Complex Human Models Research Articles

Varieties of human models and subsystems of society

This article is devoted to the study of approaches to the study of human models in society and the algorithm for their evaluation. The classical composition of the subsystems of society and the modern view of the expansion of its subsystems/spheres are presented. The expanded composition of subsystems/spheres of society allowed us to consider the question of the variety of human models in a new way. The analysis of the complex human model on a new projection of the subsystems of society has revealed the prospect of its improvement.

Image Processing-Based Center Calculation Method for General and Interval Type-2 Fuzzy Systems

Despite being a good method against uncertainties, the high complexity of the operations in type-2 fuzzy system makes it slow. So, it should be reduced to use type-2, especially in real-time problems. In this study, a method based on image digitization has been proposed for type-reduction and defuzzification steps, which are forming the actual computational complexity in type-2 fuzzy systems. Instead of solutions using iterative and complex methods, the problem is converted into image processing problem by digitizing the fired outputs. The deduction methods such as the center of gravity are computed easily on digitized images. Example scenarios show that the work done is superior to KM algorithm in terms of both precision and computational complexity. In addition, different simulations were conducted to demonstrate the superiority of the study over real problems, and the study was tested for a complex human model motion control problem.

Identification of occupant posture using a Bayesian classification methodology to reduce the risk of injury in a collision

Recent studies have shown that smart restraint systems, which will recognize and then adapt to a specific collision and occupant combination, have a strong opportunity to significantly reduce occupant injuries during a traffic accident. As a step toward the development of these adaptive restraint systems, this study proposes a novel methodology for the classification of pre-crash occupant posture. Various occupant postures were simulated with a human model and the corresponding data was recorded using sensor models implemented in a mid-size car interior. The sensor data was then used to train two Bayesian classifiers which categorized an unknown occupant posture as one of nine predefined classes. The posture classifiers and a look-up table which contained optimized restraint laws for each class were combined to form catalog controllers for the restraint systems. The benefit of these restraint systems with catalog controllers vs. a restraint system optimized at a nominal posture was estimated by analyzing crash simulations with the occupant in 200 different postures. While the minimum error rate classifier showed the highest correct classification rate (90%), the Bayesian minimum risk classifier estimated the highest average injury reduction (21%). As expected, the highest injury reduction (up to 45%) was recorded for the posture classes closest to the windshield, whereas the lowest injury reduction was found for the classes closest to the nominal position. While the proposed restraint system with a catalog controller requires considerable “offline” computational effort, it is more versatile in terms of using complex human models and injury criteria and is much faster during the brief decision window available than recent “online” controllers proposed previously in literature.

356 高速動力学計算法を用いた詳細人体モデルに基づく人間の力学計算

Efficient algorithms for dynamics computation of link-based human figures have been proposed in robotics. We applied it to far more complex human model we designed. This human model involving bones, muscles, tendons and ligament. This paper describes the methods for designing of the detailed human model and forward/inverse dynamics computation of it.