Toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks

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dc.contributor.author Parsa, Behnoosh
dc.contributor.author Samani, Ekta
dc.contributor.author Hendrix, Rose
dc.contributor.author Devine, Cameron
dc.contributor.author Singh, Shashi
dc.contributor.author Devasia, Santosh
dc.contributor.author Banerjee, Ashis G.
dc.date.accessioned 2019-07-16T09:58:27Z
dc.date.available 2019-07-16T09:58:27Z
dc.date.issued 2019-07
dc.identifier.citation Parsa, Behnoosh; Samani, Ekta; Hendrix, Rose; Devine, Cameron; Singh, Shashi; Devasia, Santosh and Banerjee, Ashis, �Toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks�, IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2019.2925305, Jul. 2019. en_US
dc.identifier.issn 2377-3766
dc.identifier.issn 2377-3774
dc.identifier.uri https://doi.org/10.1109/LRA.2019.2925305
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4610
dc.description.abstract Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications. We present a foundational paradigm to address this challenge by formulating the problem as one of action segmentation from RGB-D camera videos. Spatial features are first learned using a deep convolutional model from the video frames, which are then fed sequentially to temporal convolutional networks to semantically segment the frames into a hierarchy of actions, which are either ergonomically safe, require monitoring, or need immediate attention. For performance evaluation, in addition to an open-source kitchen dataset, we collected a new dataset comprising twenty individuals picking up and placing objects of varying weights to and from cabinet and table locations at various heights. Results show very high (87-94)% F1 overlap scores among the ground truth and predicted frame labels for videos lasting over two minutes and consisting of a large number of actions.
dc.description.statementofresponsibility by Behnoosh Parsa, Ekta Samani, Rose Hendrix, Cameron Devine, Shashi Singh, Santosh Devasia and Ashis Banerjee
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Videos en_US
dc.subject Ergonomics en_US
dc.subject Cameras en_US
dc.subject Feature extraction en_US
dc.subject Safety en_US
dc.subject Service robots en_US
dc.title Toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks en_US
dc.type Article en_US
dc.relation.journal IEEE Robotics and Automation Letters


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