Multibody Dynamics

Forensic investigations of falls using AdamsBy Giulia Pascoletti, Paolo Conti , Filippo Cianetti, Elisabetta M. Zanetti, University of Perugia

Daniele Catelani and Hemanth Kolera, MSC Software

Falls from heights is a significant occupational hazard in the construction industry and account for 42 % of all fatalities, according to the NIOSH (National Institute for Occupational Safety and Health). However, it is challenging to discern between accidents, self-inflicted harm, and assaults because the only available metrics are injuries and the final body position. Biomechanical analyses can provide insight into the initial position and the force applied to cause the fall based on the final location of the body (Figure 1).

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Figure 1 : Forensic analyses of falls

Experimentation as the basis for analysis is, however, limited. Expensive experimental dummies are never identical to the real subject, and the number of parameters measured is not extensive. Furthermore, back calculating the starting initial conditions based on the final observed state requires several input parameters to be investigated using experimentation, which could be impractical based on the specific situation surrounding the fall. Human body models using MSC Adams can supplement experimental investigations to simulate dynamics associated with falls and address experimental limitations. Adams models can reconstruct the trajectory followed by the body during the fall, allowing users to perform multiple simulations that are completely repeatable.

The application of multibody models to reconstruct aviation, railway, and automobile accidents is widely known. On the other hand, the use of numerical biomechanical models such as Adams as an analysis technique in forensic science is still little investigated. The current study aims to highlight the advantages of a multibody modelling approach as a complementary tool in forensic investigations.

There are two classes of human body models that have been historically utilized. Based on the detail with which muscular motion is simulated, models are classified into the active and passive model categories.

Formulating an active human body model is challenging because it is impossible to know the muscle activation patterns over time. Furthermore, an active model is often ill-posed as several different muscle activation patterns could lead to the same fall trajectory. Typically a passive model is initially used to conduct the analysis. Any deviations compared to the measured data are resolved through more detailed active elements that define critical muscle activation patterns.

In the current research, a detailed analysis of passive models was carried out with a focus on articular joint passive resistance modelling and a

model’s validation results. Specifically, the final goal of the study was to establish the combination of the initial parameters associated with the starting configuration of the subject, so as to be able to simulate a final configuration after the fall that is as close as possible to reality. An associated purpose of this analysis was to distinguish between falls caused by accident or by an involuntary action such as a push.

An Adams multibody model capable of carrying out analyses of this type requires the definition of inertial and geometrical properties associated with the different anatomical parts and the definition of connection joints between these anatomical segments to simulate the skeletal articulations.

Model details

Model geometryThe Adams human body model developed in this research study is an articulated android that contains 15 elements and 14 joints between them. Each element is an ellipsoid with assigned inertial and mass properties corresponding to the fiftieth percentile in the population and associated to a certain human body part, as shown in Figure 2 and Table 1.

Figure 2 : Segment names

Segment Number

Segment Name

1 Head2 Neck3 Upper Torso4 Central Torso5 Lower Torso6 Right Upper Arm7 Right Lower Arm8 Left Upper Arm9 Left Lower Arm10 Right Upper Leg11 Right Lower Leg12 Right Foot13 Left Upper Leg14 Left Lower Leg15 Left Foot

Table 1 : Segment locations on the human model

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Table 2 : Degrees of Freedom (DOF) of each articulated joint

Figure 3 shows the geometries of two connected segments and the associated coordinates systems (an example for the upper and lower arm, i segment and j segment respectively). The proximal and the distal coordinate systems are the reference marker for the connection joint between two adjacent segments; in fact, each constraint requires the creation of two markers with the same location and orientation. Therefore, with reference to Figure 3, the i distal coordinate system and the j proximal coordinate system define the location and orientation of the connection joint between the upper arm and the lower arm, that is the elbow joint.

Connection jointsThe human joints have been modeled as simple mechanical constraints or as their combinations, to get the highest degree of model fidelity.

Figure 3 : Principal coordinate systems of

Human Joint Connection Joint DOFUpper Neck Spherical 3 RotationsLower Neck Spherical 3 Rotations

Right/Left Shoulder Spherical with Perpendicular

2 Rotations (rotation along the long axis segment is removed)

Right/Left Elbow Revolute 1 Rotation in the sagittal planeLumbar Spine Spherical 3 RotationsThoracic Spine Spherical 3 Rotations

Right/Left Hip Spherical with Perpendicular

2 Rotations (rotation along the long axis segment is removed)

Right/Left Knee Revolute 1 Rotation in the sagittal planeRight/Left Ankle Revolute 1 Rotation in the sagittal plane

abduction and adduction movements of the elbows, knees and ankles were ignored since they did not impact the dynamics of a fall.

Joint passive resistive torquesAn extensive literature survey was conducted on the stiffness properties of the human joints to define and implement the most appropriate laws for describing the behavior of each constraint. The proper definition of these properties is critical to limit the freedom of movement via the mechanical constraints (avoiding the achievement of unnatural positions) and to ensure the stability of the model when the force of gravity acts on segments. Based on this literature survey, non-linear moment-rotation relationships were selected for most of the constraints. For the rest, a rotational stiffness value was selected.

The model was validated by comparison against an experimental study. Within this study, five different falling scenarios (Figure 4) were analyzed, and for each of them, the value of the impact force acting on the head was computed. These configurations were reproduced using the Adams multibody dynamics model. Initial motions have been applied to the human model for a few instants, in order to reproduce a specific scenario and then gravity takes over.

Figure 4 : Modelled scenarios

The degrees of freedom (DOF) for each joint (Table 2) Degrees of Freedom (DOF) of each articulated joint were judiciously chosen to aid the simulation intent, which was to model the fall of a human being from a certain height. Degrees of freedom that were not consequential to that intent were ignored. For example, the axial rotation along the long bone axes, as well as

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The model proved capable to replicate the kinematic behavior of the android with a maximum deviation of 11 % for the impact force values on the head.

Design of experiments analysisWith a validated model available, the next step was to identify a subset of variables (Table 3) with the most significant impact on the final body position.

Results

The core focus of this research work was to study the dynamics associated with an accidental fall. Experimental investigations using a mannequin were not an option given the number of factors involved. This research work has provided insight into the potential usage of multibody models for studying the dynamics of a fall. Using an inverse kinematic approach, the model based on the final actual position establishes under what condition the fall occurred.

Based on the modeling of the various body segments, the constraints between them and the elastic/viscous characteristics of the joints, it is possible to calculate the conditions under which the fall occurred with a high degree of repeatability and parameters, such as an external thrust signifying a push, can be isolated. Primarily, the choice of the centers of mass of the segment’s head, right and left upper leg ensures a good match (Figure 6) with the final configuration in terms of the orientation of the body in the x-y plane, orientation of the body along the x axis and prone position.

The original publication can be found here (https://www.frontiersin.org/articles/10.3389/fbioe.2019.00419/full)

Table 3 Investigated variables with the most significant impact on the final body position

Figure 5 Simulation process flow

A series of DOE analyses were carried out iteratively, with the aim of optimizing the number of initial parameters and the range of values to be investigated as shown in Figure 5.

Figure 6 Comparison between actual and simulated configurationBased on these identified variables, a Design of Experiments (DOE) was created. These

parameters were then optimized to minimize the objective function, which is the deviation between the actual final position (A) of the victim and the simulated prediction (M).