scholarly journals Optimization Based Dynamic Human Motion Prediction with Modular Exoskeleton Robots as Interactive Forces: The Case of Weight Lifting Motion

2021 ◽  
Author(s):  
Hyun-Joon Chung
Keyword(s):  
Dynamic Balance ◽  
Interaction Force ◽  
Human Motion ◽  
Performance Measure ◽  
Weight Lifting ◽  
Body Frame ◽  
Motion Constraints ◽  
Generalized Force ◽  
Box Lifting ◽  
Joint Limits

The optimization-based dynamics model is formulated for the weight lifting motion with human and exoskeleton model as interactive force term in this chapter. In the optimization algorithm, the human motion is defined as variables so that the motion which we want to generate (box lifting motion in this case) can be predicted. The objective function or cost function is defined as performance measure which can be switched by developer. In this paper we use the summation of each joint torque square which is considered as the dynamic effort for the motion. Constraints are defined as joint limits, torque limits, hand position, dynamic balance, exoskeleton assistive points, etc. Interaction force form exoskeleton robot can be derived as generalized coordinates and generalized force which are related to inertial reference frame and human body frame. The results can show how effective the exoskeleton robots are according to their assistive force.

Advances in Tracking and Recognition of Human Motion

2011 ◽  
pp. 65-71
Author(s):  
Niki Aifanti ◽  
Angel D. Sappa ◽  
Nikos Grammalidis ◽  
Sotiris Grammalidis Malassiotis
Keyword(s):  
Moving Objects ◽  
Research Area ◽  
Initial Position ◽  
Human Motion ◽  
Target Person ◽  
Motion Constraints ◽  
Simplifying Assumptions ◽  
Ill Posed ◽  
Important Research Area ◽  
Tracking And Recognition

Tracking and recognition of human motion has become an important research area in computer vision. In real world conditions it constitutes a complicated problem, considering cluttered backgrounds, gross illumination variations, occlusions, self-occlusions, different clothing and multiple moving objects. These ill-posed problems are usually tackled by making simplifying assumptions regarding the scene or by imposing constraints on the motion. Constraints such as that the contrast between the moving people and the background should be high and that everything in the scene should be static except for the target person are quite often introduced in order to achieve accurate segmentation. Moreover, the motion of the target person is often confined to simple movements with limited occlusions. In addition, assumptions such as known initial position and posture of the person are usually imposed in tracking processes.

Optimization-based posture prediction for human upper body

Robotica ◽  
2009 ◽  
Vol 27 (4) ◽  
pp. 607-620 ◽  
Author(s):  
Zan Mi ◽  
Jingzhou (James) Yang ◽  
Karim Abdel-Malek
Keyword(s):  
Performance Measures ◽  
Degrees Of Freedom ◽  
Human Performance ◽  
Computational Algorithm ◽  
Performance Measure ◽  
Upper Body ◽  
Cost Functions ◽  
Posture Prediction ◽  
Joint Limits ◽  
Digital Human

SUMMARYA general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.

scholarly journals Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

2021 ◽  
Author(s):  
Zhan Li ◽  
Shuai Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Redundancy Resolution ◽  
Neural Network Architecture ◽  
Kinematic Control ◽  
Physical Constraints ◽  
Motion Constraints ◽  
Joint Limits ◽  
First Time

AbstractRedundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.

Human Motion Tracking and Recognition

2005 ◽  
pp. 1355-1360
Author(s):  
Niki Aifanti ◽  
Angel D. Sappa ◽  
Nikos Grammalidis ◽  
Sotiris Malassiotis
Keyword(s):  
Motion Tracking ◽  
Moving Objects ◽  
Research Area ◽  
Initial Position ◽  
Human Motion ◽  
Target Person ◽  
Motion Constraints ◽  
Simplifying Assumptions ◽  
Ill Posed ◽  
Tracking And Recognition

Tracking and recognition of human motion has become an important research area in computer vision. In real-world conditions it constitutes a complicated problem, considering cluttered backgrounds, gross illumination variations, occlusions, self-occlusions, different clothing, and multiple moving objects. These ill-posed problems are usually tackled by simplifying assumptions regarding the scene or by imposing constraints on the motion. Constraints such as that the contrast between the moving people and the background should be high, and that everything in the scene should be static except for the target person, are quite often introduced in order to achieve accurate segmentation. Moreover, the motion of the target person is often confined to simple movements with limited occlusions. In addition, assumptions such as known initial position and posture of the person are usually imposed in tracking processes.

scholarly journals Moving object detection via TV-L1 optical flow in fall-down videos

2019 ◽  
Vol 8 (3) ◽  
pp. 839-846
Author(s):  
Nur Ayuni Mohamed ◽  
Mohd Asyraf Zulkifley
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
High Speed ◽  
Ground Truth ◽  
Human Motion ◽  
Performance Measure ◽  
Moving Object Detection ◽  
Moving Object ◽  
Human Motion Analysis ◽  
Surveillance Systems

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.

Cooperative Control of Manipulator and Human Operator for Direct Teaching

2021 ◽  
pp. 2150007
Author(s):  
Jongwoo An ◽  
Youdong Zhao ◽  
Jangmyung Lee
Keyword(s):  
Control Strategy ◽  
Cooperative Control ◽  
Least Square Method ◽  
Interaction Force ◽  
Human Motion ◽  
Least Square ◽  
Human Operator ◽  
Recursive Least Square ◽  
Direct Teaching ◽  
Teaching Operation

A cooperative control of a manipulator and a human operator has been proposed for an efficient direct teaching operation in this research. The main goal is making the operator be convenient and relaxed when he is operating the manipulator for a direct teaching. The proposed control strategy has two layers: In the first layer, human motion estimator (HME) has been designed to estimate a human intention. The recursive least square method has been utilized for the HME to simultaneously estimate the interaction force and the human arm admittance model. In the second layer, human motion reactor has been designed to keep the human motion intention precisely by a proportional derivative and gravity compensation in real time. Real experiments with a 3-degree of freedom robotic manipulator guided by the human operator have been conducted to draw a diamond shape on a panel. The experimental results demonstrate the effectiveness of the proposed cooperative control strategy.

Kinodynamically Consistent Motion Retargeting for Humanoids

2015 ◽  
Vol 12 (04) ◽  
pp. 1550017 ◽  
Author(s):  
Ghassan Bin Hammam ◽  
Patrick M. Wensing ◽  
Behzad Dariush ◽  
David E. Orin
Keyword(s):  
Degrees Of Freedom ◽  
Humanoid Robots ◽  
Human Motion ◽  
Redundancy Resolution ◽  
Motion Data ◽  
Current Formulation ◽  
Resolved Acceleration Control ◽  
Motion Retargeting ◽  
Joint Limits ◽  
Joint Motions

Human-to-humanoid motion retargeting is an important tool to generate human-like humanoid motions. This retargeting problem is often formulated as a Cartesian control problem for the humanoid from a set of task points in the captured human data. Classically, Cartesian control has been developed for redundant systems. While redundancy fundamentally adds new sub-task capabilities, the degree to which secondary objectives can be faithfully executed cannot be determined in advance. In fact, a robot that exhibits redundancy with respect to an operational task may have insufficient degrees of freedom (DOFs) to satisfy more critical constraints. In this paper, we present a Cartesian space resolved acceleration control framework to handle execution of operational tasks and constraints for redundant and nonredundant task specifications. The approach is well suited for online control of humanoid robots from captured human motion data expressed by Cartesian variables. The current formulation enforces kinematic constraints such as joint limits, self-collisions, and foot constraints and incorporates a dynamically-consistent redundancy resolution approach to minimize costly joint motions. The efficacy of the proposed algorithm is demonstrated by simulated and real-time experiments of human motion replication on a Honda humanoid robot model. The algorithm closely tracks all input motions while smoothly and automatically transitioning between regimes where different constraints are binding.

Adaptive Robust Cascade Force Control of 1-DOF Joint Exoskeleton for Human Performance Augmentation

2015 ◽  
Author(s):  
Shan Chen ◽  
Bin Yao ◽  
Zheng Chen ◽  
Xiaocong Zhu ◽  
Shiqiang Zhu
Keyword(s):  
Force Control ◽  
Motion Tracking ◽  
Human Performance ◽  
Control Method ◽  
Interaction Force ◽  
Human Motion ◽  
Adaptive Robust Control ◽  
Human Machine Interaction ◽  
High Level ◽  
Machine Interaction

The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the human-machine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control (ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncertainty which have been validated by experiment.

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