Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time s...Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time scales can lead to alterations in postural center of pressure(Co P)fluctuations.Multiscale entropy(MSE) has been used to identify differences in fluctuations of postural Co P time series between groups with and without known physiological impairments at multiple time scales.The purpose of this paper is to:1) review basic elements and current developments in entropy techniques used to assess physiological complexity;and 2) identify how MSE can provide insights into the complexity of physiological systems operating at multiple time scales that underlie the control of posture.We review and synthesize evidence from the literature providing support for MSE as a valuable tool to evaluate the breakdown in the physiological processes that accompany changes due to aging and disease in postural control.This evidence emerges from observed lower MSE values in individuals with multiple sclerosis,idiopathic scoliosis,and in older individuals with sensory impairments.Finally,we suggest some future applications of MSE that will allow for further insight into how physiological deficits impact the complexity of postural fluctuations;this information may improve the development and evaluation of new therapeutic interventions.展开更多
Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previous...Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.展开更多
文摘Clinical disorders often are characterized by a breakdown in dynamical processes that contribute to the control of upright standing.Disruption to a large number of physiological processes operating at different time scales can lead to alterations in postural center of pressure(Co P)fluctuations.Multiscale entropy(MSE) has been used to identify differences in fluctuations of postural Co P time series between groups with and without known physiological impairments at multiple time scales.The purpose of this paper is to:1) review basic elements and current developments in entropy techniques used to assess physiological complexity;and 2) identify how MSE can provide insights into the complexity of physiological systems operating at multiple time scales that underlie the control of posture.We review and synthesize evidence from the literature providing support for MSE as a valuable tool to evaluate the breakdown in the physiological processes that accompany changes due to aging and disease in postural control.This evidence emerges from observed lower MSE values in individuals with multiple sclerosis,idiopathic scoliosis,and in older individuals with sensory impairments.Finally,we suggest some future applications of MSE that will allow for further insight into how physiological deficits impact the complexity of postural fluctuations;this information may improve the development and evaluation of new therapeutic interventions.
基金supported by the National Key Scientific Instrument and Equipment Development Project of China(No.2013YQ49087903)the National Natural Science Foundation of China(No.61202160)
文摘Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.
