Imbalance of autonomic nerve is the cause of lots of cardiovascular and psychological diseases. Many studies have shown that Heart Rate Variability Biofeedback through respiratory training is an effective way to regulate the status of human autonomic nerves. However, due to the immature quantification of Respiratory Sinus Arrhythmia, the respiratory training process cannot be optimal personalized. In this paper, we propose the Cardiopulmonary Resonance Indices based on the Cardiopulmonary Coupling Model to calculate the personalized optimal breathing training frequency. Then the respiratory feedback training system is constructed to provide real-time audio-visual guidance for each subject. The results in the clinical trial of pregnant women in their third trimester showed that, compared to control group, with the intervention of personalized respiratory feedback training at home, pregnant women could stabilize their blood pressure and blood glucose, relieve mental stress, as well as exert positive effects on fetal development. This provides a new possibility of the autonomic imbalance treatment.

Based on an EMG-adjusted method in neuromusculoskeletal model, this study aims to predict the individual muscle force in shoulder and elbow during table tennis strokes. Muscle force estimation makes muscle activation analysis more physiological in sports. Twenty subjects, divided into professional group and amateur group, were adopted in this study. They were asked to do a basic stoke motion: backhand block. Surface electromyography (sEMG) of nine muscles was recorded, as well as the motion data collected by three inertial sensors. A Hill-type musculotendon model was then adopted to estimate individual muscle force by combining adjusted sEMG and motion data. The result shows that the method can estimate individual muscle force during table tennis strokes accurately, and the two groups show significant difference in muscle force of shoulders and elbows.

Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.

In the past decade, golf has stimulated people’s great interest and the number of golf players has increased significantly. Therefore, how to train a golfer to make a perfect swing has attracted extensive research attentions. Among these researches, the most important step is to capture and reconstruct the swing movement in a transportable and non-intrusive way. Restricted by the development of present depth imaging devices, the initial captured swing movement may not be acceptable due to occlusions and mixing up of body parts. In this paper, to restore motion information from self-occlusion and reconstruct 3D golf swing from low resolution data, a Dynamic Bayesian Network (DBN) model based golf swing reconstruction algorithm is proposed to increase the capture accuracy considering the spatial and temporal similarities of swing between different golfers. A Smart Motion Reconstruction system for Golf swing, SMRG, is presented based on the DBN model with a popular depth imaging device, Kinect, as capturing device. Experimental results have proved that the proposed system can achieve comparable reconstruction accuracy to the commercial optical motion caption (OMocap) system and better performance than state of art modification algorithms using depth information.

Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measurement is of great significance. Machine-learning methods have shown the potential to derive the relationship between physiological signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard.

Quantitative measures of autonomic regulation of cardiovascular system have clinical and prognostic value in a variety of cardiovascular diseases. This paper proposes a model-based method in the measurement of baroreflex sensitivity, sympathetic and parasympathetic activity. The method measures the continuous blood pressure and heart rate in orthostatic scenario, models the baroreflex and sympathetic regulation process, solves for personalized model parameters by optimization using measured blood pressure and heart rate variations. Experimental results have shown the validation of the quantitative measures and the effectiveness of the method.

Commercial optical human motion capture systems perform well in studio-like environments, but they do not provide solution in daily-life surroundings. Micro-sensor motion capture has shown its potentials because of its ubiquity and low cost. We present an ambulatory low-cost real-time motion capture system using wearable micro-sensors (accelerometers, magnetometers and gyroscopes), which can capture and reconstruct human motion in real-time almost everywhere. It mainly consists of three parts: a sensor subsystem, a data fusion subsystem and an animation subsystem. The sensor subsystem collects human motion signals and transfers them into the data fusion subsystem. The data fusion subsystem performs sensor fusion to obtain motion information, i.e., the orientation and position of each body segment. Using the motion information from the data fusion subsystem, the animation subsystem drives the avatar in the 3D virtual world in order to reconstruct human motion. All the processes are accomplished in real-time. The experimental results show that our system can capture motions and drive animations in real-time vividly without drift and delay. And the output from our system can be made use of in film-making, sports training and argument reality applications, etc.

Accurate human movement estimation techniques are widely used in various applications, such as robotics, human-machine interaction, sports, and rehabilitation. With rapid advances in microsensors, human movement estimation using wearable micro inertial sensors has become an active research topic. The main challenges for the wearable sensor motion estimation are the inertial sensor drift problem and the linear acceleration interference problem. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion estimation. In this paper, we take the upper limb as our research subject and present a novel upper limb movement estimation algorithm to cope with these two challenges by adaptive fusion of sensor data and human skeleton constraint. In the sensor fusion part, a quaternion-based unscented Kalman filter is invoked to fuse the gyroscope, accelerometer, and magnetometer measurement information. In the Kalman filter framework, an acceleration interference detection scheme is implemented based on the exponentially discounted average of the normalized innovation squared (NIS). According to the detection results, the process and measurement noise levels are scaled up or down automatically. To further compensate for the drift, we present a novel solution by modeling geometrical constraint in the elbow joint and fuse the constraint to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results in comparison to the BTS SMART-D optical motion tracker.

This paper proposes an ambulatory algorithm to estimate Center of Mass (CoM) displacement of the subjects for Micro-sensor Motion Capture (MMoCap) system. Estimation of the CoM displacement is based on gait analysis and segmental kinematics. In our MMoCap system, the human body is modeled as a kinematic chain of rigid segments linked by joints, and the pelvis is considered as the root joint in the model. Each joint's relative positions are obtained from hierarchical transformations. The CoM displacement is imported into the root joint of a human skeleton model to track human locomotion with reference to the global coordinate system. Performance of the algorithm is evaluated using simulated sensor data obtained from an optical motion capture database and also by our ambulatory MMoCap system. The experimental results show robust and continuous tracking of CoM movements while a subject is walking.

Heart Rate Variability (HRV) is proven to be related to critical cardiovascular diseases. However, HRV is related to other factors as well, among which the activity is the major one. A novel activity-aware HRV analysis method is proposed to improve its performance on daily life ECG data. First, activity types and intensity are obtained by processing 3-axis acceleration signals. Then heart rate data extracted from ECG signal are segmented according to the activity types and intensity. The heart rate segments are evaluated to determine its suitability for HRV analysis. The suitable segments are further processed to remove outliers and slow non-stationary trends caused by activity instability and other factors. Finally, HRV analysis is conducted on these suitable and processed segments, and the results are labeled with activity context information. Thus, HRV s under different activity contexts are obtained. The advantages of activity-aware HRV are: First, affection of activity on HRV stability is reduced. Second, we have gained HRV dynamics measure against activity types and intensity.

Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health-care and navigation. Because of the agility, upper limb motion estimation is the most difficult in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and estimate their movements separately; therefore, the estimated motion are always with serious distortion. In the paper, we proposed a novel ubiquitous upper limb motion estimation method using wearable micro-sensors, which concentrated on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure of upper limb as a link structure with 5 degrees of freedom was firstly proposed to model human upper limb motion. After that, parameters were defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb were derived, and an Unscented Kalman filter was invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.

The use of wearable micro-sensors provides an easy-to-use and cost-efficient way for human motion capture. It is essential but difficult to estimate the global displacements of human walking in micro-sensor motion capture system. This paper proposes a novel method to estimate the global displacements of walking based on the pedestrian dead reckoning technique. The global displacements are obtained from the estimated walking distance and the heading direction. Walking distance is calculated using the extension and flexion angles of the lower limbs. Heading is estimated by the fusion of magnetometer and gyroscope readings accomplished by a complementary Kalman filter. The displacements are used to drive a skeleton model to animate the walking movements of the subject. Walking experiments have demonstrated that the proposed method can estimate the displacements accurately and animate the human walking motions smoothly and vividly.

Tracking multiple maneuvering targets remains a challenge because of clutter and spurious targets. We propose a Signature Driven multiple target Tracking (SDT) method which uses target signature in spectral, spatial and temporary spaces as well as the Markov property of target movement, so that the data association process in SDT is very efficient and effective. The experimental results have shown outstanding performance.

Recent advances in wireless communication and sensor technologies have opened up a new paradigm in healthcare industry. More and more attentions in the wireless body area network (WBAN) have been received recently. However, the limited network resources constrain the application development in WBAN. In this paper, we present a service-specific middleware architecture to bridge the gap between the application development and the underlying network sensor devices. The proposed middleware is able to provide functions including network initialization and registration, service announcement, sensor actuation and control for flexible multiple modalities data acquisition, and real-time network service management. The middleware has been implemented and tested with a healthcare monitoring test-bed using Imperial College sensor nodes.

Hip angle is a major parameter in gait analysis while gait analysis plays important role in health-care, animation and other applications. Accurate and robust estimation of hip angle in ambulatory environment remains a challenge because the non-linear nature of thigh movement has not been well studied yet. Although piece-wise linear model is effective to approximate the non-linear model, the current solutions, Gaussian Particle Filter (GPF), is suffering from heavily computation load, which makes the ambulatory hip angle estimation in real time impossible. In this paper, we propose to use Discrete Wavelet Transform to detect major gait events from the measurements of the wearable accelerometer that are attached to the thigh. Based on the detection result, a corresponding linear hip angle dynamic is selected and an Unscented Kalman Filter (UKF) is invoked to estimate the hip angle. The experimental results have shown that the proposed methods can achieve robust and accurate hip angle estimation, and with much less computation loads over the previous work on the ambulatory gait analysis.

We apply the probability hypothesis density (PHD) filter to track a random number of pedestrians in image sequences. The PHD filter is implemented using particle filter. How to design importance functions of the particle PHD filter remains a challenge, especially when targets can appear, disappear, merge, or split at any time. To meet this challenge, we have modeled the targets into two categories: survival objects and spontaneous birth objects. Based on the model, we have derived the data-driven importance function for a particle PHD filter and applied to pedestrians tracking where people or groups appear, merge, split, and disappear in the field of view of a camera. The experimental results have demonstrated the effectiveness of the particle PHD filter using the proposed importance function in tracking random number of pedestrians and deriving their locations.

A physical activity monitoring system by data fusion in body sensor networks is presented in this paper, which targets at providing body status information in real time and identifying body activities. By fusion of data collected from several accelerometer sensors placed on different parts of the body, the activities can be identified and tracked Mathematical approaches employed in the system include Kalman filter and hidden Markov model. With the proposed system architecture, these algorithms are distributed to different components of the system. The proposed system is applied to monitoring and identifying daily activities in laboratory and comparatively intensive activities in a gym room. Video-based approach is used as the benchmark to evaluate its performance. Comparative results indicate that, by using the proposed system, body status of daily activities can be estimated with good accuracy in real time, and body activity is identified with high accuracy within short system latency.

This paper presents our work which involves the application of a recursive Bayesian filter, the Gaussian mixture probability hypothesis density (GMPHD) filter, to a visual tracking problem. Foreground objects are detected using statistical background modeling to obtain measurements which are input into the filter. The GMPHD filter explicitly models the birth, survival and death of objects by managing the number of Gaussian components and jointly estimates the time-varying number of objects and their states. A scene-driven method is proposed to initialize the GMPHD filter and model the birth of new objects. The results shows when a person or a group appeared, merged, split, and disappeared in the field of view, the GMPHD filter can track the number and positions at the most time. The scene-driven GMPHD filter can track the birth of new objects faster than the particle PHD filter.

This paper presents the method for the detection and localization of moving targets in passive infrared (PIR) sensor networks in both indoor and outdoor settings. It reports our design and implementation of PIR sensor network, especially, we proposed a detection algorithm, which uses Adaptive Threshold with Constant False Alarm Rate; and developed a localization algorithm using direction search in the grid space of the network. The experimental results have shown that our PIR sensor network can detect and locate the moving targets with reasonable accuracy.

A body activity tracking system based on wearable accelerometers is presented in this paper. The system maintains multiple models for tracking different movements and employs a selection strategy to choose a suitable model. The measured accelerations are converted into flexion angles of body segments after such processing. The system is applied to track ambulatory activities and the result shows that its performance is comparable to that of vision-based and magnetic sensor based approaches in tracking accuracy and smoothness while the computation complexity is very much lessened.

We present in this paper a novel electronic document watermarking method, render sequence encoding (RSE), and then further develop a RSE authentication method for electronic documents. RSE watermarks an electronic document by modulating the display sequences of words or characters. It features large information-carrying capacity and robustness over document format transcoding. The RSE authentication method is based on the NP-complete exact traveling salesman problem, which provides a rigorous foundation for security. The RSE authentication method is secure in the sense it is extremely difficult to forge the authentication process. RSE authentication process is also easy to operate, especially in comparison to digital signatures which requires public key infrastructure for its operation

This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.

This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.

We present a novel solution for authenticating printed paper documents by utilizing the inherent non--repeatable randomness existing in the printing process. For a document printed by a laser-printer, we extract the unique features of the non--repeatable print content for each copy. The shape profiles of this content are used as the feature to represent the uniqueness of that particular printed copy. These features along with some important document content is then captured as the print signature. We present theoretical and experimental details on how to register as well as authenticate this print signature. The security analysis of this technique is also presented. We finally provide experimental results to demonstrate the feasibility of the proposed method.

Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.

Automatic face location in complex scenes is extremely challenging in human face recognition systems. Further more, the facial features detection also plays an important role. The paper presents a scheme for robust face and eyes detection from an image. The scheme uses the Gaussian steerable filter to search and detect the facial feature (preattentive feature) roughly in an image. The face model is investigated to locate the whole face and facial features, such as eyes, nose and mouth. Here, multiple evidences are used in the face location and eyes detection. One important feature is the structural information of the face, i.e. facial components of certain structure. The other is the symmetry property of the face, here only the front face with certain pose variation is considered. It will reduce the computation greatly. For facial components detection, some image features and PCA features are used for verification from the candidates detected before. Experiments show that the algorithm is robust and fast.

A new paradigm for object recognition, so-called recognition-by-recall (Wu, 1997) has been put forward. It leads new research issues in learning. This paper proposes a learning approach for recognition by recall, based on the basic idea of approximate case-based reasoning as an attempt to reach the targets of the object recognition and the formalism of content-based retrieval.

Content-based retrieval of multimedia database calls for content-based indexing techniques. Different from conventional databases, where data items are represented by a set of attributes of elementary data types, multimedia objects in multimedia databases are represented by a collection of features; similarity of object contents depends on context and frame of reference; and features of objects are characterized by multimodal feature measures. These lead to great challenges for content-based indexing. On the other hand, there are special requirements on content-based indexing: to support visual browsing, similarity retrieval, and fuzzy retrieval, nodes of the index should represent certain meaningful categories. That is to say that certain semantics must be added when performing indexing. ContIndex, the context-based indexing technique presented in this paper, is proposed to meet these challenges and special requirements. The indexing tree is formally defined by adapting a classification-tree concept. Horizontal links among nodes in the same level enhance the flexibility of the index. A special neural-network model, called Learning based on Experiences and Perspectives (FEP), has been developed to create node categories by fusing multimodal feature measures. It brings into the index the capability of self-organizing nodes with respect to certain context and frames of reference. An icon image is generated for each intermediate node to facilitate visual browsing. Algorithms have been developed to support multimedia object archival and retrieval using Contlndex.

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An adaptive image-coding system using neural networks is presented. The design of the system is based on the fact that system adaptability is a key to its effectiveness and efficiency. A composite source data model is suggested as a mathematical model for image data. Based on the composite source model, the coding system first classifies image data and then transforms and codes data classes with dedicated schemes. LEP, a reliable learning neural network model that uses experiences and perspectives, is proposed for image data classification using textures. A scheme for learning Karhunen-Loeve (K-L) transform basis arranged in the descent order with respect to eigenvalues in a two-layer linear-forward network is developed. These two learning mechanisms serve as essential parts of the coding system and enhance the adaptability of the system considerably. The experimental results show compressed images of good quality with bit rates as low as 0.1767 bit per pixel.

This paper presents a query language, QPF, and describes its implementation in a knowledge-based geographical information system (KGIS). Using QPF, users can retrieve both symbolic and graphical data, and browse through the entire database without prior knowledge of the contents and organization of the KGIS. Processing of complex queries, such as query by object spatial relations, is guided by knowledge represented in frames that simplifies not only query processing, but also query definition and makes querying user-friendly.