Human Gait Prediction with a High DOF Upper Body: A Multi-Objective Optimization of Discomfort and Energy Cost

2017 ◽  
Vol 14 (01) ◽  
pp. 1650025
Author(s):  
Hyun-Jung Kwon ◽  
Hyun-Joon Chung ◽  
Yujiang Xiang
Keyword(s):  
Human Motion ◽  
Performance Measure ◽  
Joint Torque ◽  
Upper Body ◽  
Whole Body ◽  
Multi Objective Optimization ◽  
Backward Walking ◽  
Multi Objective ◽  
Body Model ◽  
Neutral Angle

To predict the 3D walking pattern of a human, a detailed upper body model that includes the spine, shoulders, and neck must be made, which is challenging because of the coupling relations of degrees of freedom (DOF) in these body sections. The objective of this study was to develop a discomfort function for including a high DOF upper body model during walking. A multi-objective optimization (MOO) method was formulated by minimizing dynamic effort (DE) and the discomfort function simultaneously. The discomfort function is defined as the sum of the squares of deviation of joint angles from their neutral angle positions. The neutral angle position is defined as a relaxed human posture without actively applied external forces. The DE is the sum of the joint torque squared. To illustrate the capability of including a high DOF upper body, backward walking is used as an example. By minimizing both DE and the discomfort function, a 3D whole-body model with a high DOF upper body for walking was simulated successfully. The proposed MOO is a promising human performance measure to predict human motion using a high DOF upper body with full range of motion. This has been demonstrated by simulating backward walking, lifting, and ingress motions.

Multi-Objective Optimization of Human Gait With a Discomfort Function

2016 ◽  
Author(s):  
Hyun-Jung Kwon ◽  
Hyun-Joon Chung ◽  
Yujiang Xiang
Keyword(s):  
Joint Torque ◽  
Upper Body ◽  
Whole Body ◽  
Human Gait ◽  
Multi Objective Optimization ◽  
Joint Angles ◽  
Backward Walking ◽  
Multi Objective ◽  
Body Model ◽  
Neutral Angle

The objective of this study was to develop a discomfort function for including a high DOF upper body model during walking. A multi-objective optimization (MOO) method was formulated by minimizing dynamic effort and the discomfort function simultaneously. The discomfort function is defined as the sum of the squares of deviation of joint angles from their neutral angle positions. The dynamic effort is the sum of the joint torque squared. To investigate the efficacy of the proposed MOO method, backward walking simulation was conducted. By minimizing both dynamic effort and the discomfort function, a 3D whole body model with a high DOF upper body for walking was demonstrated successfully.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

Robotica ◽  
2010 ◽  
Vol 29 (2) ◽  
pp. 245-253 ◽  
Author(s):  
Jingzhou (James) Yang ◽  
Tim Marler ◽  
Salam Rahmatalla
Keyword(s):  
Performance Measures ◽  
Human Performance ◽  
Design Tool ◽  
Upper Body ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Validation Criteria ◽  
Posture Prediction ◽  
Development And Validation ◽  
Digital Human

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Whole-Body Human-to-Humanoid Motion Imitation

2013 ◽  
Vol 479-480 ◽  
pp. 617-621
Author(s):  
Hsien I Lin ◽  
Zan Sheng Chen
Keyword(s):  
Degrees Of Freedom ◽  
Humanoid Robot ◽  
Optimal Solution ◽  
Human Motion ◽  
Body Motion ◽  
Upper Body ◽  
Whole Body ◽  
Nao Robot ◽  
Novel Approach ◽  
Intuitive Method

Human-to-Humanoid motion imitation is an intuitive method to teach a humanoid robot how to act by human demonstration. For example, teaching a robot how to stand is simply showing the robot how a human stands. Much of previous work in motion imitation focuses on either upper-body or lower-body motion imitation. In this paper, we propose a novel approach to imitate human whole-body motion by a humanoid robot. The main problem of the proposed work is how to control robot balance and keep the robot motion as similar as taught human motion simultaneously. Thus, we propose a balance criterion to assess how well the root can balance and use the criterion and a genetic algorithm to search a sub-optimal solution, making the root balanced and its motion similar to human motion. We have validated the proposed work on an Aldebaran Robotics NAO robot with 25 degrees of freedom. The experimental results show that the root can imitate human postures and autonomously keep itself balanced.

Effect of Foot Location on Human Running Prediction

2014 ◽  
Author(s):  
Hyun-Joon Chung ◽  
Rajan Bhatt ◽  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Karim Abdel-Malek
Keyword(s):  
Degrees Of Freedom ◽  
Reaction Force ◽  
Performance Measure ◽  
Joint Torque ◽  
Upper Body ◽  
Human Model ◽  
Digital Human Model ◽  
Digital Human ◽  
Dynamics Method ◽  
Human Running

Human running is simulated in this work by using a skeletal digital human model with 55 degrees of freedom (DOFs). A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effect of foot location on the running motion prediction are simulated and studied.

A Body Model Server for Human Motion Capture and Representation

1996 ◽  
Vol 5 (4) ◽  
pp. 381-392 ◽  
Author(s):  
Joshua Bers
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Geometric Model ◽  
Human Motion ◽  
The Body ◽  
Geometric Constraints ◽  
Upper Body ◽  
Accurate Information ◽  
Joint Angles ◽  
Body Model

This paper presents a body model server (BMS) that provides real-time access to the position and posture of a person's torso, arms, hands, head, and eyes. It can be accessed by clients over a network. The BMS is designed to function as a device-independent data-layer between the sensing devices and client applications that require real-time human motion data, such as animation control. It can provide clients with accurate information at up to 40 Hz. For data collection, the model uses four magnetic position/ orientation sensors, two data-gloves, and an eye-tracker. The BMS combines the data-streams from the sensors and transforms them into snapshots of the user's upper-body pose. A geometric model made up of joints and segments structures the input. Posture of the body is represented by joint angles. Two unique characteristics of our approach are the use of the implicit, geometric constraints of the sensed body to simplify the computation of the unmeasured joint angles, and the use of time-stamped data that allow synchronization with other data streams, e.g., speech input. This paper describes the architecture of the BMS, including the management of multiple input devices, the representation and computation of the position and joint angle data, and the client-server interface.

The Implement of Optimization Design of a Car Body Based on VCD-ICAE System

2012 ◽  
Vol 479-481 ◽  
pp. 2577-2581
Author(s):  
Wen Bin Hou ◽  
Zhen Jun Bi ◽  
Hong Zhe Zhang ◽  
Ping Hu
Keyword(s):  
The Body ◽  
Vehicle Body ◽  
Whole Body ◽  
Design Parameters ◽  
Concept Design ◽  
Multi Objective Optimization ◽  
Detailed Model ◽  
Car Body ◽  
Multi Objective ◽  
Body Concept

In order to get the optimistic structure property and design parameters of a car body, the system of vehicle body concept design (VCD-ICAE) was developed by us to make the body design in the conceptual phase in the paper. It can build parametric geometry modeling and FEM model of body-in-white (BIW) automatically, and the structural stiffness was calculated. Based on the former model, a multi-objective optimization of the total body was designed to afford the reasonable parameters for detailed model of BIW, which realized lightweight of the whole body and high stiffness. In the paper, the total theory and flowchart of vehicle body concept design were afforded. An example with real body’s data was shown to prove the validity of the multi-objective optimization module in VCD-ICAE system. Finally, the optimal design scheme of the body was provided.

scholarly journals Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot

2021 ◽  
Vol 11 (7) ◽  
pp. 3114
Author(s):  
Hyunjin Choi
Keyword(s):  
Rehabilitation Medicine ◽  
Human Motion ◽  
Joint Torque ◽  
Whole Body ◽  
Gait Rehabilitation ◽  
Walking Ability ◽  
Muscular Weakness ◽  
Rehabilitation Robots ◽  
Actuation System ◽  
Powered Exoskeleton

Robotic systems for gait rehabilitation have been actively developed in recent years; many of the rehabilitation robots have been commercialized and utilized for treatment of real patients in hospitals. The first generation of gait rehabilitation robots was a tethered exoskeleton system on a treadmill. While these robots have become a new trend in rehabilitation medicine, there are several arguments about the effectiveness of such robots due to the passiveness of the motions that the robots generate, i.e., the continuous passive motions may limit the active involvement of patients’ voluntary motion control. In order to let a patient be more actively involved by requiring the self-control of whole-body balance, untethered powered exoskeletons, wearable robots that patients can wear and walk on the ground, are receiving great attention. While several powered exoskeletons have been commercialized already, the question about their effectiveness has not been cleared in the viewpoint of rehabilitation medicine because most of the powered exoskeletons provide still continuous passive motions, even though they are on the ground without tethering. This is due to their control strategy; the joints of a powered exoskeleton are position-controlled to repeatedly follow a predefined angle trajectory. This may be effective when a wearer is completely paraplegic such that the powered exoskeleton must generate full actuation power for walking. For people with muscular weakness due to various reasons, the powered exoskeleton must assist only the lack of muscular force without constraining human motion. For assistance and rehabilitation of people with partial impairment in walking ability, Angel Legs is introduced in this paper. The proposed powered exoskeleton system is equipped with a transparent actuation system such that the assistive force is accurately generated. The overall design and control of Angel Legs are introduced in this paper, and a clinical verification with a human subject is also provided.

Optimization-based dynamic 3D human running prediction: effects of foot location and orientation

Robotica ◽  
2014 ◽  
Vol 33 (2) ◽  
pp. 413-435 ◽  
Author(s):  
Hyun-Joon Chung ◽  
Yujiang Xiang ◽  
Jasbir S. Arora ◽  
Karim Abdel-Malek
Keyword(s):  
Human Subjects ◽  
Reaction Force ◽  
Three Dimensional ◽  
Performance Measure ◽  
Joint Torque ◽  
Upper Body ◽  
Human Model ◽  
Curved Paths ◽  
Dynamics Method ◽  
Human Running

SUMMARYThis paper presents optimization-based dynamic three-dimensional (3D) human running prediction. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. In addition, a slow jog along curved paths has been formulated. It is a non-symmetric running motion, so a stride formulation has been used. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effects of foot location and orientation on the running motion prediction are simulated and studied. Simulation results from this methodology show good correlation with experimental data obtained from human subjects.

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

2021 ◽  
pp. 2159032
Author(s):  
Ning Yang ◽  
Hai-Lin Liu
Keyword(s):  
Optimization Problems ◽  
Performance Measure ◽  
Experimental Results ◽  
The Other ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Selection Operators ◽  
Handling Technique ◽  
Feasible Solutions

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

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