Validation of a task demand measure for predicting mental workloads of physical therapists

The purpose of this study is to validate an analytical method in assessing demanded mental workloads for physical therapists, and to discuss its possible ergonomics implications on the design of healthcare working systems in which physical therapists are considered as the users. A task analysis was first used to understand the operation steps of three identified physical therapies. Then, the McCracken–Aldrich technique was applied to assign ratings of mental workload demanded for performing each step of the therapies. Finally, the assigned ratings were validated by the analysis of correlation with the answers of the NASA-TLX questionnaire collected from seventeen physical therapists in the rehabilitation center of a university-affiliated hospital. Results showed that the proposed McCracken–Aldrich technique was suitable as an analysis tool for predicting metal workloads of physical therapists. Some possible implications about the information provision and user interface design for reducing therapists' mental workloads on current therapy operations were discussed.Relevance to industryResults of this paper are expected to contribute the research and development in healthcare industry on its system design and workflow analysis.     

Mathematical models of the thermal decomposition of coal: 3. Density,porosity and contraction behaviour

Mathematical models are proposed to predict the true density of coal, semi-coke and coke and the porosity of a coke oven charge during carbonization. These models are combined in a further model which predicts the hitherto unexplained ‘twin-peaked’ contraction curve observed for semi-coke. It is found that the first contraction peak results from the relatively high rate of mass loss occurring towards the end of the primary devolatilization stage. The second peak occurs because of the increase in true density arising from the elimination of hydrogen from the semi-coke.     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

基于扩散距离和MDS的非刚性模型相似性分析

在非刚性模型相似性分析方法中, 基于测地距离的等距嵌入方法对模型拓扑变化非常敏感, 对于有局部拓扑变化的完全相似的模型也无法得到正确结果。为了弥补这一不足, 提高非刚性模型相似性分析的准确性, 将扩散距离与多维尺度分析(MDS)相结合, 计算非刚性模型的等距嵌入模型, 再利用迭代最近点(ICP)算法通过计算嵌入模型的相似性来实现原始非刚性模型的相似性分析。实例证明该方法对含有拓扑变化的非刚性模型可以得到理想的相似性分析结果。     

SIMULATION AND ESTIMATION OF LONG MEMORY CONTINUOUS TIME MODELS

Abstract. Some general properties of long memory continuous time processes are recalled or proved. Methods of simulation are studied. A comparison with the usual discrete time autoregressive fractionally integrated moving-average filter is made and illustrations are provided. Then, two methods of estimation of the parameters of such a model from a discrete sample are studied, both theoretically and empirically, with Monte Carlo experiments.     

REVERSED RESIDUALS IN AUTOREGRESSIVE TIME SERIES ANALYSIS

Abstract. Both linear and non-linear time series can have directional features which can be used to enhance the modelling and investigation of linear or non-linear autoregressive statistical models. For this purpose, reversed p th-order residuals are introduced. Cross-correlations of residuals and squared reversed residuals allow extensions of current model identification ideas. Quadratic types of partial autocorrelation functions are introduced to assess dependence associated with non-linear models which nevertheless have linear autoregressive correlation structures. The use of these residuals and their cross-correlation functions is exemplified empirically on some deseasonalized river flow data for which a first-order autoregressive model is a satisfactory second-order fit. Parallel theoretical computations are undertaken for the non-linear first-order random coefficient autoregressive model and comparisons are made. While the data are shown to be strongly non-linear, their correlational signatures are found to be convincingly different from those of a first-order autoregressive model with random coefficients.     

基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。