Search Results

Digital human modeling can nowadays be animated by means of motion capture systems and inverse kinematic algorithms. This process of animating digital models is called motion reconstruction. It consists of calculating the joint angles corresponding to the kinematic architecture of a model. This process can be seen as an optimization process, minimizing the distance between measured and reconstructed marker positions. For lower body, upper body and head segments, this process can be easily over constrained seeing as it is experimentally possible to put at least two or three markers on these bodies. On the other hand, the spine is often modeled as several body segments and markers cannot be placed on each of them for the simple reasons that subjects sit in a car and that it might be decomposed into very small body elements. Under these conditions, an infinite set of spine posture can achieve the same constraints on the pelvis and torso.

This paper focuses on full body dynamical analysis of car ingress/egress motion. It aims at proposing an experimental protocol adapted for analysing joint loads using inverse dynamics. Two preliminary studies were first performed in order to 1/ define the main driver/car interactions so as to allow measuring the contact forces at all possible contact zones and 2/ identify the design parameters that mainly influence the discomfort. In order to verify the feasibility of the protocol, a laboratory study was carried out, during which two subjects tested two car configurations. The experimental equipment was composed of a variable car mock-up, an optoelectronic motion tracking system, two 6D-force plates installed on the ground next to the doorframe and on the car floor, a 6D-Force sensor between the steering wheel and the steering column, and two pressure maps on the seat. Motions were reconstructed from measured surface markers trajectories using inverse kinematics.

Realistic simulation of human posture and movement is one of key requirements for digital human models for workplace design. In a recent European research project REAL MAN (IST 2000-29357), we have suggested a data-based motion simulation approach, which includes motion capture, model-based motion reconstruction, motion analysis and data structuration, motion simulation and discomfort estimation. After the REAL MAN project, we have decided to apply this approach and to create a complete in-vehicle reach motion database for car interior design. The objective of this paper is to show our in-vehicle reach motion database. Two female and four male subjects participated in motion data collection. Each subject carried out 64 reach movements which covered 17 common driver’s control command reaches. Motion data were analyzed in order to identify key kinematic characteristics of each motion and then structured according to subject’s anthropometric information; task and command location.

This paper investigates the feasibility of calculating joint forces and moments during a whole body truck cabin ingress/egress motion. For such a task, it is difficult to evaluate a future truck instep as the influences of the architecture parameters are complex over the motion and the discomfort feeling. In order to evaluate the future product at an early stage of the design process, Digital Human Models (DHMs) are interesting tools. However, most existing DHM simulation packages can only efficiently evaluate the kinematics of postures where the dynamics of the whole motion is necessary for such a task. The enhancement of DHMs towards a dynamic analysis and modeling is therefore necessary. In this study, the motions of subjects entering and exiting an adjustable truck cabin were measured by mean of an opto-electronic motion capture system and six load sensors. The joint angles were then calculated using an inverse kinematics method.

Improvement in the accessibility assessment of the seatbelts using a Digital Human Model requires a precise description of driver belt donning movement and of the associated discomfort. In order for automotive designers to be able to simulate seatbelt reaching movement, a general approach of motion simulation for complex and specific tasks has been proposed in this paper. It consists of three steps: constitution of a structured database, selection of an appropriate movement and its adaptation to meet new constraints. From an experiment, a database of 644 movements of automotive seatbelt reaching movements has been built-up. In order to structure the database, the temporal and spatial characteristics of the trajectories of main markers (e.g. markers attached to the hand and the torso) as well as joint movements were analysed, allowing us to identify motion control strategies.

Digital Human Models enable to evaluate very early a design architecture as they can interact with a digital environment. However, the question of which and how many manikins should be used to evaluate an architecture remains open. Most frequently, only a few manikins, representing the 5th, the 50th and the 95th percentile are used. Evaluations, based on so few manikins, give only rough ideas of how well the design fits a population of users. This paper proposes to use a sample population of manikins, randomly generated and representative in terms of anthropometric dimensions of the target population of users. The application case evaluates a truck instep and handle geometric configuration. The simulated posture is the one of the manikin reaching the handles with the hands and the first step with the left foot, the right foot remaining on the ground. For such a task, the possible collision between the left knee and the second step has to be evaluated and avoided.

Realistic motion reconstruction is the first step for ‘human like’ motion simulation by a digital human. In a recent European research project REALMAN (IST 2000-29357), a model-based motion reconstruction method from external marker trajectories was developed. It consists of two steps. The first one is to define a digital twin of a real subject using the technique of superimposing a digital human model upon at least two photos of different view, and identifying marker positions on it. In a second step, joint angles are estimated by using a kinematic model of the human body which is described using natural coordinates: coordinates of points and components of unit vectors for defining the joint locations. The model includes a detailed description of the torso, arms and legs, with simplified hands and feet. A total of 26 joints are used, connecting 27 rigid links, among them 6 located on the spine.