Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

1. Interaction. The interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help. 2. Interdependence. Learners must depend on one another to accomplish a common objective. Each group member has specific tasks to complete, and successful completion of each member’s tasks results in attaining the overall group objective.

将“Cooperative Learning”融入课堂——浅谈英语素质教育

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

A gait analysis method is implemented by a gait analysis system including a sensing unit, a storing unit storing a plurality of computing programs, and a processing unit electrically connected to the sensing unit and the storing unit, and comprises steps of: sensing a gait by the sensing unit to output a sensing signal, wherein a gait cycle includes a stance phase, a push-off phase, a swing phase and a heel-strike phase; obtaining a signal vector magnitude (SVM) and a signal magnitude subtraction (SMS) by the processing unit according to the sensing signal; identifying the stance phase, push-off phase, swing phase and heel-strike phase according to the SVM and SMS, wherein the push-off phase, swing phase and heel-strike phase are determined according to a dynamic threshold; and implementing a classification of the gait according to the stance phase, push-off phase, swing phase and heel-strike phase.

Gait analysis method and gait analysis system

This paper presents a self-adaptive recurrent neuro-fuzzy inference system (R-SANFIS) for dealing with dynamic problems. The proposed recurrent system possesses two salient features: 1) it incorporates fuzzy basis functions (FBFs) with dynamic elements for better approximation of nonlinear dynamic functions, and 2) it is capable of translating the complicated behaviors of dynamic systems into a set of simple linguistic "dynamic" rules and state-space equations as well. A systematic self-adaptive learning algorithm has been developed to construct the R-SANFIS with a parsimonious network structure and fast parameter learning convergence. Computer simulations and the performance comparisons with some existing recurrent networks on identification and control of nonlinear dynamic systems have been conducted to validate the effectiveness of the proposed R-SANFIS.

An efficient recurrent neuro-fuzzy system for identification and control of dynamic systems

This paper presents an efficient data preprocessing procedure for the support of vector clustering (SVC) to reduce the size of a training dataset Solving the optimization problem and labeling the data points with cluster labels are time-consuming in the SVC training procedure This makes using SVC to process large datasets inefficient We proposed a data preprocessing procedure to solve the problem The procedure contains a shared nearest neighbor (SNN) algorithm, and utilizes the concept of unit vectors for eliminating insignificant data points from the dataset Computer simulations have been conducted on artificial and benchmark datasets to demonstrate the effectiveness of the proposed method

/pdf/an-efficient-data-preprocessing-procedure-for-support-vector-3ou7n7lpaa.pdf

An efficient data preprocessing procedure for support vector clustering

A moving trajectory generation method includes the following steps of: sensing a movement of a writing device in the air by a moving trajectory sensing module so as to generate a moving trajectory and then generate a moving trajectory signal; generating a virtual projection plane and a trajectory projection plane by a trajectory reconstruction unit of a trajectory reconstruction module according to the moving trajectory signal; calculating an angle between the virtual projection plane and the trajectory projection plane, and generating a transformation matrix according to the angle; transforming the virtual projection plane to the trajectory projection plane according to the transformation matrix; and projecting the moving trajectory to the trajectory projection plane so as to obtain a two-dimensional trajectory signal.

Moving trajectory generation method

An emotion regulation system and a regulation method thereof are disclosed. A physiological emotion processing device of the emotion regulation system comprises an emotion feature processing unit and a physiological emotion analyzing unit. The emotion feature processing unit outputs a physiological feature signal according to a physiological signal generated by a user listening to a first music signal. The physiological emotion analyzing unit analyzes the user's physiological emotion according to the physiological feature signal and generates a physiological emotion state signal. A music feature processing unit of a musical emotion processing device obtains corresponding music feature signals from music signals. A music emotion analyzing processing unit analyzes the music feature signals to obtain musical emotions of the music signals and outputs a corresponding second music signal to the user according to the physiological emotion state signal and a target emotion.

Jeen-Shing Wang

Papers

Gait analysis method and gait analysis system

An efficient recurrent neuro-fuzzy system for identification and control of dynamic systems

An efficient data preprocessing procedure for support vector clustering

Moving trajectory generation method

Emotion regulation system and regulation method thereof