A real-time virtual-hand recognition system.

by Tsang Kwok Hang Elton.Thesis submitted in: December 1998.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 78-83).Abstract also in Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Virtual-hand Recognition --- p.5Chapter 2.1 --- Hand model --- p.6Chapter 2.1.1 --- Hand structure --- p.6Chapter 2.1.2 --- Motions of the hand joints --- p.8Chapter 2.2 --- Hand-tracking technologies --- p.9Chapter 2.2.1 --- Glove-based tracking --- p.10Chapter 2.2.2 --- Image-based tracking --- p.12Chapter 2.3 --- Problems in virtual-hand recognition --- p.13Chapter 2.3.1 --- Hand complexity --- p.13Chapter 2.3.2 --- Human variations --- p.13Chapter 2.3.3 --- Immature hand-tracking technologies --- p.14Chapter 2.3.4 --- Time-varying signal --- p.14Chapter 2.3.5 --- Efficiency --- p.14Chapter 3 --- Previous Work --- p.16Chapter 3.1 --- Posture and gesture recognition algorithms --- p.16Chapter 3.1.1 --- Template Matching --- p.17Chapter 3.1.2 --- Neural networks --- p.18Chapter 3.1.3 --- Statistical classification --- p.20Chapter 3.1.4 --- Discontinuity matching --- p.21Chapter 3.1.5 --- Model-based analysis --- p.23Chapter 3.1.6 --- Hidden Markov Models --- p.23Chapter 3.2 --- Hand-input systems --- p.24Chapter 3.2.1 --- Gesture languages --- p.25Chapter 3.2.2 --- 3D modeling --- p.25Chapter 3.2.3 --- Medical visualization --- p.26Chapter 4 --- Posture Recognition --- p.28Chapter 4.1 --- Fuzzy concepts --- p.28Chapter 4.1.1 --- Degree of membership --- p.29Chapter 4.1.2 --- Certainty factor --- p.30Chapter 4.1.3 --- Evidence combination --- p.30Chapter 4.2 --- Fuzzy posture recognition system --- p.31Chapter 4.2.1 --- Objectives --- p.32Chapter 4.2.2 --- System overview --- p.32Chapter 4.2.3 --- Input parameters --- p.33Chapter 4.2.4 --- Posture database --- p.36Chapter 4.2.5 --- Classifier --- p.37Chapter 4.2.6 --- Identifier --- p.40Chapter 5 --- Performance Evaluation --- p.42Chapter 5.1 --- Experiments --- p.42Chapter 5.1.1 --- Accuracy analysis --- p.43Chapter 5.1.2 --- Efficiency analysis --- p.46Chapter 5.2 --- Discussion --- p.48Chapter 5.2.1 --- Strengths and weaknesses --- p.48Chapter 5.2.2 --- Summary --- p.50Chapter 6 --- Posture Database Editor --- p.51Chapter 6.1 --- System architecture --- p.51Chapter 6.1.1 --- Hardware configuration --- p.51Chapter 6.1.2 --- Software tools --- p.53Chapter 6.2 --- User interface --- p.54Chapter 6.2.1 --- Menu bar --- p.55Chapter 6.2.2 --- Working frame and data frame --- p.56Chapter 6.2.3 --- Control panel --- p.56Chapter 7 --- An Application: 3D Virtual World Modeler --- p.59Chapter 7.1 --- System Design --- p.60Chapter 7.2 --- Common operations --- p.62Chapter 7.3 --- Virtual VRML Worlds --- p.65Chapter 8 --- Conclusion --- p.70Chapter 8.1 --- Summaries on previous work --- p.70Chapter 8.2 --- Contributions --- p.73Chapter 9 --- Future Work --- p.75Bibliography --- p.7

Development of a Three-Dimensional Anthropometric Model for Simulating Hand Work

This dissertation aims to increasing the understanding on the variables needed to realistically model a human hand by 1) developing a skeleton-driven 3-D parametric model of the hand, and 2) providing new data for predicting hand strength capabilities. Hand models can be used to understand the effect of hand posture and shape to force exertions while interacting with surfaces. 3D models representing skin and bone surfaces and their axes for use in simulation models were developed from Hand-CT Scans. These models were used to adapt four methods for determining phalange Centers of Rotation (CoR). Sphere- and Ellipsoid-fitted CoRs were considered fixed, with pin-joint rotational axes and simple links. 3D-Reuleaux and ICP-based CoRs were considered instantaneous, with variable link lengths affected by the rotation and gliding of the adjacent anatomical segments. There was a significant difference between fixed and instantaneous CoRs, leading to a more accurate and robust kinematic model. These CoRs were leveraged to develop landmark-free statistical models from 43 clinical hand-CT scans. The 3D-surfaces of flat hand posture, previously developed, were used to standardize the data. Kinematics developed from the ICP method were used to rotate finger segments of fitted hands to 1) obtain hand skin measurements in a common posture, 2) predict whole hand skeleton shape/size, and 3) evaluate final shape predictions in their original postures. Principal component analysis and regression (PCAR) were used to develop statistical models for shape/size prediction of individual finger bone geometries, as well as a whole hand skeleton model with hand skin surface reference points for scaling based on anthropometric data (hand length, hand breadth, hand thickness and sex). The predicted skeleton with the skin reference points can be used as a baseline for any hand surface model to establish kinematics based on internal bone segments. Additionally, to complement data in literature, a study of 12 participants was performed to investigate the effect of hand posture and surface orientation on hand force while pressing a flat surface. Joint moment and finger force distribution data from this study can be incorporated in computerized 3D-models of the hand to compare strength capabilities between postures.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138612/1/rosemfig_1.pd

Correlation between musculoskeletal structure of the hand and primate locomotion: Morphometric and mechanical analysis in prehension using the cross- and triple-ratios

Biometric ratios of the relative length of the rays in the hand have been analyzed between primate species in the light of their hand function or phylogeny. However, how relative lengths among phalanges are mechanically linked to the grasping function of primates with different locomotor behaviors remains unclear. To clarify this, we calculated cross and triple-ratios, which are related to the torque distribution, and the torque generation mode at different joint angles using the lengths of the phalanges and metacarpal bones in 52 primates belonging to 25 species. The torque exerted on the finger joint and traction force of the flexor tendons necessary for a cylindrical grip and a suspensory hand posture were calculated using the moment arm of flexor tendons measured on magnetic resonance images, and were compared among\ua0Hylobates\ua0spp.,\ua0Ateles\ua0sp., and\ua0Papio hamadryas. Finally, the torques calculated from the model were validated by a mechanical study detecting the force exerted on the phalanx by pulling the digital flexor muscles during suspension in these three species. Canonical discriminant analysis of cross and triple-ratios classified primates almost in accordance with their current classification based on locomotor behavior. The traction force was markedly reduced with flexion of the MCP joint parallel to the torque in brachiating primates; this was notably lower in the terrestrial quadrupedal primates than in the arboreal primates at mild flexion. Our mechanical study supported these features in the torque and traction force generation efficiencies. Our results suggest that suspensory or terrestrial quadrupedal primates have hand structures that can exert more torque at a suspensory posture, or palmigrade and digitigrade locomotion, respectively. Furthermore, our study suggests availability of the cross and triple-ratios as one of the indicators to estimate the hand function from the skeletal structure

Can virtual reality predict body part discomfort and performance of people in realistic world for assembling tasks?

This paper presents our work on relationship of evaluation results between virtual environment (VE) and realistic environment (RE) for assembling tasks. Evaluation results consist of subjective results (BPD and RPE) and objective results (posture and physical performance). Same tasks were performed with same experimental configurations and evaluation results were measured in RE and VE respectively. Then these evaluation results were compared. Slight difference of posture between VE and RE was found but not great difference of effect on people according to conventional ergonomics posture assessment method. Correlation of BPD and performance results between VE and RE are found by linear regression method. Moreover, results of BPD, physical performance, and RPE in VE are higher than that in RE with significant difference. Furthermore, these results indicates that subjects feel more discomfort and fatigue in VE than RE because of additional effort required in VE

A Model of Movement Coordinates in Motor Cortex: Posture-Dependent Changes in the Gain and Direction of Single Cell Tuning Curves

Central to the problem of elucidating the cortical mechanisms that mediate movement behavior is an investigation of the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. In the last decade, neurophysiologists have probed how the preferred direction of an individual motor cortical cell (as determined by a center-out task) will change with posture because such changes are useful for inferring underlying cordinates. However, while the importance of shifts in preferred direction is well-known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve, i.e. gain changes, have not been similarly identified as a means of coordinate inference. This paper develops a vector field framework which, by viewing the preferred direction and the gain of a cell's tuning curve as dual components of a unitary response vector, can compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. This integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: 1) cell activity correlates with hand movement direction, 2) cell activity correlates with hand movement speed, 3) preferred directions vary with posture, and 4) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single cell level and example protocols arc simulated for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.Defense Advanced Research Projects Agency (N00014-92-J-4015); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-90-00530, IRI-97-20333); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-94-l-0940, N00014-95-1-0657)

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Synergy-based Hand Pose Sensing: Reconstruction Enhancement

Low-cost sensing gloves for reconstruction posture provide measurements which are limited under several regards. They are generated through an imperfectly known model, are subject to noise, and may be less than the number of Degrees of Freedom (DoFs) of the hand. Under these conditions, direct reconstruction of the hand posture is an ill-posed problem, and performance can be very poor. This paper examines the problem of estimating the posture of a human hand using(low-cost) sensing gloves, and how to improve their performance by exploiting the knowledge on how humans most frequently use their hands. To increase the accuracy of pose reconstruction without modifying the glove hardware - hence basically at no extra cost - we propose to collect, organize, and exploit information on the probabilistic distribution of human hand poses in common tasks. We discuss how a database of such an a priori information can be built, represented in a hierarchy of correlation patterns or postural synergies, and fused with glove data in a consistent way, so as to provide a good hand pose reconstruction in spite of insufficient and inaccurate sensing data. Simulations and experiments on a low-cost glove are reported which demonstrate the effectiveness of the proposed techniques.Comment: Submitted to International Journal of Robotics Research (2012