126 research outputs found

    Automaticity with Balance in Dual-Task Tests in Healthy Adolescents

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    BACKGROUND/CONTEXT: The hallmark of healthy postural control in adolescents is automaticity which is the ability of the nervous system to successfully coordinate posture with minimal use of attention-demanding executive control resources. Automaticity plays an important role in adolescents because it is necessary to perform motor skills, motor learning and reduce the risk of sport injury. Research has shown in dual-task (DT) paradigms that the ability to use postural control and cognitive skills of the brain uses both local and global functional connectivity in the Prefrontal Cortex, which controls a healthy adolescent\u27s ability to perform a dual-task test with adequate balance. PURPOSE: The purpose of our study is to examine if healthy adolescents show strong functional connectivity to compensate for the deficit in their postural control. Automaticity can be measured by presenting a healthy adolescent with a dual-task test, and observing if the brain activity is impacted in the Prefrontal Cortex (PFC), inferring a significant deficit in their postural control. The use of force plates are used to measure the sway area and the average velocity of the participants when they are given a single-task test and a dual-task test. Smaller sway area and average velocity presents a better performance in the local and global functional connectivity between regions of the brain. We hypothesized that there is no significant difference in terms of single or dual-task tests in their functional connectivity. METHODS: 15 healthy adolescents (12 male (80%), age: 16.33±0.94 years, height: 1.69±0.10 m, mass: 64.08±9.81 kg) were recruited. Activity of the left/right prefrontal cortex (dorsal lateral and dorsal medial regions) were monitored using fNIRS, sampling rate of 20.3 Hz. The AMTI force plate is used to measure the center of pressure (CoP), sampling rate of 2000 Hz. Participants performed two standing trials on force plates for 30 seconds in single task (ST) and dual task (DT: concurrent cognitive task subtracting by 7’s) conditions. There was a 10-second quiet standing before each trial to serve as the baseline for the fNIRS signals. Our dependent variable included the HbO2 level, local and global efficiency of the prefrontal cortex and the 95% sway area and average CoP velocity. Three two-way MANOVA with repeated measures were used to examine the task difference (alpha level = 0.05). OUTCOMES: There was no significant task effect on balance performance (F3,12 = 4.048, p = 0.033). Post pairwise tests indicated that single-task tests presented a smaller average CoP velocity in the anterior posterior (p = 0.037, ST vs DT: 6.78 ± 2.39 vs. 10.22 ± 5.80 cm/s) and medial lateral (p = 0.048, ST vs DT: 4.38 ± 1.44 vs. 6.31 ± 3.79 cm/s) directions than in dual-task test. There was no significant task effect on HbO2 level, local and global efficiency (p \u3e 0.05). IMPACT: There was no significant task effect on brain efficiency in balance performance. We observed a worse balance performance under dual-task tests compared to single-task tests while the functional connectivity remains the same. These results suggest that adolescents are still developing their automaticity in balance when compared to the healthy young adults who would have the same balance performance under the dual-task tests

    Functional Connectivity in Gait Under Dual-Task Paradigm in Healthy Adolescents

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    PURPOSE: Functional connectivity can be viewed as the mechanism used to coordinate different neural networks in order to perform a complex task. Dual-task walking requires an individual to walk, while simultaneously performing a secondary task. The purpose of this study was to determine the level of functional connectivity and neuro-efficiency in adolescents under the dual-task walking. We hypothesized that we would see an increase in local and global efficiency within adolescents when transitioning from a single task gait test to a dual task gait test. METHODS: 15 healthy adolescents (12 male, age: 16.33±0.94 years, height: 1.69±0.10 m, mass: 64.08±9.81 kg) were recruited. The brain activity of the left and right prefrontal cortex (dorsal lateral, and dorsal media) were measured by fNIRS, the sampling rate of 20.3 Hz. Vicon motion capture system was used to record kinematic data, the sampling rate of 100 Hz. The first test was a single task gait test in which the subject walked at a self-selected speed between two cones 15 meters apart for 2 minutes with 10 seconds of standing as the baseline for fNIRS measures. Subjects were then tested under a dual-task paradigm (serially subtracting 7’s from randomly presented 2 or 3-digit numbers). The primary outcome measures include normalized local and global efficiency, gait speed, and stride length. Two two-way MANOVA with repeated measures were used to examine the task difference (alpha level = 0.05). RESULTS: There was a significant task effect on gait performance (F3,12 = 6.430, p = 0.008). Post hoc pairwise tests indicated that single-task presented greater average walking velocity (p \u3c 0.001, ST vs. DT: 1.33 ± 0.18 vs. 1.23 ± 0.20 m/s) and shorter stride time (p = 0.002, ST vs. DT: 1.11 ± 0.10 vs. 1.14 ± 0.12 s) than dual-task. There was no significant task effect on brain activity and neural efficiency (p \u3e 0.05). CONCLUSION: There was a significant difference in gait speed between adolescents and young adults. This is due to the task complexity affecting adolescents significantly more than adults. Young adults don’t see a change in speed but do see an increase in PFC activation. Adolescents having lower levels of functional connectivity compared to young adults could be due to the number/size of functionally connected regions measured within adolescence. Children are still developing day by day, indicating that the strength of functional connectivity seemingly develops as they age. With this information we can conclude that functional connectivity continuously changes while going through your adolescent years

    LSTM Pose Machines

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    We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

    Tai Chi Can Improve Postural Stability as Measured by Resistance to Perturbation Related to Upper Limb Movement Among Healthy Older Adults

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    Purpose: The aim of the study was to examine the effects of Tai Chi (TC) training on postural control when upright standing was perturbed by upper limb movement. Methods: Three groups, TC, Brisk walk (BW), and sedentary (SE), of thirty-six participants aged from 65 to 75 years were recruited from local community centers. Participants performed static balance task (quiet standing for 30 s with eyes open and closed) and fitting task (two different reaching distances X three different opening sizes to fit objects through). During tasks, the COP data was recorded while standing on the force plate. Criteria measures calculated from COP data were the maximum displacement in anterior-posterior (AP) and medial-lateral (ML) directions, the 95% confidence ellipse area (95% area), and the mean velocity. Results: No significant effect was observed in the static balance task. For fitting tasks, the group effect was observed in all directions on COP 95% area (p \u3c 0.05) and the TC group showed reduced area. The tests of subject contrasts showed significant trends for reaching different distances and fitting different openings conditions in all directions, the 95% area, and the mean velocity (p \u3c 0.05). Conclusion: Compared to the other two groups, long-term TC exercise helps in reducing the effects of upper body perturbation as measured by posture sway

    Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

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    Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge. In this paper, we present PGIM -- a two-stage framework that aims to leverage ChatGPT as an implicit knowledge base and enable it to heuristically generate auxiliary knowledge for more efficient entity prediction. Specifically, PGIM contains a Multimodal Similar Example Awareness module that selects suitable examples from a small number of predefined artificial samples. These examples are then integrated into a formatted prompt template tailored to the MNER and guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the original text and fed into a downstream model for further processing. Extensive experiments show that PGIM outperforms state-of-the-art methods on two classic MNER datasets and exhibits a stronger robustness and generalization capability.Comment: Accepted to Findings of EMNLP 202

    Dynamic analysis and control of strip mill vibration under the coupling effect of roll and rolled piece

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    According to the “Hill rolling force formula”, taking particular account of the influence from horizontal vibration of rolled piece in roll gap, a dynamic rolling force model is analyzed. Considering the interaction between vibration of strip and roll, the dynamic vibration model of rolling mill is established. On this basis, the time delayed feedback is introduced to control the vibration of the roll system. The amplitude frequency response of the coupled vibration control equation is obtained by using the multiple scales method. Different time delay parameters are selected to test the control effect. Research results show that the unstable vibration of the roll system can be suppressed with appropriate time delay feedback parameters. Because it is simpler and has good control effect in solving nonlinear mechanical vibration, so these results will make a difference for the research of strip mill vibration, and provide theoretical basis for strip steel production

    Random vibration analysis for coupled vehicle-track systems with uncertain parameters

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    Purpose – The purpose of this paper is to present a new random vibration-based assessment method for coupled vehicle-track systems with uncertain parameters when subjected to random track irregularity. Design/methodology/approach – The uncertain parameters of vehicle are described as bounded random variables. The track is regarded as an infinite periodic structure, and the dynamic equations of the coupled vehicle-track system, under mixed physical coordinates and symplectic dual coordinates, are established through wheel-rail coupling relationships. The random track irregularities at the wheel-rail contact points are converted to a series of deterministic harmonic excitations with phase lag by using the pseudo excitation method. Based on the polynomial chaos expansion of the pseudo response, a chaos expanded pseudo equation is derived, leading to the combined hybrid pseudo excitation method-polynomial chaos expansion method. Findings – The impact of uncertainty propagation on the random vibration analysis is assessed efficiently. According to GB5599-85, the reliability analysis for the stability index is implemented, which can grade the comfort level by the probability. Comparing to the deterministic analysis, it turns out that neglect of the parameter uncertainty will lead to potentially risky analysis results. Originality/value – The proposed method is compared with Monte Carlo simulations, achieving good agreement. It is an effective means for random vibration analysis of uncertain coupled vehicle-track systems and has good engineering practicality

    Facial Data Minimization: Shallow Model as Your Privacy Filter

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    Face recognition service has been used in many fields and brings much convenience to people. However, once the user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there exist various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or auxiliary data. Hence, in this paper, by fully considering two cases of uploading facial images and facial features, which are very typical in face recognition service systems, we proposed a data privacy minimization transformation (PMT) method. This method can process the original facial data based on the shallow model of authorized services to obtain the obfuscated data. The obfuscated data can not only maintain satisfactory performance on authorized models and restrict the performance on other unauthorized models but also prevent original privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we also propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT in defending against face reconstruction, data abuse, and face attribute estimation attacks. These experimental results demonstrate that PMT performs well in preventing facial data abuse and privacy leakage while maintaining face recognition accuracy.Comment: 14 pages, 11 figure
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