Effect of Angle of Abduction and Illumination Level on Performance in a Human-Machine Interaction Environment

2017 ◽  
Vol 8 (4) ◽  
pp. 11
Author(s):  
Deepali Atheaya ◽  
Jasleen Bhatti
Keyword(s):  
Experimental Investigation ◽  
Human Performance ◽  
Illumination Level ◽  
Human Machine Interaction ◽  
Machine Interaction

An experimental investigation was conducted to evaluate the human performance in the context of human-machine interaction environment. It was observed that both the illumination level and an angle of abduction have significant effect on human performance when workers work in human-machine interaction environment.

scholarly journals Identify eight aspects of ergonomics to determine the improvement of human-machine interaction work (case studies in manufacturing industry)

2018 ◽  
Vol 218 ◽  
pp. 04018
Author(s):  
Wahyu Susihono ◽  
Tania Anggi Saputri
Keyword(s):  
Work Performance ◽  
Manufacturing Industry ◽  
Human Performance ◽  
Muscle Power ◽  
Raw Materials ◽  
Appropriate Technology ◽  
Human Machine Interaction ◽  
Cross Sectional ◽  
Variable Effect ◽  
Machine Interaction

Manufacturing Industry is one of the industrial activities in Indonesia, manufacturing industry is an industry with main activities is to change raw materials, components, or other parts into goods which is according to company specifications standards. In the production floor, activity in the manufacturing industry, the workers have different job specifications with each other. Some works consist of human-machine interaction is found by the activity between workers and lathe machine, welding maching, milling machine, frais machine, and others. The manufacturing industry will increase its ability to serve a variety of better quality products caused by the desire or encouragement of the customers. In general, according to the increase of corporate targets, its also need improvement from the aspect of work performance. To obtain a description of the proposed improvement based on human performance, it is necessary to identify the eight aspects of ergonomics include the consumption of nutritionalfor workers (energy), muscle power, body posture, environment, time conditions, social conditions, information conditions, and human machine interaction. This research use cross sectional method approach that is research done at one time, no follw up, to find the correlation between independent variable (risk factor) with dependent variable (effect). The conclusion of this research is needed nutrition intake or nutrition to recover the workers, it is necessary to design facilities such as manufacturing aids to reduce the use of excess muscle or appropriate technology (TTG). After the application of TTG (Appropriate Technology) to reduce the excessive use of muscle to the workers, the company should provide the nutritional intake accordance with workload of employees in the manufacturing industry

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.

On Developing Theory-Based Functions to Moderate Human Performance Models in the Context of Systems Analysis

1989 ◽  
Vol 33 (2) ◽  
pp. 105-109 ◽  
Author(s):  
Kevin Boettcher ◽  
Robert North ◽  
Victor Riley
Keyword(s):  
Systems Analysis ◽  
Decision Aid ◽  
Human Performance ◽  
Resource Theory ◽  
Human Machine Interaction ◽  
Assessment Technique ◽  
Interaction Dynamics ◽  
Multiple Resource Theory ◽  
Multiple Resource ◽  
Machine Interaction

Preliminary work is described toward the development of moderator functions for systems analysis that reflect human behavioral limitations and tendencies. In particular, a model of human-machine interaction dynamics in complex systems is introduced to give a moderating influence on overall operator/decision aid performance. A key input to this model is the operator's current workload. To further ground the moderator function in human behavioral considerations, a multiple resource theory-based workload assessment technique is used to provide this input.

scholarly journals Human social motor solutions for human–machine interaction in dynamical task contexts

2019 ◽  
Vol 116 (4) ◽  
pp. 1437-1446 ◽  
Author(s):  
Patrick Nalepka ◽  
Maurice Lamb ◽  
Rachel W. Kallen ◽  
Kevin Shockley ◽  
Anthony Chemero ◽  
...  
Keyword(s):  
Human Performance ◽  
Social Contexts ◽  
Artificial Agent ◽  
Human Machine Interaction ◽  
Multiagent Coordination ◽  
Agent Behavior ◽  
Behavioral Dynamics ◽  
Social Encounters ◽  
Health Sport ◽  
Machine Interaction

Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human–machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human–human and human–artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human–human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a “Turing-like” methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.

New Methods for Modeling Human-Machine Interaction

1996 ◽  
Vol 40 (4) ◽  
pp. 162-163
Author(s):  
Raymond S. Nickerson
Keyword(s):  
Complex Systems ◽  
Mental Models ◽  
Human Performance ◽  
Human Machine Interaction ◽  
Information Theoretic ◽  
Modeling Methods ◽  
The Third ◽  
New Methods ◽  
Information Theoretic Method ◽  
Machine Interaction

Use of the term mental model has proliferated in the discussion of human-machine interaction. Although it seems clear that humans must depend on mental models when doing problem solving in the domain of complex systems, the literature on the topic presents a confusing variety of perspectives, and there is little empirical evidence of the structure of the models people use or of how they influence human performance. The objectives of this symposium are to (a) provide a taxonomy for mental models and suggest a theory that is intended to unify what appear now to be disparate views, (b) outline an information-theoretic method for determining the structure of complex systems, and (c) describe an application of the theory and method to a process-control simulation. In the first presentation, Moray makes the case for the need for modeling methods that can deal effectively with systems of unusual complexity. In the second, Conant describes such a method. Jamieson, in the third, reports the results of an experiment in which this method was applied.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

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