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Early automatic detection of cardiovascular diseases is of great importance to provide timely treatment and reduce fatality rate. Although many efforts have been devoted to detecting various arrhythmias, classification of other common cardiovascular diseases still lacks comprehensive and intensive studies. This work aims at developing an automatic diagnosis system for myocardial infarction, valvular heart disease, cardiomyopathy, hypertrophy, and bundle branch block, based on the clinic recordings provided by PTB Database. The proposed diagnosis system consists of the components as baseline wander reduction, beat segmentation, feature extraction, feature reduction and classification. The selected features are the location, amplitude and width of each wave, exactly the parameters of ECG dynamical model. We also propose a mean shift algorithm based method to extract these features. To demonstrate the availability and efficacy of the proposed system, we use a total of 13,564 beats to conduct a large scale experiment, where only 25% beats are utilized to train the eigenvectors of generalized discriminant analysis in the feature reduction phase and 25% beats are applied to train the support vector machine in the classification phase. The average sensitivity, specificity and positive predicitivity for the test set, containing 75% beats, are respectively 96.06%, 99.32% and 97.29%.

This paper introduces a face detection, classification and counting system that is robust and works in real-time. It tracks multiple people, which is useful for face counting. The classification problem is defined as differentiating and then classifying the front of a face into Asian or non-Asian categories. The first step, is principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we employ support vector machine (SVM) for the training process and combine different SVM classifiers to create new classifiers, which improves the classification rate. Based on this, we can count the number of Asians and non-Asians. Experiments show that our system achieves a classification rate of 82.5% based on a database containing 750 face images from FERET.

Detecting human abnormal behaviors in a crowded situation is a challenging problem for public security departments. Human abnormal behaviors are seen either in an individual or in a crowd. This paper is focused on the latter, which is more crucial at some important spots and has been less studied. To achieve this goal, we define two categories of video energy based on intensity variation and motion features and adopt two surveillance methods for the two energy accordingly. Using wavelet analysis of the energy curves, we have obtained a result which shows that both methods can be used to deal with crowd modeling and real-time surveillance satisfactorily. A comparison of the two methods is then made in the actual environment in a metro surveillance system.

This paper presents a method for modeling abnormal human gait using hidden Markov model under the framework of a shoe-integrated system. The intelligent system focuses on modeling the following patterns: normal gait, toe in abnormality, and toe out abnormality. In the developed prototype, an inertial measurement unit (IMU) consisting of three-dimensional gyroscopes and accelerometers is employed to measure the angular velocities and accelerations of the foot. Four force sensing resistors (FSRs) and one bend sensor are arranged on the insole of each foot for force and flexion information acquisition. The proposed method is mainly based on supervised Principal Component Analysis (SPCA) for feature generation and hidden Markov model (HMM) for multi-pattern modeling. The "similarity distance measure" criterion is introduced to do model-to-model evaluation. Experimental results demonstrate the proposed approach is robust and efficient in detecting abnormal gait patterns. Our goal is to provide a cost-effective system for detecting gait abnormalities in order to assist persons with abnormal gaits in developing a normal walking pattern in their daily life.

Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.

Recently, audio analysis has been proposed by some researchers for surveillance application. Compared with video surveillance, the effectiveness of audio surveillance is not influenced by the occlusions. In this paper, we propose a learning based approach to intelligent audio surveillance in household environment. This approach can be applied to static or mobile audio surveillance systems which work solely or work as the supplements of video surveillance systems. After extracting Mel frequency cepstral coefficients (MFCC) from a 1.0 s waveform, a classifier trained from a labeled dataset using modest AdaBoost is employed to determine whether this waveform is normal or abnormal. The result and simulation show the effectiveness of this approach.

This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

In this paper, an efficient and low-cost cellphone-commandable mobile manipulation system is described. Aiming at house and elderly caring, this system can be easily commanded through common cellphone network to efficiently grasp objects in household environment, utilizing several low-cost off-the-shelf devices. Unlike the visual servo technology using high quality vision system with high cost, the household-service robot may not afford to such high quality vision servo system, and thus it is essential to use some of low-cost device. However, it is extremely challenging to have the said vision for precise localization, as well as motion control. To tackle this challenge, we developed a realtime vision system with which a reliable grasping algorithm combining machine vision, robotic kinematics and motor control technology is presented. After the target is captured by the arm camera, the arm camera keeps tracking the target while the arm keeps stretching until the end effector reaches the target. However, if the target is not captured by the arm camera, the arm will take a move to help the arm camera capture the target under the guidance of the head camera. This algorithm is implemented on two robot systems: the one with a fixed base and another with a mobile base. The results demonstrated the feasibility and efficiency of the algorithm and system we developed, and the study shown in this paper is of significance in developing a service robot in modern household environment.