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

Lenz,; Sotzek,; Roder,; Radrich,; Huber,; Glasauer, Stefan

doi:10.1109/iros.2011.6048188

Cited by 10 publications

(9 citation statements)

References 2 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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“…Despite of the findings of Gleeson et al, there is a lack of work focused on activity recognition in industrial environments. The work most similar to ours is that of Lenz et al [10]. The authors automatically recognised the activities of humans, who were sitting in front of a work table and collaborated with an industrial robot.…”

Section: Related Workmentioning

confidence: 95%

Human activity recognition in the context of industrial human-robot interaction

Roitberg

Perzylo

Somani

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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Human activity recognition is crucial for intuitive cooperation between humans and robots. We present an approach for activity recognition for applications in the context of humanrobot interaction in industrial settings. The approach is based on spatial and temporal features derived from skeletal data of human workers performing assembly tasks. These features were used to train a machine learning framework, which classifies discrete time frames with Random Forests and subsequently models temporal dependencies between the resulting states with a Hidden Markov Model. We considered the following three groups of activities: Movement, Gestures, and Object handling. A dataset has been collected which is comprised of 24 recordings of several human workers performing such activities in a human-robot interaction environment, as typically seen at small and medium-sized enterprises. The evaluation shows that the approach achieves a recognition accuracy of up to 88% for some activities and an average accuracy of 73%.

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“…In [52], we have presented a Hidden Markov Model (HMM) based workflow analysis of an assembly task jointly performed by a human and an assistive robotic system. In an experiment subjects had to assemble a tower by combining six cubes with several bolts for their own without the influence of a robot or any other technical device.…”

Section: B Capability: Recognitionmentioning

confidence: 99%

Mechanisms and capabilities for human robot collaboration

Lenz

Knoll

2014

The 23rd IEEE International Symposium on Robot and Human Interactive Communication

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This paper deals with the concept of a collaborative human-robot workspace in production environments recapitulating and complementing the work of the author presented in [1]. Different aspects regarding collaboration are discussed and applied in an exemplary scenario. Modalities including visualizations and audio are used to inform the human worker about next assembly steps and the current status of the system. The robot supplies the human worker with needed parts in an adaptive manner to prevent errors and to increase ergonomic benefits. Further, the human worker can intuitively interact and adjust the robot using projected menus on the worktable and by force-guidance of the robot. All these functions are brought together in an overall architecture.

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

Cited by 10 publications

References 2 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

Human activity recognition in the context of industrial human-robot interaction

Mechanisms and capabilities for human robot collaboration

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