scholarly journals A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6347
Author(s):  
Fatemeh Mohammadi Amin ◽  
Maryam Rezayati ◽  
Hans Wernher van de Venn ◽  
Hossein Karimpour
Keyword(s):  
Action Recognition ◽  
Manufacturing Systems ◽  
Physical Contact ◽  
Contact Detection ◽  
Human Beings ◽  
Industrial Automation ◽  
Human Actions ◽  
Shared Workspace ◽  
Human Safety ◽  
Human Robot Collaboration

Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

scholarly journals A Mixed Perception Approach for Safe Human-Robot Collaboration in Industrial Automation

2020 ◽  
Author(s):  
Fatemeh Mohammadi Amin ◽  
Maryam Rezayati ◽  
Hans Wernher van de Venn ◽  
Hossein Karimpour
Keyword(s):  
Action Recognition ◽  
Manufacturing Systems ◽  
Detection System ◽  
Low Cost ◽  
Human Action ◽  
Physical Contact ◽  
Contact Detection ◽  
Human Beings ◽  
Industrial Automation ◽  
Human Robot Collaboration

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings. This issue is addressed in this work by implementing a reliable system for real-time safe human-robot collaboration based upon the combination of human action and contact type detection systems. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and robot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, lead to the enhancement of human safety and an increase of the level of robot awareness about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Security robot for the prevention of workplace violence using the non-linear adaptive heuristic mathematical model

Work ◽  
2021 ◽  
pp. 1-9
Author(s):  
Hai Tao ◽  
Md Arafatur Rahman ◽  
Ahmed AL-Saffar ◽  
Renrui Zhang ◽  
Sinan Q Salih ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Workplace Violence ◽  
Physical Contact ◽  
Optimal Decision ◽  
Human Being ◽  
Human Beings ◽  
Wide Range ◽  
Non Linear ◽  
Adaptive Heuristic ◽  
Human Robot Collaboration

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects. OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology. RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions. CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively.

scholarly journals PLATÃO: AS PAIXÕES DA UNIDADE "CORPOALMA" SEGUNDO O TIMEU

Sapere Aude ◽  
2019 ◽  
Vol 10 (19) ◽  
pp. 31-42
Author(s):  
Maria Dulce Reis
Keyword(s):  
Human Body ◽  
Human Beings ◽  
Human Actions ◽  
Doctoral Thesis ◽  
The Status ◽  
Present Text ◽  
Ethics And Politics

Uma das últimas obras escritas por Platão, o Timeu, nos parece um dos textos mais ricos para identificarmos o estatuto das paixões na filosofia de Platão: a origem dessas paixões/afecções, suas propriedades, seu papel no equilíbrio psíquico e na condução das ações humanas. Tal riqueza, profundidade e extensão teórica constituiu grande parte de nossa tese de doutoramento, que visou demonstrar a articulação entre Psicologia, Ética e Política nos diálogos República, Timeu e Leis. No presente texto, nos limitaremos a apresentar nossa interpretação a respeito de passagens da cosmologia do Timeu dedicadas a tratar da constituição da unidade corpoalma humana, o que inclui suas afecções. Nosso recorte limita-se a mostrar que as afecções – próprias ao que há de apetitivo, irascível e racional na unidade corpoalma humana – decorrem da encarnação, e o seu direcionamento psíquico é capaz de conduzir os seres humanos à saúde ou à doença, à virtude ou ao vício.PALAVRAS-CHAVE: Platão. Filosofia Antiga. Psicologia. Páthos. ABSTRACT:One of the last works written by Plato, the Timaeus, seems to us one of the richest texts to identify the status of passions in Plato's philosophy: The origin of the passions/affections, their properties, their role in the psychic balance and the conduct of human actions. Such wealth, depth and theoretical extension constituted a large part of our doctoral thesis, that aimed to demonstrate the articulation between Psychology, Ethics, and Politics in the dialogues Republic, Timaeus, and Laws. In the present text, we shall confine ourselves to our interpretation of passages in the cosmology of the Timaeus devoted to the constitution of the human body-soul unity, which includes its affections. Our clipping is limited to showing that the affections - proper to that which is appetitive, irascible and rational in the human body-soul unity - elapsed from the incarnation and its psychic direction are capable of leading human beings to health or sickness, into virtue or vice.PALAVRAS-CHAVE: Platão. Filosofia Antiga. Psicologia. Páthos.

Graphical Models for Representation and Recognition of Human Actions

2010 ◽  
pp. 31-54 ◽  
Author(s):  
Pradeep Natarajan ◽  
Ramakant Nevatia
Keyword(s):  
Action Recognition ◽  
Graphical Models ◽  
Video Processing ◽  
Hierarchical Organization ◽  
Training Data ◽  
State Transitions ◽  
Human Actions ◽  
Low Level ◽  
Spatio Temporal ◽  
Representation Scheme

Building a system for recognition of human actions from video involves two key problems - 1) designing suitable low-level features that are both efficient to extract from videos and are capable of distinguishing between events 2) developing a suitable representation scheme that can bridge the large gap between low-level features and high-level event concepts, and also handle the uncertainty and errors inherent in any low-level video processing. Graphical models provide a natural framework for representing state transitions in events and also the spatio-temporal constraints between the actors and events. Hidden Markov models(HMMs) have been widely used in several action recognition applications but the basic representation has three key deficiencies: These include unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of these activities. Several extensions have been proposed to address one or more of these issues and have been successfully applied in various gesture and action recognition domains. More recently, conditionalrandomfields (CRF) are becoming increasingly popular since they allow complex potential functions for modeling observations and state transitions, and also produce superior performance to HMMs when sufficient training data is available. The authors will first review the various extension of these graphical models, then present the theory of inference and learning in them and finally discuss their applications in various domains.

scholarly journals UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions

2020 ◽  
Vol 34 (03) ◽  
pp. 2677-2684
Author(s):  
Marjaneh Safaei ◽  
Pooyan Balouchian ◽  
Hassan Foroosh
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Large Scale ◽  
Temporal Information ◽  
Training Data ◽  
Body Motion ◽  
Human Actions ◽  
Still Images ◽  
Still Image ◽  
Novel Approach

Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images. The key characteristics of UCF-STAR include (1) focusing on human body-motion rather than relatively static human-object interaction categories, (2) collecting images from the wild to benefit from a varied set of action representations, (3) appending multiple human-annotated labels per image rather than just the action label, and (4) inclusion of rich, structured and multi-modal set of metadata for each image. This departs from existing datasets, which typically provide single annotation in a smaller number of images and categories, with no metadata. UCF-STAR exposes the intrinsic difficulty of action recognition through its realistic scene and action complexity. To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets.

Industrial Automation and Manufacturing Systems: Concepts and Applications

2014 ◽  
Vol 903 ◽  
pp. 291-296
Author(s):  
Abdul Sattar ◽  
Qadir Bakhsh ◽  
Muhammad Sharif
Keyword(s):  
Radio Frequency Identification ◽  
Lean Manufacturing ◽  
Intelligent Systems ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Numerical Control ◽  
Computer Integrated Manufacturing ◽  
Industrial Automation ◽  
Advanced Technique ◽  
Programmable Logic Control

Manufacturing comprises an effective and efficient integration of automation tools and advanced technologies for the industrial production. Automation is an advanced technique used in the manufacturing industry for integrating the machine tools to automatically perform different tasks. This paper presents the study about industrial automation and manufacturing system. The research and development in the area of automation includes programmable logic control (PLC), robotics, distributed control system (DCS), computerized numerical control machine (CNC), radio frequency identification (RFID). The intelligent systems for scheduling and manufacturing the product such as flexible manufacturing systems (FMS), computer aided manufacturing (CAM), computer integrated manufacturing (CIM), lean manufacturing and green manufacturing.

An Outline of Genetic Psychology According to the Theory of Inherited Mind

Philosophy ◽  
1931 ◽  
Vol 6 (23) ◽  
pp. 347-364
Author(s):  
R. F. Rattray
Keyword(s):  
Medical Science ◽  
The Other ◽  
Human Beings ◽  
Human Actions ◽  
Genetic Psychology ◽  
Samuel Butler ◽  
Physical Universe ◽  
The One

One of the great difficulties in effecting a synthesis of experience is the contradiction of the apparently mechanical character of the physical universe on the one hand, and the sense of freedom we associate with life on the other. In our own persons, we are told by medical science, or some of it, we are governed by physiological laws which are mechanical, as distinct from vital, in their nature. The best reconciliation of these with freedom, in the writer's opinion, is the philosophy of Samuel Butler. In studying freedom as experienced by human beings Butler pointed out that a large number of practices which are apparently mechanical are really habits that have become stereotyped, and he drew attention to the fact that human actions can be classified as follows:—

Ifs and Cans Revisited

Philosophy ◽  
1962 ◽  
Vol 37 (141) ◽  
pp. 245-256 ◽  
Author(s):  
Don Locke
Keyword(s):  
Original Problem ◽  
Basic Problem ◽  
Human Beings ◽  
General Sense ◽  
Human Actions

In this paper I shall be principally concerned with three points arising from Professor Austin's British Academy Lecture on ‘Ifs and Cans’.1 These points only concern that use of ‘can’ where it is used in the general sense of ‘to be able’ and applied to human beings in respect of actual or possible actions.2 To some extent, of course, the basic problem is simply what sense of ‘can’ it is which is involved when we talk of possible but not actual human actions, i.e. when we say that a person could do or could have done what he does not or did not do (this was Moore's original problem).

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

scholarly journals Michel Henry: Sense of self and hallucination

2016 ◽  
Vol 33 (3) ◽  
pp. 425-430
Author(s):  
Florinda MARTINS ◽  
Andrés Eduardo Aguirre ANTÚNEZ
Keyword(s):  
Clinical Practice ◽  
Sense Of Self ◽  
Human Beings ◽  
Human Actions ◽  
Starting Point ◽  
Michel Henry ◽  
Therapeutic Needs

Abstract In this paper we develop the thesis of the possibility of understanding human beings, starting from the phenomenality of their therapeutic needs. We bring the phenomenality of hallucination to the center of the debate. We show how, in Michel Henry, the phenomenality of sight, touch and anguish is, in all, comparable to the phenomenality of hallucination. From the starting point of this phenomenality we will understand human actions and thus, the essence of clinical practice.

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