Stick Figure Model Research Articles

Automatic Sitting Pose Generation for Ergonomic Ratings of Chairs.

Human poses play a critical role in human-centric product design. Despite considerable researches on pose synthesis and pose-driven product design, most of them adopt the simple stick figure model that captures only skeletons rather than real body geometries and do not link human poses to the environment (e.g., chairs for sitting). This paper focuses on user-tailored ergonomic design and rating of chairs using scanned human geometries. Fully utilizing the anthropometric information of the human models, our method considers more ergonomic guidelines of chair design (such as pressure distribution and support intensity) and links the geometry of 3D chair models and human-to-chair interactions into the pose deformation constraints of the human avatars. The core of our method is a pose generation algorithm which rigs the user's successive poses through coarse- and fine-level pose deformations. We define a non-linear energy function with contact, collision, and joint limit terms, and solve it using a hill-climbing algorithm. The fitting results allow us to quantitatively evaluate the chair model in terms of various ergonomic criteria. Our method is flexible and effective and can be applied to users with varying body shapes and a wide range of chairs. Moreover, the proposed technique can be easily extended to other furniture, such as desk, bed, and cabinet. Extensive evaluations and a user study demonstrate the efficiency and advantages of the proposed virtual fitting method. Given that our method avoids tedious on-site trying, facilitates the exploration/evaluation of various chair products, and provides valuable feedback for the designers and manufacturers to deliver customized products, it is ideal for online shopping of chairs.

Generalized Lower Limb Joint Angular Phase Space Analysis of Subject Specific Normal and Modified Gait.

This study presents and applies generalized angular phase space analysis to lower limb joint angles of specific subject during normal and modified gait for discrimination of gait and joint angular movements. Case study of an adult healthy male in-vivo and noninvasive kinematic assessment of skin surface adhesive markers at lower limb was performed at human movement lab during normal gait, stiff knee gait and slow running. Musculoskeletal modeling was performed using AnyGait v.0.92 morphing Twente Lower Extremity Model (TLEM) to match the size and joint morphology of the stick-figure model. Inverse kinematics was performed obtaining hip, knee and ankle joint flexion-extension angular displacements, velocities and accelerations. Generalized phase space analysis was applied to lower limb joint angular displacements, velocities and accelerations. Directional statistics was applied to generalized phase planes with mean direction, resultant length and circular standard deviation assessment. Rayleigh test was employed for directional concentration and coordination assessment, and Watson's $\mathrm{U^{2$ goodness of fit test applied to the von Mises distribution. Results point for the importance of subject specific study, generalized joint angular phase space analysis, comparing results with other normalization methods and validation of applied methods with qualitative clinical analysis.

EFFECT OF OBSERVING DIFFERENT MODEL DEMONSTRATIONS ON THE DEVELOPMENT OF INTERNAL MOTOR REPRESENTATION

This study was designed to investigate the relative effects of observing video, point-light, and stick-figure model demonstrations on the development of internal motor representation of a highly complex sport skill. Forty one novice female and male students were randomly assigned to video, point-light, stick-figure and no-demonstration control groups. Internal motor representation was evaluated by a computer - based test using the error detection paradigm. Participants had to view ten digital photos representing different phases of a Baseball pitching and were instructed to identify by mouse-clicking various movement errors. The test was respectively performed after 5 familiarization trials (pre-test), 3 acquisition blocks of 10 trials (post-test) and one week without practice (retention test). Participants observed related model demonstrations prior to each acquisition block. Results showed that demonstration groups improved their scores in either post-test or retention test; however these improvements were not statistically significant. Moreover, there was no significant difference between groups either in post-test or retention test. The findings are discussed in terms of difficulty of errors, insufficient amount of physical or observational practice, and small sample size.

Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios

Human identification by gait analysis has attracted a great deal of interest in the computer vision and forensics communities as an unobtrusive technique that is capable of recognizing humans at range. In recent years, significant progress has been made, and a number of approaches capable of this task have been proposed and developed. Among them, approaches based on single source features are the most popular. However, the recognition rate of these methods is often unsatisfactory due to the lack of information contained in single feature sources. Consequently, in this paper, a hierarchal and multi-featured fusion approach is proposed for effective gait recognition. In practice, using more features for fusion does not necessarily mean a better recognition rate and features should in fact be carefully selected such that they are complementary to each other. Here, complementary features are extracted in three groups: Dynamic Region Area; Extension and Space features; and 2D Stick Figure Model features. To balance the proportion of features used in fusion, a hierarchical feature-level fusion method is proposed. Comprehensive results of applying the proposed techniques to three well-known datasets have demonstrated that our fusion-based approach can improve the overall recognition rate when compared to a benchmark algorithm.

Recognition of Tennis Serve Performed by a Digital Player: Comparison among Polygon, Shadow, and Stick-Figure Models

The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

Gait posture estimation using wearable acceleration and gyro sensors

A method for gait analysis using wearable acceleration sensors and gyro sensors is proposed in this work. The volunteers wore sensor units that included a tri-axis acceleration sensor and three single axis gyro sensors. The angular velocity data measured by the gyro sensors were used to estimate the translational acceleration in the gait analysis. The translational acceleration was then subtracted from the acceleration sensor measurements to obtain the gravitational acceleration, giving the orientation of the lower limb segments. Segment orientation along with body measurements were used to obtain the positions of hip, knee, and ankle joints to create stick figure models of the volunteers. This method can measure the three-dimensional positions of joint centers of the hip, knee, and ankle during movement. Experiments were carried out on the normal gait of three healthy volunteers. As a result, the flexion–extension (F–E) and the adduction–abduction (A–A) joint angles of the hips and the flexion–extension (F–E) joint angles of the knees were calculated and compared with a camera motion capture system. The correlation coefficients were above 0.88 for the hip F–E, higher than 0.72 for the hip A–A, better than 0.92 for the knee F–E. A moving stick figure model of each volunteer was created to visually confirm the walking posture. Further, the knee and ankle joint trajectories in the horizontal plane showed that the left and right legs were bilaterally symmetric.

Gait analysis using gravitational acceleration measured by wearable sensors

A novel method for measuring human gait posture using wearable sensor units is proposed. The sensor units consist of a tri-axial acceleration sensor and three gyro sensors aligned on three axes. The acceleration and angular velocity during walking were measured with seven sensor units worn on the abdomen and the lower limb segments (both thighs, shanks and feet). The three-dimensional positions of each joint are calculated from each segment length and joint angle. Joint angle can be estimated mechanically from the gravitational acceleration along the anterior axis of the segment. However, the acceleration data during walking includes three major components; translational acceleration, gravitational acceleration and external noise. Therefore, an optimization analysis was represented to separate only the gravitational acceleration from the acceleration data. Because the cyclic patterns of acceleration data can be found during constant walking, a FFT analysis was applied to obtain some characteristic frequencies in it. A pattern of gravitational acceleration was assumed using some parts of these characteristic frequencies. Every joint position was calculated from the pattern under the condition of physiological motion range of each joint. An optimized pattern of the gravitational acceleration was selected as a solution of an inverse problem. Gaits of three healthy volunteers were measured by walking for 20 s on a flat floor. As a result, the acceleration data of every segment was measured simultaneously. The characteristic three-dimensional walking could be shown by the expression using a stick figure model. In addition, the trajectories of the knee joint in the horizontal plane could be checked by visual imaging on a PC. Therefore, this method provides important quantitive information for gait diagnosis.

60-Hertz Electric-Field Exposures in Transmission Line Towers

This article reports on an investigation of 60-Hz electric-field exposures of line workers in 230- to 765-kV transmission line towers. The exposures were based on computations of the unperturbed electric field along climbing routes and at work positions on the towers and on insulated ladders suspended in towers. Computed exposures were expressed in terms of the unperturbed electric field averaged over the body as stipulated by guidelines. For the realistic on-tower positions, the worker's posture, the uniformity of the field, and the field orientation differed from the guideline exposure scenario of standing erect in a vertical uniform field. These differences suggest the need for care in comparing electric-field exposures in towers with guideline limits. The unperturbed nonuniform fields at discrete points near steel and aluminum lattice structures were computed using Monte Carlo methods that model surface and spatial electric fields on and near standard geometrical elements. To estimate a whole-body average, fields were computed at 10 discrete points positioned on segments of an articulated stick-figure model of the human body. The whole-body average field was computed from fields at all the points weighted by the fraction of body volume that the corresponding segment represented. We estimated the average unperturbed electric field, the space potential at the torso, and the induced short-circuit current for 19 climbing and work positions in six towers. The maximum average electric-field exposure during climbing ranged from 10 kV/m for a 230-kV tower to 31 kV/m for a 765-kV tower. Exposures at on-tower work positions were lower than the estimated maximum exposures during climbing. For 500- and 765-kV towers, computed exposures while climbing and at some on-tower positions exceeded the limit of 20 kV/m given in the recently adopted IEEE Standard C95.6 2002. For lower voltage towers, exposures did not exceed 20 kV/m.

Perception of human motion with different geometric models

Human figures have been animated using a variety of geometric models, including stick figures, polygonal models and NURBS-based models with muscles, flexible skin or clothing. This paper reports on experimental results indicating that a viewer's perception of motion characteristics is affected by the geometric model used for rendering. Subjects were shown a series of paired motion sequences and asked if the two motions in each pair were the same or different. The motion sequences in each pair were rendered using the same geometric model. For the three types of motion variation tested, sensitivity scores indicate that subjects were better able to observe changes with the polygonal model than they were with the stick-figure model.

Methods for displaying macromolecular structural uncertainty: Application to the globins

Most molecular graphics programs ignore any uncertainty in the atomic coordinates being displayed. Structures are displayed in terms of perfect points, spheres, and lines with no uncertainty. However, all experimental methods for defining structures, and many methods for predicting and comparing structures, associate uncertainties with each atomic coordinate. We have developed graphical representations that highlight these uncertainties. These representations are encapsulated in a new interactive display program, PROTEAND. PROTEAND represents structural uncertainty in three ways: (1) The traditional way: The program shows a collection of structures as superposed and overlapped stick-figure models. (2) Ellipsoids: At each atom position, the program shows an ellipsoid derived from a three-dimensional Gaussian model of uncertainty. This probabilistic model provides additional information about the relationship between atoms that can be displayed as a correlation matrix. (3) Rigid-body volumes: Using clouds of dots, the program can show the range of rigid-body motion of selected substructures, such as individual α helices. We illustrate the utility of these display modalities by the applying PROTEAND to the globin family of proteins, and show that certain types of structural variation are best illustrated with different methods of display.

Tracking Human Body Motion Based on a Stick Figure Model

Tracking Human Body Motion Based on a Stick Figure Model

Image Perspective Transformationsâ€â€?Urban Scenes

This paper discusses a technique for converting digitized oblique low-altitude aerial photography to gray level imagery as seen from other viewing perspectives. The technique makes use of one- and two-camera perspective transformations. Inputs to these transformations are camera model information and a stick figure representation of the scene. The required camera model information is extracted from the imagery in conjunction with limited map data. Three-dimensional stick figure models are constructed interactively using the camera model and the digitized photograph. The perspective transformations are applied to the gray level image, pixel-by-pixel, and hidden surface corrections are performed to produce the new perspective views. The resulting images maintain the noise and resolution qualities of the original digitized imagery.