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

Roitberg, Alina; Perzylo, Alexander; Somani, Nikhil; Giuliani, Manuel; Rickert, Markus; Knoll, Alois

doi:10.1109/apsipa.2014.7041588

Cited by 62 publications

(18 citation statements)

References 25 publications

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“…The increasing degree of automation also does not necessarily lead to enhanced operator performance [20]. Handling human factors is a challenging problem concerning both cellular manufacturing [21] and human-robot interaction [22]. For example, smart factories have to take into consideration operators who are aging or apprentices by using advanced technologies to help people to integrate into the modern manufacturing workforce [23].…”

Section: Introductionmentioning

confidence: 99%

Enabling Technologies for Operator 4.0: A Survey

et al. 2018

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The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.

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Section: Introductionmentioning

confidence: 99%

Enabling Technologies for Operator 4.0: A Survey

et al. 2018

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“…In some approaches the feature space is either limited or is constrained to a specific scenario. For example, in [10] the method strongly relies on the hip-joint for action classification and [15] restricts the feature space by mainly concentrating on the movement of hands for action recognition. In an industrial setup, however, it is not guaranteed that certain parts of the human body will be visible throughout the action.…”

Section: Related Workmentioning

confidence: 99%

“…Most approaches assume that the actions are segmented temporally [22][20] [15], but in real-world scenarios, actions do not occur in segmented fashion but in combination with other actions. The approach in [9] while assuming the availability of segmented actions also mention that only one action is taking place at a given instant of time, for example if a person is walking and waving his hands, only either of the actions would be classified.…”

Section: Related Workmentioning

confidence: 99%

“…However, most these approaches mainly deal with action classification in domestic scenarios. Only few approaches are proposed [15] [16], which consider action classification in an industrial scenarios. There are three key criteria that need consideration when dealing with human-robot interaction in an industrial setting: a) requirement of quick reaction and low latency, b) reconfigurability and c) high flexibility.…”

Section: Introductionmentioning

confidence: 99%

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Action recognition for human robot interaction in industrial applications

Akkaladevi

Heindl

2015

2015 IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS)

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Human action recognition plays a vital role in the field of human-robot interaction and is widely researched for its potential applications. In this paper we propose a human action recognition framework for human robot interaction in industrial applications. First, a set of key descriptors are learned from a collection of weak spatio-temporal skeletal joint descriptors using random forests, which reduces the dimensionality and computational effort. We show that our approach reduces the descriptor dimensionality by 61 percent. The key descriptors are used with a multi-label one-versus-all binary random forest classifier for action classification. We propose an extension to the framework that allows recognizing multiple actions for a given time instant. This results in a low latency, flexible and reconfigurable method that performs on par with other sophisticated approaches on challenging benchmarks like the MSR Action 3D dataset.

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“…This method is effective for recognizing basic emotions but was not effective for compound emotions such as anger and surprise. In [191], an activity recognition method was proposed for HRI in industrial settings. It was based on the spatial and temporal features from skeletal data of the human workers performing the assembly task.…”

Section: Human-robot Interactionmentioning

confidence: 99%

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

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

Cited by 62 publications

References 25 publications

Enabling Technologies for Operator 4.0: A Survey

Enabling Technologies for Operator 4.0: A Survey

Action recognition for human robot interaction in industrial applications

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

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