Joo H. Kim

A new digital human environment and assessment of vehicle interior design

Vehicle interior design directly relates to driver performance measures such as comfort, efficiency, risk of injury, and vehicle safety. A digital human is a convenient tool for satisfying the need to reduce the design cycle in order to save time and money. This paper presents a digital human environment, Santos(TM), developed at The University of Iowa, and its assessment as applied to the interior design of a Caterpillar vehicle. The digital human environment involves male models and accommodates a large percentage of the operator population (from the 5th percentile to the 95th percentile). It has a user-friendly interface and includes various tools such as posture prediction, reachability check, zone differentiation, and biomechanics assessment for the upper body and hand. The key difference from a traditional digital human environment is that Santoss environment is optimization-based. This can answer design questions regarding whether the operator can reach relevant controls, what the comfort level is if one can reach the control, and what strength is required of the operator to pull a shift, etc. The illustrative example of a Caterpillar cab is demonstrated using this digital human environment.

Prediction and analysis of human motion dynamics performing various tasks

Several digital human softwares have shown the capabilities of simulating simple reach motions. However, predicting the dynamic effects on human motion due to different task loads is still immature. This paper presents an optimisation-based algorithm for simulating the dynamic motion of a digital human. The hypothesis is that human performance measures such as the total energy consumption governs human motion; thus the process of human motion simulation can be formulated as an optimisation problem that minimises human performance measures given at different constraints and hand loads, corresponding to a number of tasks. General equations of motion using Lagrangian dynamics method are derived for the digital human, and human metabolic energy is formulated in terms of joint space. Joint actuator torques and metabolic energy expenditure during motion are formulated and calculated within the algorithm, and it is applied to Santos™, a kinematically realistic digital human, developed at the University of Iowa. Results show that different external loads and tasks lead to different human motions and actuator torque distributions.

Real-time optimal reach-posture prediction in a new interactive virtual environment

Human posture prediction is a key factor for the design and evaluation of workspaces, in a virtual environment using virtual humans. This work presents a new interface and virtual environment for the direct human optimized posture prediction (D-HOPP) approach to predicting realistic reach postures of digital humans, where reach postures entail the use of the torso, arms, and neck. D-HOPP is based on the contention where depending on what type of task is being completed, and human posture is governed by different human performance measures. A human performance measure is a physics-based metric, such as energy or discomfort, and serves as an objective function in an optimization formulation. The problem is formulated as a single-objective optimization (SOO) problem with a single performance measure and as multi-objective-optimization (MOO) problem with multiple combined performance measures. We use joint displacement, change in potential energy, and musculoskeletal discomfort as performance measures. D-HOPP is equipped with an extensive yet intuitive user-interface, and the results are presented in an interactive virtual environment.

Posture Prediction and Force/Torque Analysis for Human Hands

Human hands are the bridge between humans and the objects to be manipulated or grasped both in the real and virtual world. Hands are used to grasp or manipulate objects and one of the most important functionalities is to position the fingers, i.e., given the position of the fingertip and to determine the joint angles. Last year we presented a 25-degree of freedom (DOF) hand model that has palm arch functionality. In this paper we preset an optimization-based inverse kinematics approach to position this 25 DOF hand locally with respect to the wrist instead of the traditional Moore-Penrose pseudo-inverse and experiment methods. The hypothesis is that human performance measures govern the configuration and motion of the hand. We also propose contact force and joint torque prediction. The exposition addresses (1) the determination whether a point is reachable (i.e., is it within the reach envelope), (2) the prediction of a finger posture for a given target point, (3) given the finger contact force analyzing the joint torque, and (4) given joint torque analyzing finger contact force. We illustrate the methodology through examples.

Quantifying Dynamic Characteristics of Human Walking for Comprehensive Gait Cycle

Normal human walking typically consists of phases during which the body is statically unbalanced while maintaining dynamic stability. Quantifying the dynamic characteristics of human walking can provide better understanding of gait principles. We introduce a novel quantitative index, the dynamic gait measure (DGM), for comprehensive gait cycle. The DGM quantifies the effects of inertia and the static balance instability in terms of zero-moment point and ground projection of center of mass and incorporates the time-varying foot support region (FSR) and the threshold between static and dynamic walking. Also, a framework of determining the DGM from experimental data is introduced, in which the gait cycle segmentation is further refined. A multisegmental foot model is integrated into a biped system to reconstruct the walking motion from experiments, which demonstrates the time-varying FSR for different subphases. The proof-of-concept results of the DGM from a gait experiment are demonstrated. The DGM results are analyzed along with other established features and indices of normal human walking. The DGM provides a measure of static balance instability of biped walking during each (sub)phase as well as the entire gait cycle. The DGM of normal human walking has the potential to provide some scientific insights in understanding biped walking principles, which can also be useful for their engineering and clinical applications.

Development of the virtual-human Santos®

This paper presents the background and history of the virtual human Santos™ developed by the Virtual Soldier Research (VSR) Program at The University of Iowa. The early virtual human environment was called Mira™. This 15-degree-of-freedom (DOF) upper-body model with posture and motion prediction was funded by John Deere Inc. and US Army TACOM Automotive Research Center. In 2003 US Army TACOM began funding VSR to develop a new generation of virtual humans called Santos (109 DOFs), which was to be another generation of Mira. Later on, Caterpillar Inc., Honda R&D North Americas, Natick Soldier System Center, and USCAR (GM, Ford, and Chrysler) joined the VSR partnership. The objective is to develop a new generation of digital humans comprising realistic human models including anatomy, biomechanics, physiology, and intelligence in real time, and to test digital mockups of products and systems before they are built, thus reducing the significant costs and time associated with making prototypes. The philosophy is based on a novel optimization-based approach for empowering these digital humans to perform, un-aided, in a physics-based world. The research thrusts include the following areas: (1) predictive dynamics, (2) modeling of cloth, (3) hand model, (4) intuitive interface, (5) motion capture, (6) muscle and physiology modeling, (7) posture and motion prediction, (8) spine modeling, and (9) real-time simulation and virtual reality (VR). Currently, the capabilities of Santos include whole-body posture prediction, advanced inverse kinematics, reach envelope analysis, workspace zone differentiation, muscle force and stress analysis, muscle fatigue prediction, simulation of walking and running, dynamic motion prediction, physiologic assessment, a user-friendly interface, a hand model and grasping capability, clothing modeling, thermo discomfort assessment, muscle wrapping and sliding, whole-body vibration analysis, and collision avoidance.

Passive and dynamic gait measures for biped mechanism: formulation and simulation analysis

Understanding and mimicking human gait is essential for design and control of biped walking robots. The unique characteristics of normal human gait are described as passive dynamic walking, whereas general human gait is neither completely passive nor always dynamic. To study various walking motions, it is important to quantify the different levels of passivity and dynamicity, which have not been addressed in the current literature. In this paper, we introduce the initial formulations of Passive Gait Measure (PGM) and Dynamic Gait Measure (DGM) that quantify passivity and dynamicity, respectively, of a given biped walking motion, and the proposed formulations will be demonstrated for proof-of-concepts using gait simulation and analysis. The PGM is associated with the optimality of natural human walking, where the passivity weight functions are proposed and incorporated in the minimization of physiologically inspired weighted actuator torques. The PGM then measures the relative contribution of the stance ankle actuation. The DGM is associated with the gait stability, and quantifies the effects of inertia in terms of the Zero-Moment Point and the ground projection of center of mass. In addition, the DGM takes into account the stance foot dimension and the relative threshold between static and dynamic walking. As examples, both human-like and robotic walking motions during single support phase are generated for a planar biped system using the passivity weights and proper gait parameters. The calculated PGM values show more passive nature of human-like walking as compared with the robotic walking. The DGM results verify the dynamic nature of normal human walking with anthropomorphic foot dimension. In general, the DGMs for human-like walking are greater than those for robotic walking. The resulting DGMs also demonstrate their dependence on the stance foot dimension as well as the walking motion; for a given walking motion, smaller foot dimension results in increased dynamicity. Future work on experimental validation and demonstration will involve actual walking robots and human subjects. The proposed results will benefit the human gait studies and the development of walking robots.

Vision Performance Measures for Optimization-Based Posture Prediction

Although much work has been completed with modeling head-neck movements as well with studying the intricacies of vision and eye movements, relatively little research has been conducted involving how vision affects human upper-body posture. By leveraging direct human optimized posture prediction (D-HOPP), we are able to predict postures that incorporate one’s tendency to actually look towards a workspace or see a target. DHOPP is an optimization-based approach that functions in real time with Santos, a new kind of virtual human with a high number of degrees-of-freedom and a highly realistic appearance. With this approach, human performance measures provide objective functions in an optimization problem that is solved just once for a given posture or task. We have developed two new performance measures: visual acuity and visual displacement. Although the visual-acuity performance measure is based on well-accepted published concepts, we find that it has little effect on the predicted posture when a target point is outside one’s field of view. Consequently, we have developed visual displacement, which corrects this problem. In general, we find that vision alone does not govern posture. However, using multi-objective optimization, we combine visual acuity and visual displacement with other performance measures, to yield realistic and validated predicted human postures that incorporate vision.

Planning load-effective dynamic motions of highly articulated human model for generic tasks

The robotic motion planning criteria has evolved from kinematics to dynamics in recent years. Many research achievements have been made in dynamic motion planning, but the externally applied loads are usually limited to the gravity force. Due to the increasing demand for generic tasks, the motion should be generated for various functions such as pulling, pushing, twisting, and bending. In this paper, a comprehensive form of equations of motion, which includes the general external loads applied at any point of branched tree structures, is implemented. An optimization-based algorithm is then developed to generate load-effective motions of redundant tree-structured systems for generic tasks. A highly articulated dual-arm human model is used to generate different effective motions to sustain different external load magnitudes. The results also provide a new scientific insight of human motion.

The potential of accelerometers in the evaluation of stability of total knee arthroplasty.

An accelerometer attached to the anterior proximal tibia was investigated as an evaluation of knee stability of Total Knee Arthroplasty (TKA) patients while performing daily activities. Acceleration data of 38 TKA knees with a minimum follow up of 6months were compared with 34 control knees. The activities performed were: walking three steps forward and coming to a sudden stop; turning in the direction of non-tested knee; sit-to-stand; and stepping up and down from a 7 inch step. The acceleration results showed significant differences between TKA and controls while stepping down and while turning in the non-tested knee direction. The higher accelerations with the TKA group may have represented an objective measure of stability, even if this was not directly discernible to the patient.