Human workflow analysis using 3D occupancy grid hand tracking in a human-robot collaboration scenario

Lenz, Claus; Sotzek, Alice; Röder, Thorsten; Radrich, Helmuth; Knoll, Alois; Huber, Markus A.; Glasauer, Stefan

doi:10.1109/iros.2011.6094570

Cited by 23 publications

(3 citation statements)

References 25 publications

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“…It can also be applied to human-action pattern recognition via sensor-data streams. Related works demonstrate pattern recognition [33] and prediction [34,35]. One of the challenges surrounding the use of HMM for online applications is the issue of a method for dealing with data segmentation via data streams and recognizing patterns via short segments.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Chen

Zhong

Cannella

et al. 2015

International Journal of Advanced Robotic Systems

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Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM) method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.

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Section: Introductionmentioning

confidence: 99%

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Chen

Zhong

Cannella

et al. 2015

International Journal of Advanced Robotic Systems

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“…With this recognition method, they were able to robustly detect human motions, including walking, running, jumping, and sitting. Lenz et al [7] trained an HMM with data from a hand tracking device to automatically recognize the states of an interaction in an industrial human-robot interaction scenario. Finally, Vinciarelli et al [8] give a general overview on the field of social signal recognition.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Social behavior recognition using body posture and head pose for human-robot interaction

Gaschler

Jentzsch

Giuliani

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Robots that interact with humans in everyday situations, need to be able to interpret the nonverbal social cues of their human interaction partners. We show that humans use body posture and head pose as social signals to initiate and terminate interaction when ordering drinks at a bar. For that, we record and analyze 108 interactions of humans interacting with a human bartender. Based on these findings, we train a Hidden Markov Model (HMM) using automatic body posture and head pose estimation. With this model, the bartender robot of the project JAMES can recognize typical social behaviors of human customers. Evaluation shows a recognition rate of 82.9 % for all implemented social behaviors and in particular a recognition rate of 91.2 % for bartender attention requests, which will allow the robot to interact with multiple humans in a robust and socially appropriate way.

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“…Sensors are a crucial element in the development of a cognitive assembly line, namely two deployment modalities for sensing in an assembly line can be found in the literature: infrastructure based sensors and mobile or wearable sensors. These are based on sensory information in the visual domain [31,32], auditory domain [33,34], and haptic/mechanical domain [35][36][37]. In addition, we see that these sensors not only monitor humans and their behaviour [38][39][40] but also equipment [41,42], individual parts of the product [43,44] and machines [45].…”

Section: Sensingmentioning

confidence: 99%

Towards Flexible and Cognitive Production—Addressing the Production Challenges

et al. 2022

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Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity.

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Human workflow analysis using 3D occupancy grid hand tracking in a human-robot collaboration scenario

Cited by 23 publications

References 25 publications

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

Social behavior recognition using body posture and head pose for human-robot interaction

Towards Flexible and Cognitive Production—Addressing the Production Challenges

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