Nonlinear inverse optimization approach for determining the weights of objective function in standing reach tasks

This paper presents a nonlinear inverse optimization approach to determine the weights for the joint displacement function in standing reach tasks. This inverse optimization problem can be formulated as a bi-level highly nonlinear optimization problem. The design variables are the weights of a cost function. The cost function is the weighted summation of the differences between two sets of joint angles (predicted posture and the actual standing reach posture). Constraints include the normalized weights within limits and an inner optimization problem to solve for joint angles (predicted standing reach posture). The weight linear equality constraints, obtained through observations, are also implemented in the formulation to test the method. A 52 degree-of-freedom (DOF) human whole body model is used to study the formulation and visualize the prediction. An in-house motion capture system is used to obtain the actual standing reach posture. A total of 12 subjects (three subjects for each percentile in stature of 5th percentile female, 50th percentile female, 50th percentile male and 95th percentile male) are selected to run the experiment for 30 tasks. Among these subjects one is Turkish, two are Chinese, and the rest subjects are Americans. Three sets of weights for the general standing reach tasks are obtained for the three zones by averaging all weights in each zone for all subjects and all tasks. Based on the obtained sets of weights, the predicted standing reach postures found using the direct optimization-based approach have good correlation with the experimental results. Sensitivity of the formulation has also been investigated in this study. The presented formulation can be used to determine the weights of cost function within any multi-objective optimization (MOO) problems such as any types of posture prediction and motion prediction.     

Effects of BMI and task parameters on joint angles during simulated small parts assembly

Functional limitations of persons classified into different obesity categories were evaluated while performing a simulated small parts assembly tasks. Joint angles (measured using electro-goniometers) and maximum forward function reach (MFFR) were used to quantify function limitations, and tasks were completed on three workstations designed for the 5th, 50th, and 95th percentile workers based on anthropometric data tables. Results revealed that BMI category did not significantly affect measured joint angles. Results also indicated that joint angles when working at the 95th percentile workstation configuration were significantly larger than those measured at the 5th percentile workstation configuration. Further, obese class 2 and obese class 3 groups MFFRs were significantly shorter than the normal weight group, which was expected. These results imply that workstation design considerations should include obesity levels, and that work should be placed near the worker and should be elevated to reduce pressure on joint angles while working for larger (obese) individuals.     

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 （SO0） problem with a single performance measure and as multiobjective-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.     

The effects of joint angle variability and different driving load scenarios on maximum muscle activity – A driving posture simulation study

To date, no studies have been conducted on the main and interaction effects of joint angles on maximum muscle activity in different driving load scenarios. To investigate the influence of joint angle variability on the muscular system, this study calculated maximum muscle activity during three static driving load scenarios through the use of musculoskeletal inverse dynamic simulation. Six joint angles in sagittal plane were varied with reference to reported driving posture angles in the literature. A digital manikin with a height of 180 cm and weight of 70 kg was used with simple muscles and a minimum fatigue criterion for muscle activation optimization. Three static driving load scenarios were simulated: sitting with no external forces except gravity, steering, and pedaling operation. Prediction models were developed for each driving load scenario using Least Squares Support Vector Machine. Finally, the Pareto optimization method was applied for multi-objective optimization combining the three developed models.The results indicate that the developed models can be used for the prediction of simulated maximum muscle activity. The six joint angles explain a higher percentage of maximum muscle activity variance in the steering and pedaling operation scenarios compared to the sitting scenario. The six joint angles differ in their main and interaction effects on maximum muscle activity depending on the driving load scenario. The optimum joint angle values of the driving posture depend on the driving load scenarios. The different driving postures based on minimum maximum muscle activity are presented for the three driving load scenarios.Relevance to industryThe results of this study can be utilized in establishing driving posture simulation models to improve vehicle interiors during the early development stage. Furthermore, the results of this study can provide base data for the development of a tool for real driving posture evaluation of maximum muscle activity.     

Guidelines for wrist posture based on carpal tunnel pressure thresholds

OBJECTIVE: To develop work guidelines for wrist posture based on carpal tunnel pressure. Background: Wrist posture is considered a risk factor for distal upper extremity musculoskeletal disorders, and sustained wrist deviation from neutral at work may be associated with carpal tunnel syndrome. However, the physiologic basis for wrist posture guidelines at work is limited. METHODS: The relationship of wrist posture to carpal tunnel pressure was examined in 37 healthy participants. The participants slowly moved their wrists in extension-flexion and radioulnar deviation while wrist posture and carpal tunnel pressure were recorded. The wrist postures associated with pressures of 25 and 30 mmHg were identified for each motion and used to determine the 25th percentile wrist angles (the angles that protect 75% of the study population from reaching a pressure of 25 or 30 mmHg). RESULTS: Using 30 mmHg, the 25th percentile angles were 32.7 degrees (95% confidence interval [CI] = 27.2-38.1 degrees) for wrist extension, 48.6 degrees (37.7 -59.4 degrees) for flexion, 21.8 degrees (14.7-29.0 degrees) for radial deviation, and 14.5 degrees (9.6-19.4 degrees) for ulnar deviation. For 25 mmHg, the 25th percentile angles were 26.6 degrees and 37.7 degrees for extension and flexion, with radial and ulnar deviation being 17.8 degrees and 12.1 degrees, respectively. CONCLUSION: Further research can incorporate the independent contributions of pinch force and finger posture into this model. APPLICATION: The method presented can provide wrist posture guidelines for the design of tools and hand-intensive tasks.     

Prediction accuracy in estimating joint angle trajectories using a video posture coding method for sagittal lifting tasks

This study investigated prediction accuracy of a video posture coding method for lifting joint trajectory estimation. From three filming angles, the coder selected four key snapshots, identified joint angles and then a prediction program estimated the joint trajectories over the course of a lift. Results revealed a limited range of differences of joint angles (elbow, shoulder, hip, knee, ankle) between the manual coding method and the electromagnetic motion tracking system approach. Lifting range significantly affected estimate accuracy for all joints and camcorder filming angle had a significant effect on all joints but the hip. Joint trajectory predictions were more accurate for knuckle-to-shoulder lifts than for floor-to-shoulder or floor-to-knuckle lifts with average root mean square errors (RMSE) of 8.65°, 11.15° and 11.93°, respectively. Accuracy was also greater for the filming angles orthogonal to the participant's sagittal plane (RMSE = 9.97°) as compared to filming angles of 45° (RMSE = 11.01°) or 135° (10.71°). The effects of lifting speed and loading conditions were minimal. To further increase prediction accuracy, improved prediction algorithms and/or better posture matching methods should be investigated.

Statement of Relevance: Observation and classification of postures are common steps in risk assessment of manual materials handling tasks. The ability to accurately predict lifting patterns through video coding can provide ergonomists with greater resolution in characterising or assessing the lifting tasks than evaluation based solely on sampling with a single lifting posture event.     

An interactive method for the selection of design criteria and the formulation of optimization problems in computer aided optimal design

This paper presents an interactive method for the selection of design criteria and the formulation of optimization problems within a computer aided optimization process of engineering systems. The key component of the proposed method is the formulation of an inverse optimization problem for the purpose of determining the design preferences of the engineer. These preferences are identified based on an interactive modification of a preliminary optimization result that is the solution of an initial problem statement. A formulation of the inverse optimization problem is presented, which is based on a weighted-sum multi-objective approach and leads to an explicit optimization problem that is computationally inexpensive to solve. Numerical studies on structural shape optimization problems show that the proposed method is able to identify the optimization criteria and the formulation of the optimization problem which drive the interactive user modifications.     

Two‐handed human reach prediction models for ergonomic evaluation

As an essential function of computerized ergonomic evaluation models based on digital human models, realistic simulation or prediction of human reach profiles is of great importance. Although several human‐modeling efforts have been made to provide the capability of reach simulation, most studies have been limited to the reach of a single extremity. A variety of activities of human operators, however, frequently involve simultaneous positioning of two or more extremities to different target positions. Such a multiple reach problem cannot be satisfactorily resolved by means of conventional single‐extremity reach models because formulation of the problem as a series of single reaches rarely yields accurate trajectory of human‐reach profiles due to interactions of multiple extremities. In this research, a two‐handed reach prediction model was developed. The human upper body was modeled as a seven‐link system with 13 degrees of freedom, being regarded as a redundant open kinematic chain with two end‐effectors. As a way of solving the two‐handed reach problem, the resolved motion method was adopted among several inverse kinematics methods as the technique is fit for real‐time redundancy control. The method is also capable of incorporating the joint range availability criterion as a cost function to minimize excessive deviations of body joints from their neutral positions. Real human‐reach profiles were compared to those obtained from the prediction model and were found to be statistically similar. The methodology is expected to be applicable to the reach simulation of both upper and lower extremities without algorithmic difficulties. © 2010 Wiley Periodicals, Inc.     

A finger motion model for reach and grasp

This study aimed to develop a model that describes human finger motion for simulation of reach and grasp for selected objects and tasks. Finger joint angles and timing of their changes were measured for six subjects as they reached 20-40 cm and grasped cylindrical handles (1.3-10.2 cm D) of varying orientation (vertical/axial). The empirical results from multiple regression analyses served as inputs to allow a fourth order polynomial to predict motion of each finger joint. The proposed model showed good fit with observations, with high coefficients of determination from 0.54 to 1 and reasonable errors from 0.04° to 5.44° for all conditions considered. The proposed finger motion model was implemented in an existing kinematic hand model to employ a contact algorithm for refined prediction of grip posture and to illustrate its predictive power by graphically displaying the opening and closing of the hand.

Relevance to industry

Finger joint motions during reach and grasp are needed for prediction of (1) tendon excursions for study of work-related musculoskeletal disorders, (2) required space for the hand, (3) finger locations on work objects, and (4) hand grip postures and strength.     

Simulated effect of driver and vehicle interaction on vehicle interior layout

Digital human modeling is an essential tool to reduce cost and to save time in a design process where humans take the part of users of the design. Considering this phenomenon for a vehicle interior, the importance of the seat track location and adjustment ranges become important. This paper presents the effect of driver and vehicle interaction on vehicle interior layout based on simulation approach. This simulation method includes two optimizations. The first optimization problem is the physics-based seated posture prediction. In order to represent physical drivers, 4,500 virtual drivers are generated based on an anthropometric database-ANSUR. Interaction forces between the digital human and pedal, seat, ground, and steering wheel are incorporated in the physics-based posture prediction. Three different pedal reaction moments (0, 20, and 40 N m) are implemented into the formulation to examine the effect of pedal reaction moment on driver seat location and adjustment ranges. To study the effect of shear forces, the physics-based posture prediction is compared to kinematics-based posture prediction. After posture predictions are completed, individuals' preferred seat locations are used in a second optimization problem to predict the seat track location and adjustment ranges. For a specific vehicle with 20 N m pedal reaction moment, adjustment ranges are predicted as 223 mm and 82 mm in horizontal and vertical directions, respectively. Also, it was shown that shear force due to the interaction between the driver and the seat pan and the pedal reaction moment are both influential to the seat track location and adjustment ranges.Relevance to industryThe simulation model presented in this paper is useful in vehicle and seat design and can be easily used for virtual design assessment in vehicle design.     

Low back joint loading and kinematics during standing and unsupported sitting

The aim was to examine lumbar spine kinematics, spinal joint loads and trunk muscle activation patterns during a prolonged (2 h) period of sitting. This information is necessary to assist the ergonomist in designing work where posture variation is possible -- particularly between standing and various styles of sitting. Joint loads were predicted with a highly detailed anatomical biomechanical model (that incorporated 104 muscles, passive ligaments and intervertebral discs), which utilized biological signals of spine posture and muscle electromyograms (EMG) from each trial of each subject. Sitting resulted in significantly higher (p<0.001) low back compressive loads (mean +/- SD 1698 +/- 467 N) than those experienced by the lumbar spine during standing (1076 +/- 243 N). Subjects were equally divided into adopting one of two sitting strategies: a single 'static' or a 'dynamic' multiple posture approach. Within each individual, standing produced a distinctly different spine posture compared with sitting, and standing spine postures did not overlap with flexion postures adopted in sitting when spine postures were averaged across all eight subjects. A rest component (as noted in an amplitude probability distribution function from the EMG) was present for all muscles monitored in both sitting and standing tasks. The upper and lower erector spinae muscle groups exhibited a shifting to higher levels of activation during sitting. There were no clear muscle activation level differences in the individuals who adopted different sitting strategies. Standing appears to be a good rest from sitting given the reduction in passive tissue forces. However, the constant loading with little dynamic movement which characterizes both standing and sitting would provide little rest/change for muscular activation levels or low back loading.     

Relationship analysis between body flexion angles and smartphone tilt during smartphone use

Although smartphones are used as essential devices in everyday life, many users are exposed to joint diseases owing to prolonged use. The objectives of this study were to analyze how posture and smartphone tasks affect various body flexion angles and develop an algorithm to classify posture/task and estimate body flexion angles using smartphone tilt data. Eighteen participants performed two tasks (playing a game and reading news) in two postures (sitting and standing) in a laboratory environment. The three-axis orientation data (azimuth, pitch, and roll) of the smartphone and the participants’ body flexion angles were measured simultaneously. This study found that the cervical, thoracic, lumbar, and overall flexion angles were all statistically significantly different depending on the posture of the smartphone user, and the cervical flexion angle was significantly different depending on the task. Furthermore, task and task × posture can be classified with high accuracy based on smartphone tilt data, and tilt data had a high correlation with body flexion angles. Relevance to industry: The results of this study can be used as a reference for designing various products and interfaces for neck health. The results can be applied as a smartphone alarm or a built-in application, which can inform the user of the need to stretch his or her neck.     

Prediction of human reach posture using a neural network for ergonomic man models

For proper evaluation of operator's usability through ergonomic man models, accurate prediction of human reach is one of the essential functions that those models should possess. This study examined the applicability of artificial neural networks to the prediction of human reach posture. The three-dimensional motion trajectories of the joints of upper limb (shoulder, elbow, and wrist) in the right arm from 5 percentile female to 95 percentile male were obtained through a motion analysis system that photographed actual human reach. The data obtained were divided into two data sets — training data set and test data set. The backpropagation method being usually used for a pattern associator was employed as a tool for predicting such human movements. Comparisons between prediction and real measurements were made using a pairwise t-test, and no significant differences were found between the two data sets for all the joints considered. Thus, the neural network approach adopted in this study showed a very promising prediction capability of human reach and it is, therefore, expected that this method be used to accurately simulate human reach better than existing heuristic or analytic methods as well as to improve a human modelling capability in general.     

Low back joint loading and kinematics during standing and unsupported sitting

The aim was to examine lumbar spine kinematics, spinal joint loads and trunk muscle activation patterns during a prolonged (2 h) period of sitting. This information is necessary to assist the ergonomist in designing work where posture variation is possible—particularly between standing and various styles of sitting. Joint loads were predicted with a highly detailed anatomical biomechanical model (that incorporated 104 muscles, passive ligaments and intervertebral discs), which utilized biological signals of spine posture and muscle electromyograms (EMG) from each trial of each subject. Sitting resulted in significantly higher (p< 0.001) low back compressive loads (mean±SD 1698±467 N) than those experienced by the lumbar spine during standing (1076±243 N). Subjects were equally divided into adopting one of two sitting strategies: a single ‘static’ or a ‘dynamic’ multiple posture approach. Within each individual, standing produced a distinctly diVerent spine posture compared with sitting, and standing spine postures did not overlap with flexion postures adopted in sitting when spine postures were averaged across all eight subjects. A rest component (as noted in an amplitude probability distribution function from the EMG) was present for all muscles monitored in both sitting and standing tasks. The upper and lower erector spinae muscle groups exhibited a shifting to higher levels of activation during sitting. There were no clear muscle activation level diVerences in the individuals who adopted diVerent sitting strategies. Standing appears to be a good rest from sitting given the reduction in passive tissue forces. However, the constant loading with little dynamic movement which characterizes both standing and sitting would provide little rest/change for muscular activation levels or low back loading.     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

FGA优化RBF网络的机器人逆运动学求解研究

针对如何提高六自由度机器人逆运动学的求解精度问题,采用FGA对RBF神经网络的节点中心向量、基宽向量以及网络隐含层到输出层的权向量进行优化,并将其应用于六自由度机器人的逆运动学求解。以机器人工作空间的位姿矩阵作为预测网络的输入变量,以关节空间中的关节角度作为输出变量,构建机器人逆解RBF预测网络,然后选取样本对网络进行训练。最后对网络进行测试,仿真结果显示,优化后的网络预测精度高,泛化能力强。     

基于代价敏感间隔分布优化的软件缺陷定位

在大型软件项目的开发与维护中,从大量的代码文件中定位软件缺陷费时、费力,有效地进行软件缺陷自动定位,将能极大地降低开发成本.软件缺陷报告通常包含了大量未发觉的软件缺陷的信息,精确地寻找与缺陷报告相关联的代码文件,对于降低维护成本具有重要意义.目前,已有一些基于深度神经网络的缺陷定位技术相对于传统方法,其效果有所提升,但相关工作大多关注网络结构的设计,缺乏对训练过程中损失函数的研究,而损失函数对于预测任务的性能会有极大的影响.在此背景下,提出了代价敏感的间隔分布优化（cost-sensitive margin distribution optimization,简称CSMDO）损失函数,并将代价敏感的间隔分布优化层应用到深度卷积神经网络中,能够良好地处理软件缺陷数据的不平衡性,进一步提高缺陷定位的准确度.     

Real-time construction worker posture analysis for ergonomics training

Construction activities performed by workers are usually repetitive and physically demanding. Execution of such tasks in awkward postures can strain their body parts and can result in fatigue, injuries or in severe cases permanent disabilities. In view of this, it is essential to train workers, before the commencement of any construction activity. Furthermore, traditional worker monitoring methods are tedious, inefficient and are carried out manually whereas, an automated approach, apart from monitoring, can yield valuable information concerning work-related behavior of worker that can be beneficial for worker training in a virtual reality world. Our research work focuses on developing an automated approach for posture estimation and classification using a range camera for posture analysis and categorizing it as ergonomic or non-ergonomic. Using a range camera, first we classify worker’s pose to determine whether a worker is ‘standing’, ‘bending’, ‘sitting’, or ‘crawling’ and then estimate the posture of the worker using OpenNI middleware to get the body joint angles and spatial locations. A predefined set of rules is then formulated to use this body posture information to categorize tasks as ergonomic or non-ergonomic.