Discovery and recognition of motion primitives in human activities

Sanzari, Marta; Ntouskos, Valsamis; Pirri, Fiora

doi:10.1371/journal.pone.0214499

Cited by 14 publications

(7 citation statements)

References 77 publications

(136 reference statements)

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“…Social interactions involve non trivial tasks, such as intention prediction (Sebanz and Knoblich, 2009 ; Ognibene and Demiris, 2013 ; Donnarumma et al, 2017a ), activity recognition (Ansuini et al, 2015 ; Lee et al, 2015 ; Sanzari et al, 2019 ) or even simple gesture recognition (e.g., pointing at a target), which may require perceptual policies that are difficult to precompile. This is because they are contingent on previous observations, hierarchically organized (Proietti et al, 2021 ), and must extend over time, space and scene elements which may not be always visible (Ognibene et al, 2013 ).…”

Section: The Challenge Of Social Interactionsmentioning

confidence: 99%

Editorial: Active Vision and Perception in Human-Robot Collaboration

et al. 2022

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Section: The Challenge Of Social Interactionsmentioning

confidence: 99%

Editorial: Active Vision and Perception in Human-Robot Collaboration

et al. 2022

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“…pressing, lifting, reaching, or even unnamed ones [44]. MP have been recognized in other tasks by analyzing kinematics [47] [48] or dynamic data [49], muscular activity [50] [51], or video [44] [52]. Note that MP in Fig.…”

Section: Taxonomy Of Actions In Foodservicementioning

confidence: 99%

Towards Foodservice Robotics: A Taxonomy of Actions of Foodservice Workers and a Critical Review of Supportive Technology

Pereira

Bozzato

Dario

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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Foodservice workers perform several burdensome, tedious, and unsafe tasks that risk their health and well-being. This could be mitigated or even more avoided by using autonomouslyactuated machines. Therefore, this article aims to build the foundation to support the development of a new field of robotics research dedicated to foodservice and with a human/workercentered framework. As so, we introduce a two-level taxonomy of basic actions that compose the physical tasks of foodservice workers; it can guide future studies to design bio-inspired control models for foodservice robots. Actions are clustered in 16 categories according to their purpose and to the handled food. Furthermore, authors make a critical review of single-action equipment (SAE) and advanced equipment (AE) currently available for foodservice, which allowed us to identify opportunities for research. As a result, authors found some categories of actions rarely automated, aimed at i) separating solid-solid food parts, ii) moving food between workstations or independent appliances in the kitchen, iii) introducing food into another solid food or recipient, and iv) other specific actions, e.g. sewing food. In addition, authors discuss the applicability of collaborative robotics and human-robot collaboration to different contexts in foodservice, and show how artificial intelligence is improving the capabilities of SAE and AE and what else it could improve in this context. Note to practitioners -This paper was motivated by a criticalneed in foodservice: the ability to produce consistent and highquality meals ad-hoc, without overloading the workers or harming their health. Robotic and autonomous systems are promising technologies to solve this. However, there is not a unified framework in robotics research focused on the professional foodservice environment. This paper provides two tools for researchers and engineers in this field: (i) a taxonomy of basic actions that foodservice workers perform during their physical tasks; and (ii) a systematic review of mechatronic systems being developed or already in use in foodservice. The taxonomy can be immediately useful to divide research and development by the classes of actions. In addition, we found specific categories of actions that have been rarely automated so far and need further investigation. The results of our review can be readily applied in industry, too: presently, most equipment is a custom-built machine with limited adaptiveness; when systems include industrial robots, cobots are being preferred; the implementation of collaborative operations between humans and robots is not common yet and its applicability may be suitable only for certain contexts; finally, we identify scientific publications introducing

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“…Human motion recognition is a key technology for intelligent video surveillance. It is widely used in various scenarios such as human-computer interaction [1], motion analysis [2][3][4][5][6][7], intelligent monitoring, gesture recognition [8,9], and facial emotion recognition [10][11][12][13]. Human motion recognition is divided into single-person motion recognition and multi-person interactive motion recognition.…”

Section: Introductionmentioning

confidence: 99%

Human Interaction Recognition Based on Whole-Individual Detection

Zhong

Chang

et al. 2020

Sensors

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Human interaction recognition technology is a hot topic in the field of computer vision, and its application prospects are very extensive. At present, there are many difficulties in human interaction recognition such as the spatial complexity of human interaction, the differences in action characteristics at different time periods, and the complexity of interactive action features. The existence of these problems restricts the improvement of recognition accuracy. To investigate the differences in the action characteristics at different time periods, we propose an improved fusion time-phase feature of the Gaussian model to obtain video keyframes and remove the influence of a large amount of redundant information. Regarding the complexity of interactive action features, we propose a multi-feature fusion network algorithm based on parallel Inception and ResNet. This multi-feature fusion network not only reduces the network parameter quantity, but also improves the network performance; it alleviates the network degradation caused by the increase in network depth and obtains higher classification accuracy. For the spatial complexity of human interaction, we combined the whole video features with the individual video features, making full use of the feature information of the interactive video. A human interaction recognition algorithm based on whole–individual detection is proposed, where the whole video contains the global features of both sides of action, and the individual video contains the individual detail features of a single person. Making full use of the feature information of the whole video and individual videos is the main contribution of this paper to the field of human interaction recognition and the experimental results in the UT dataset (UT–interaction dataset) showed that the accuracy of this method was 91.7%.

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Discovery and recognition of motion primitives in human activities

Cited by 14 publications

References 77 publications

Editorial: Active Vision and Perception in Human-Robot Collaboration

Editorial: Active Vision and Perception in Human-Robot Collaboration

Towards Foodservice Robotics: A Taxonomy of Actions of Foodservice Workers and a Critical Review of Supportive Technology

Human Interaction Recognition Based on Whole-Individual Detection

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