is very effective to evaluate new ankle replacement in the aspect of design and material used. However, both cost and time can be one of the constraints factors, particularly in the early stage of design or analysis. Numerical method has been addressed as an alternative to predict wear on ankle replacement. The computational wear simulation has been widely used on the hip and knee but very less found in study related to wear analysis of the ankle. The purpose of this research is to develop computational simulation to predict wear on total ankle replacement (TAR). Three dimensional (3D) models of the right ankle TAR were developed using BOX total ankle replacement model. Mobile bearing device was developed consisting of three components tibial, bearing and talar. Each component has different design and purposes representing its physiological behaviour of the ankle. The ankle load applied was based on the joint reaction force profile at the ankle joint. This is to determine the distribution of contact stress on the meniscal bearing surfaces contact with talar component for 25 discrete instant during stance phase of gait cycle. The sliding distance was obtained from predominates motion of plantar/dorsi flexion. The computed linear wear depth and cumulative volumetric wear were 0.01614 mm per million cycles and 30.5 mm3, respectively. The values obtained were proven to be consistent with the previous in vitro result.
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Haraguchi, Naoki, Robert S. Armiger, Mark S. Myerson, John T. Campbell, and Edmund Y. S. Chao. "Prediction of Three-Dimensional Contact Stress and Ligament Tension in the Ankle During Stance Determined from Computational Modeling." Foot & Ankle International 30, no. 2 (February 2009): 177–85. http://dx.doi.org/10.3113/fai-2009-0177.
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Haraguchi, Naoki, Robert S. Armiger, Mark S. Myerson, John T. Campbell, and Edmund Y. S. Chao. "Prediction of Three-Dimensional Contact Stress and Ligament Tension in the Ankle During Stance Determined from Computational Modeling." Foot & Ankle International 30, no. 02 (February 2009): 177–85. http://dx.doi.org/10.3113/fai.2009.0177.
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Wu, Juan. "War as a Backdrop for Legislation." Indo-Iranian Journal 62, no. 4 (December 5, 2019): 293–339. http://dx.doi.org/10.1163/15728536-06204003.
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Abstract While the story of the Magadhan king Ajātaśatru’s seeking the Buddha’s advice on attacking the Vṛjis is well known and much studied, rather less known and little studied are stories of his war or conflict with the Vṛjis embedded in Indian Buddhist monastic law codes. This paper explores these lesser-known stories of Ajātaśatru’s warfare, primarily focusing on their function as narrative frames for monastic rules or exceptions (anāpatti) that have no necessary relation to war. It investigates the rationale behind Indian Buddhist jurists’ utilization of these stories to account for monastic legislation, and discusses the perceptions of war reflected therein. Moreover, the paper shows that Indian Buddhist jurists of different sects or schools do not seem to have shared the same stance on predicting warfare, some arguably more ambivalent than others, especially when a prediction proves wrong and is thus liable to shake the laity’s faith and/or harm the mutual trust between monks themselves.
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Moura da Silva, Patrícia Mayara, Ana Beatriz Oliveira Bezerra, Luanna Barbara Araújo Farias, Tatiana Souza Ribeiro, Edgard Morya, and Fabrícia Azevêdo da Costa Cavalcanti. "Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review." BMJ Open 12, no. 2 (February 2022): e051981. http://dx.doi.org/10.1136/bmjopen-2021-051981.
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IntroductionType 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive methods to forecast complications and develop early strategies to guide treatments. To date, no research has systematically summarised the predictive methods used to assess type 2 diabetic gait. Therefore, this protocol aims to identify which predictive methods have been employed to assess the diabetic gait.Methods and analysisThis protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) statement. Electronic searches of articles from inception to January 2022 will be performed, from May 2021 to 31 January 2022, in the Web of Science, MEDLINE, Embase, IEEE Xplore Digital Library, Scopus, CINAHL, Google Scholar, APA PsycInfo, the Cochrane Library and in references of key articles and grey literature without language restrictions. We will include studies that examined the development and/or validation of predictive methods to assess type 2 diabetic gait in adults aged >18 years without amputations, use of assistive devices, ulcers or neuropathic pain. Two independent reviewers will screen the included studies and extract the data using a customised charting form. A third reviewer will resolve any disagreements. A narrative synthesis will be performed for the included studies. Risk of bias and quality of evidence will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.Ethics and disseminationEthical approval is not required because only available secondary published data will be analysed. The findings will be disseminated through peer-reviewed journals and/or presentations at relevant conferences and other media platforms.PROSPERO registration numberCDR42020199495.
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Chun, Sabin, Sangha Park, Duhyun Kim, Jae Myoung Park, and Jong Chul Park. "A Comparative Study on Neuromuscular Control Patterns during Side Cutting Depending on Direction Prediction Presence or Absence and Gender." Korean Journal of Sport Science 33, no. 4 (December 31, 2022): 554–61. http://dx.doi.org/10.24985/kjss.2022.33.4.554.
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PURPOSE This study aimed to identify the lower limb muscle activity based on direction prediction presence or absence and gender during side cutting in healthy college students. METHODS The study participants included 14 healthy males and females (8 males; 6 females). All participants ran at full speed for a distance of 12m, and side-cutting was carried out at 45 degrees in a randomly indicated direction and in a fixed direction. Simultaneously, data regarding vastus medialis, vastus lateralis, semitendinosus, and biceps femoris muscle activity of the dominant leg were collected using an electromyography sensor, and data regarding vertical acceleration were collected using an inertial sensor attached to the pelvis. A sync webcam was used for obtaining the initial contact of side cutting and the stance period time. During the 10 milliseconds (pre-activation) prior to the initial contact and 50% of the stance phase (loading phase), vastus medialis, vastus lateralis, semitendinosus, and biceps femoris average muscle activity and hamstring to quadriceps ratio included as variables. RESULTS During the pre-activation and loading phase, the vastus medialis muscle activity of the male group was higher in the unexpected condition than in the expected condition. Furthermore, hamstring to quadriceps ratio was confirmed to be lower under unexpected condition compared to under expected condition during on loading phase. CONCLUSIONS The study results suggest that the risk of anterior cruciate ligament injury may increase with side cutting under unpredictable conditions. It is expected to provide useful information for identifying factors related to knee injury in the general population.
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Ren, Yanze, Yan Liu, Jing Chen, Xiaoyu Guo, Junyu Shi, and Mengmeng Jia. "News Stance Discrimination Based on a Heterogeneous Network of Social Background Information Fusion." Entropy 25, no. 1 (December 30, 2022): 78. http://dx.doi.org/10.3390/e25010078.
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Media with partisan tendencies publish news articles to support their preferred political parties to guide the direction of public opinion. Therefore, discovering political bias in news texts has important practical significance for national election prediction and public opinion management. Some biased news often has obscure expressions and ambiguous writing styles. By bypassing the language model, the accuracy of methods that rely on news semantic information for position discrimination is low. This manuscript proposes a news standpoint discrimination method based on social background information fusion heterogeneous network. This method expands the judgment ability of creators and topics on news standpoints from external information and fine-grained topics based on news semantics. Multi-attribute features of nodes enrich the feature representation of nodes, and joint representation of heterogeneous networks can reduce the dependence of position discrimination on the news semantic information. To effectively deal with the position discrimination of new news, the design of a multi-attribute fusion heterogeneous network is extended to inductive learning, avoiding the cost of model training caused by recomposition. Based on the Allsides dataset, this manuscript expands the information of its creator’s social background and compares the model for discriminating political positions based on news content. In the experiment, the best transductive attribute fusion heterogeneous network achieved an accuracy of 92.24% and a macro F1 value of 92.05%. The effect is improved based purely on semantic information for position discrimination, which proves the effectiveness of the model design.
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Bjerva, Johannes, Wouter Kouw, and Isabelle Augenstein. "Back to the Future – Temporal Adaptation of Text Representations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7440–47. http://dx.doi.org/10.1609/aaai.v34i05.6240.
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Language evolves over time in many ways relevant to natural language processing tasks. For example, recent occurrences of tokens 'BERT' and 'ELMO' in publications refer to neural network architectures rather than persons. This type of temporal signal is typically overlooked, but is important if one aims to deploy a machine learning model over an extended period of time. In particular, language evolution causes data drift between time-steps in sequential decision-making tasks. Examples of such tasks include prediction of paper acceptance for yearly conferences (regular intervals) or author stance prediction for rumours on Twitter (irregular intervals). Inspired by successes in computer vision, we tackle data drift by sequentially aligning learned representations. We evaluate on three challenging tasks varying in terms of time-scales, linguistic units, and domains. These tasks show our method outperforming several strong baselines, including using all available data. We argue that, due to its low computational expense, sequential alignment is a practical solution to dealing with language evolution.
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Aftab, Zohaib, Gulraiz Ahmed, Asad Ali, and Nazia Gillani. "Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms." Applied Bionics and Biomechanics 2022 (October 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/2883026.
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Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.
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Fraiwan, Luay, and Omnia Hassanin. "Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers." PLOS ONE 16, no. 6 (June 4, 2021): e0252380. http://dx.doi.org/10.1371/journal.pone.0252380.
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This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.
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Fu, Qianyi, Thomas J. Armstrong, and Albert Shih. "Development of a Decision Support System for Ankle-Foot Orthosis (AFO) Design Based on Lumped Parameter Models for Human Locomotion Prediction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 463–67. http://dx.doi.org/10.1177/1071181319631194.
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This work aims to develop a decision support system (DSS) to help orthotic clinicians design AFOs to correct pathological gait patterns that result from various nerve impairments. The DSS utilizes lumped parameter models to predict key joint angles, step length, and swing and stance durations based on anthropometric data, impairment and AFO design. The predicted gait patterns by DSS were in general agreement with data from available literature. In the presented drop foot example, the DSS shows that increasing AFO stiffness would result in increased stride duration on both sides; proper AFO stiffness may result in increased stride length on both sides comparing with stride length for pathological gait without AFO. The DSS can be used by clinicians to suggest proper AFO’s mechanical properties, such as stiffness and range of motion, to help improve abnormal gait patterns resulting from underlying nerve impairments.
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Wanna, Soh Bua Chai, Khairul Salleh Basaruddin, Fauziah Mat, Mohd Hanafi Mat Som, and Abdul Razak Sulaiman. "Finite Element Prediction on Fracture Load of Femur with Osteogenesis Imperfecta under Various Loading Conditions." Applied Bionics and Biomechanics 2022 (January 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/8722333.
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Osteogenesis Imperfecta (OI) is an inherited disorder characterized by extreme bone fragility due to collagen defects. It is an incurable disease. Bone fractures can occur frequently without prior notice, especially among children. Early quantitative prediction of fracture loads due to OI tends to alert patients to avoid unnecessary situations or dangerous conditions. This study is aimed at investigating the fracture loads of femur with OI under various types of loading. Ten finite element models of an OI-affected bone were reconstructed from the normal femur with different bowing angles ranging from 7.5 to 30.0°. The boundary conditions were assigned on an OI-affected femoral head under three types of load: medial-lateral impacts, compression-tension, and internal-external torsions, and various loading direction cases that reflect the stance condition. The fracture load was examined based on the load that can cause bone fracture for each case. The results show that the loads bearable by the femur before fracture were decreased with respect to the increase of OI bowing angles in most of the loading cases. The risk of fracture for the femur with OI was directly proportional to the increase of bowing angles in the frontal plane. This study provides new insights on fracture load prediction in OI-affected bone with respect to various loading types, which could help medical personnel for surgical intervention judgement.
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Morbidoni, Christian, Alessandro Cucchiarelli, Sandro Fioretti, and Francesco Di Nardo. "A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking." Electronics 8, no. 8 (August 14, 2019): 894. http://dx.doi.org/10.3390/electronics8080894.
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Correctly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of 94.9 for learned subjects and 93.4 for unlearned ones, while mean absolute difference ( ± S D ) between phase transitions timing predictions and footswitch data was 21.6 ms and 38.1 ms for heel-strike and toe off, respectively. The suitable performance achieved by the proposed method suggests that it could be successfully used to automatically classify gait phases and predict foot–floor-contact signal from sEMG signals during level ground walking.
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Trachtenberg, Lianne, Mary Jane Esplen, Brenda Toner, and Niva Piran. "Gender Socialization as a Predictor of Psychosocial Well-Being in Young Women with Breast Cancer." Current Oncology 29, no. 11 (October 27, 2022): 8121–32. http://dx.doi.org/10.3390/curroncol29110641.
Full textAbstract:
This study aimed to explore the relationship between gender socialization and psychosocial well-being among young women diagnosed with and treated for breast cancer. A total of 113 women between the ages of 18–49 completed a one-time questionnaire package. Four key measures of gender socialization were included: Gender Role Socialization Scale (GRSS), Objectified Body Consciousness Scale (OBCS), Mental Freedom Scale (MFS), and Silencing the Self Scale (SSS). Two measures of psychosocial well-being were included: Functional Assessment of Cancer Therapy-Breast (FACT-B) and Experience of Embodiment Scale (EES). Correlational and regression analyses were conducted to assess the relationship between gender socialization variables and well-being. In multiple regression models, GRSS and MFS added significant increments to the prediction of variance of the FACT-B (R2 = 23.0%). In contrast, the OBCS and MFS added significant increments to the prediction of variance of the EES (R2 = 47.0%). Findings suggested that women with greater endorsements to proscribed gender socialization were associated with poor well-being scores. Women who endorsed a critical stance, resisting traditional gender-role expectations, objectification pressures, and other social discourses, were associated with greater well-being scores. Future studies are needed to examine the impact of gender socialization on the well-being of young people with breast cancer.
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Wanke, Riccardo, and Vincenzo Santarcangelo. "Memory as the Aspatial Domain for the Perception of Certain Genres of Contemporary Art Music." Music & Science 4 (January 1, 2021): 205920432199765. http://dx.doi.org/10.1177/2059204321997658.
Full textAbstract:
This paper enquires into the nature of the connections between memory and certain genres of contemporary art music whose unique features rely particularly on our early mnemonic processes. Specific sound configurations of this music are often associated, during listening, with visual and tactile sensorial qualities and with abstract geometries. They are perceived fundamentally as the results of acoustic-physical forces and energies and are organized according to Gestalt and kinesthetic principles. This kind of music calls for a specific listening attitude, which we define as the vertical stance, and seems particularly apt to respond to mechanisms of the working memory where echoic, short- and long-term memories assume a central role. In this vertical stance, memory is involved in the mental construction (segregation, storage, and prediction) of the Gestalt configurations of this music within a perceptual domain that crucially has no spatial connection to the external world. In tying in neurophysiological and psychological research with musicological theories, we discuss the perceptual approach to these music practices in the light of the philosophical concept of the ‘No-Space world’ as conceived by the philosopher Peter Strawson. We propose that – under certain conditions – memory may be the realm of the purely spectro-temporal features of music. The sound configurations of this music in particular are part of an internal-external perceptual framework, being decoded in the conceptual space of perception and able to elicit high-order recollections typical of an embodied engagement with the external world.