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

This paper presents two accelerometer-based metabolic equivalent (MET) estimation methods: 1) MET regression models estimation; and 2) a mono-exponential MET estimation equation. The MET estimation methods can predict MET for the following seven categories of activities: sitting, standing, walking upstairs, walking downstairs, level walking, running, and cycling. The intensity of these activities ranged from sedentary (≤ 1.5 METs) to vigorous (> 6 METs), where 1 MET is equivalent to the metabolic rate associated with sitting. First, MET regression model estimations were developed, including a single MET regression model and multiple MET regression models. Then, the second estimation method, a mono-exponential MET estimation equation, was developed to enhance estimation accuracy in non-steady and steady states during exercise. The experimental results successfully validate the effectiveness of the proposed method, the mono-exponential MET estimation equation, which substantially ameliorates estimation errors in non-steady states and achieves satisfactory accuracy for both non-steady and steady states. Keywords-accelerometer, energy expenditure, metabolic equivalent of task, regression model estimation, non-steady state.

/pdf/accelerometer-based-energy-expenditure-estimation-methods-5c11cs9rkp.pdf

Accelerometer-based energy expenditure estimation methods and performance comparison

The paper introduces a novel defuzzification procedure to segment the input domains and assign selectable gain factors. With proper factor values in the defuzzifier, a fuzzy logic controller can speed-up the transient response without affecting stability. In addition, an instability due to inappropriate control rules can be accommodated. Also, the proposed defuzzifier can improve the system performance by using a genetic algorithm to learn the gain factors.

A modified defuzzifier for control of the inverted pendulum using learning

This paper focuses on the development of model dimension/order determination algorithms for determining minimal dimensions/orders of recurrent neural networks using only input-output measurements of unknown systems. We present two types of model dimension/order determination approaches. The first type is named all-in-one strategy that includes the minimum description length (MDL) principle and the eigensystem realization algorithm (ERA). This type is capable of identifying the model dimension/order and model parameters simultaneously. The other type is named divide-and-conquer strategy that includes the Lipschitz quotients and false nearest neighbors (FNN). This type usually requires additional parameter optimization algorithms to estimate the model parameters for closely emulating the dynamic behavior of unknown systems. The effectiveness of these four algorithms has been validated through nonlinear dynamic system identification examples. In addition, we provide performance comparisons and discussion on the characteristics of these four algorithms as method-selection guidelines.

Minimal model dimension/order determination algorithms for recurrent neural networks

This paper presents a hybrid predictor for the CATS (competition on artificial time series) benchmark. Considering the time series as a sum of two components: the major trend and a residual series, we tackled the prediction problem by a hybrid predictor consisting of two models - a kernel regression model and a recurrent neuro-fuzzy model. The kernel regression model based on Gaussian function expansions was first applied to predict the major trend of the time series. The time series was sectioned into several data sets to obtain the best-fitting regression model. Subsequently, the recurrent neuro-fuzzy model associated with a learning algorithm was used to predict the dynamics of the residual series. The learning algorithm has been developed to construct a minimum size of the recurrent model in state-space representation. The best prediction results were presented and discussed.

A hybrid predictor for time series prediction

This study presents the nonvideo, noninvasive, automatic, and on-site monitoring system employing ultrasonic transducers to detect the behavior in sows before, during and after parturition. An ultrasonic transmitting/receiving (T/R) circuit of 40kHz is mounted above a conventional parturition bed. The T/R units use ultrasonic time-of-flight (TOP) ranging technology to measure the height of the confined sows at 8 predetermined locations. It is suggested that the system could be applied to automatic prediction of sow parturition, with automatic notification of remote management personnel so human attendance at the birth could reduce rates of sow and piglet mortality.

Jeen-Shing Wang

Papers

Accelerometer-based energy expenditure estimation methods and performance comparison

A modified defuzzifier for control of the inverted pendulum using learning

Minimal model dimension/order determination algorithms for recurrent neural networks

A hybrid predictor for time series prediction

Quantification of preparturition restlessness in crated sows using ultrasonic measurement