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Motion capture provides the best interface to the understanding of trajectory planning and formations in the central nervous system, which has enormously benefited the research in interactive game and learning, animation, film special effects, health care, and navigation. 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, a ubiquitous real-time low-cost human motion capture system that uses wearable microsensors becomes possible. 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 present a novel upper limb motion estimation algorithm by hierarchical fusion of sensor data and human skeleton constraints. Our method represents the orientations of upper limb segments in quaternion, which is computationally effective and can avoid the singularity problem. To address the nonlinear human body segment motion, a particle filter is proposed to fuse inertial and magnetic sensor data. To compensate for the drift, which is the most challenging issue in motion estimation using inertial sensors, we present a novel solution by modeling the geometrical constraint in elbow joint and fuse the constraint in the framework of particle filter to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results compared to the ground truth. [ABSTRACT FROM AUTHOR]

The lateral bending test is routinely used by clinicians for the preoperative assessment of spinal mobility. The evaluation of bending motion is usually based on the qualitative analysis of a two-dimensional (2-D) antero-posterior X-ray image. The aim of this paper is to introduce a novel three-dimensional (3-D) reconstruction technique that is a prerequisite for the quantitative 3-D analysis of lateral bending motion. An algorithm was developed for the 3-D reconstruction of the spine from a single X-ray image. The X-ray is calibrate using a small calibration object and an explicit calibration algorithm. The information contained in the single X-ray is completed by registering a priori 3LD geometric models of individual vertebrae. Part of the error yielded by the 3-D12-D registration is corrected by a vertebral alignment constraint that aims to minimize intervertebral dislocations. Three-di- mensional models of 15 different scoliosis patients, obtained from a standard stereo-radiographic 3-D reconstruction, were used in simulation and validation experiments. Experimental results show that the new method is robust and accurate. With pessimistic levels of simulated noise, the average root mean square reconstruction error is 2.89 mm, which is appropriate for common clinical applications. [ABSTRACT FROM AUTHOR]

Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images. [ABSTRACT FROM AUTHOR]

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods. [ABSTRACT FROM AUTHOR]

A novel algorithm for the localization of event-related potential (ERP) sources within the brain is proposed here. In this technique, spatial notch filters are developed to exploit the multi-channel electroencephalogram data together with a model of ERP with variable parameters in order to accurately localize the corresponding ERP signal sources. The algorithm is robust in the presence of reasonably high noise. The performance of the proposed system has been compared to that of linear constrained minimum variance (LCMV) beamformer for different noise and correlation levels and its superiority has been demonstrated. [ABSTRACT FROM AUTHOR]