Magnetic neural stimulation of straight neurons with bends (1) in a semi-infinite volume conductor with a planar interface and (2) in the model of the human head is analyzed. Two stimulating coils, namely the double-square and the double circular, producing the magnetic field for the neuron stimulation are considered. The results indicate that the stimulating coil characteristics (size, shape and location) and the neuron shape affect the magnitude and location of the stimulation. The activating function, defined as the electric field derivative along the neuron, has two components. One component depends on the derivative of the electric field along the straight section of the neuron, and the other on the field magnitude. For bent neurons in a semi-infinite volume conductor, an analytical expression of the activating function (the stimulus) of the neuron was derived. The maximal stimulation point is at the bend of the nerve and its position depends on the nerve shape and coil parameters. The analysis also shows a better performance (a stronger stimulus) for a double-circular (figure eight) coil than for a double-square coil of comparable size. Stimulating bent neurons in the human head is also analyzed. The head model consists of an outer sphere representing the skull and scalp and two inner spheres such that each represents one half of the brain. The 3D-impedance method was used to obtain the induced electric fields by the double-square and double-circular coils. Quasi-static conditions are assumed. The geometry of the neuron in this model approximates the normal configuration of motor neurons in the human head. The analysis shows that the stimulation occurs almost at the highest point on the nerve (the closest point to the coil) with the coil positioned in such a way that its center is directly over the highest point on the nerve. It is also shown that the double-square coil produces a stronger stimulus than the double-circular coil. This result is in contradiction with that for a bent neuron in a semi-infinite volume conductor, however, it agrees with the results obtained for a straight neuron [1]. The analysis of bent neurons represents a more realistic approximation of the actual anatomy. The results of this analyses confirms the conclusions and, therefore, usefulness of simplified analyses of straight neurons. The results are expected to be of some use in clinical applications where non-invasive neural stimulation is desired and location of stimulation needs to be known. / Graduate / 0544

Magnetic neural stimulation of straight neurons with bends (1) in a semi-infinite volume conductor with a planar interface and (2) in the model of the human head is analyzed. Two stimulating coils, namely the double-square and the double circular, producing the magnetic field for the neuron stimulation are considered. The results indicate that the stimulating coil characteristics (size, shape and location) and the neuron shape affect the magnitude and location of the stimulation. The activating function, defined as the electric field derivative along the neuron, has two components. One component depends on the derivative of the electric field along the straight section of the neuron, and the other on the field magnitude. For bent neurons in a semi-infinite volume conductor, an analytical expression of the activating function (the stimulus) of the neuron was derived. The maximal stimulation point is at the bend of the nerve and its position depends on the nerve shape and coil parameters. The analysis also shows a better performance (a stronger stimulus) for a double-circular (figure eight) coil than for a double-square coil of comparable size. Stimulating bent neurons in the human head is also analyzed. The head model consists of an outer sphere representing the skull and scalp and two inner spheres such that each represents one half of the brain. The 3D-impedance method was used to obtain the induced electric fields by the double-square and double-circular coils. Quasi-static conditions are assumed. The geometry of the neuron in this model approximates the normal configuration of motor neurons in the human head. The analysis shows that the stimulation occurs almost at the highest point on the nerve (the closest point to the coil) with the coil positioned in such a way that its center is directly over the highest point on the nerve. It is also shown that the double-square coil produces a stronger stimulus than the double-circular coil. This result is in contradiction with that for a bent neuron in a semi-infinite volume conductor, however, it agrees with the results obtained for a straight neuron [1]. The analysis of bent neurons represents a more realistic approximation of the actual anatomy. The results of this analyses confirms the conclusions and, therefore, usefulness of simplified analyses of straight neurons. The results are expected to be of some use in clinical applications where non-invasive neural stimulation is desired and location of stimulation needs to be known. / Graduate / 0544

The main objectives of the present thesis were to define the dimension of head injuries in Sweden over a longer period and to present a Finite Element (FE) model of the human head which can be used for preventive strategies in the future. The annual incidence of head injuries in Sweden between 1987 and 2000 was defined at over 22 000, cases most of which were mild head injuries. In contrast to traffic accidents, head injuriy due to fall was the most important etiology. Of special interest was that the number of hematoma cases has increased. A detailed and parameterized FE model of the human head was developed and used to evaluate the effects of head size, brain size and impact directions. The maximal effective stresses in the brain increased more than a fourfold, from 3.6 kPa for the smallest head size to 16.3 kPa for the largest head size using the same acceleration impulse. The size dependence of the intracranial stresses associated with injury is not predicted by the Head Injury Criterion (HIC). Simulations with various brain sizes indicated that the increased risk of Subdural Hematoma (SDH) in elderly people may to a part be explained by the reduced brain size resulting in a larger relative motion between the skull and the brain with distension of bridging veins. The consequences of this increased relative motion due to brain atrophy cannot be predicted by existing injury criteria. From studies of the influence of impact directions to the human head, the highest shear strain in the brain stem is found for a Superior-Inferior (SI) translational impulse, and in the corpus callosum for a lateral rotational impulse when imposing acceleration pulses corresponding to the same impact power. It was concluded that HIC is unable to predict consequences of a pure rotational impulse, while the Head Impact Power (HIP) criterion needs individual scaling coefficients for the different terms to account for differences in intracranial response due to a variation in load direction. It is also suggested that a further evaluation of synergic effects of the directional terms of the HIP is necessary to include combined terms and to improve the injuryprediction. Comparison of the model with experiments on localized motion of the brain shows that the magnitude and characteristics of the deformation are highly sensitive to the shear properties of the brain tissue. The results suggest that significantly lower values of these properties of the human brain than utilized in most 3D FE models today must be used to be able to predict the localised brain response of an impact to the human head. There is a symmetry in the motion of the superior and inferior markers for both the model and the experiments following a sagittal and a coronal impact. This can possibly be explained by the nearly incompressible properties of brain tissue. Larger relative motion between the skull and the brain is more apparent for an occipital impact than for a frontal one in both experiments and FE model. This correlates with clinical findings. Moreover, smaller relative motion between the skull and the brain is more apparent for a lateral impact than for a frontal one for both experiments and FE model. This is thought to be due to the supporting structure of the falx cerebri. Such a parametrized and detailed 3D model of the human head has not, to the best knowledge of the author, previously been developed. This 3D model is thought to be of significant value for looking into the effects of geometrical variations of the human head. / QC 20100428

With the recent developments in Computer Vision and Pattern Recognition, surveillance applications are equipped with the capabilities of event/activity understanding and interpretation which usually require recognizing humans in real world scenes. Real world scenes such as airports, streets and train stations are complex because they involve many people, complicated occlusions and cluttered backgrounds. Although complex real world scenes exist, human detectors have the capability to locate pedestrians accurately even in complex scenes and visual trackers have the capability to track targets in cluttered environments. The integration of visual object detection and tracking, which are the fundamental features of available surveillance applications, is one of the solutions for multi-human tracking problem in crowded scenes which is studied in this thesis. In this thesis, human body part detectors, which are capable of detecting human heads and human upper body parts, are trained with Support Vector Machines (SVM) by using Histogram of Oriented Gradients (HOG), which is one of the state-of-the-art descriptor for human detection. The training process is elaborated by investigating the effects of the parameters of the HOG descriptor. The human heads and upper body parts are searched in the region of interests (ROI) computed by detecting motion. In addition, these human body part detectors are integrated with a multi-human tracker which solves the data association problem with the Multi Scan Markov Chain Monte Carlo Data Association (MCMCDA) algorithm. Associated measurements of human upper body part locations are used for state correction for each track. State estimation is done through Kalman Filter. The performance of detectors are evaluated using MIT Pedestrian dataset and INRIA Human dataset.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 4 uppsatser.

Understanding of the shape and size of different features of human body from the scanned data is necessary for automated design and evaluation of product ergonomics. The traditional method of finding required body dimensions by manual measurements (Anthropometry) has many sociological, logistical and technical drawbacks such as prolonged time, skilled researcher for consistency and accuracy of measurements, undesirable physical contact between the subject and the researcher, required presence of people from different demographic categories or travel of researcher with equipments. If these di- mensions are extracted from the stored digital human models, above drawbacks can be eliminated. With the emergence of laser based 3d scanners, it is now possible generate a large database of surface models of humans from different demographic backgrounds but the automatic processing of 3d meshes is under development. Though some commercial packages are available for extraction of a limited number of dimensions from full body scans, mostly belonging to topologically separable body parts like hands and legs, the dimensions associated with head and face are particularly not available in public domain. The processing of surface models of head and face from the automatic measurement point of view is also not discussed in literature though this type of data has many practical applications like ergonomic design of close-fitting products like respiratory masks,ophthalmic frames (spectacles), helmets and similar head-mounted devices; Creation of a facial feature database for face modeling coding and reconstruction and for use in forensic sciences; Automated anthropological surveys and Medical growth analysis and aesthetic surgery planning. Hence, in this thesis, a computational framework is developed for automatic detection, recognition and measurement of important facial features namely eyes, eyebrows, nose, mouth and moustache (if applicable) from scanned head and shoulder polyhedral models. After preprocessing the scanned mesh manually to fill holes and remove singular vertices, discrete differential geometric operators were implemented to compute surface normals and curvatures. Mean curvature magnitude was used as the primary metric to segment the mesh using morphological watershed algorithms which treat the mesh as a height map and separate the regions according to the water catchment basins. After visualization it was hypothesized that the important facial features consist of relatively high curvature regions and based on this hypothesis a much faster approach was then employed based on mathematical morphology to group the high curvature vertices into regions based on adjacency. The important feature regions isolated this way were then identified and labeled to be belonging to different facial features by a decision tree based on their relative spatial disposition. Adaptive selection of parameters was incorporated later to ensure robustness of this algorithm. Critical points of these identified features are recognized as the standard landmarks associated with those primary facial features. A number of clinically identified landmarks lie on the facial mid-line. An efficient algorithm is proposed for detection and processing of the mid-line using a point sampling technique which is fast and has immunity to noise in the data. An algorithm to find shortest path between two vertices while traveling along the edges is implemented to measure on-surface distances and to isolate the nose. Complete program comprising of curvature and surface normal computations, seg- mentation and identification of 6 important features, facial mid-line processing, detection of total 17 landmarks and shortest path computations to separate nose takes about 2 minutes to work including visualization on a full resolution mesh of typically 2,15,521 Vertices and 4,30,560 Faces. The algorithm was tested successfully on more than 40 faces with minor exceptions. The results match human perception. The computed measurements were also compared with the physical measurements for a few subjects, the measurements were found to be in good agreement and satisfactory for its usage in product ergonomics and clinical applications.