Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/55124

TitleA multi-modal architecture for non-intrusive analysis of performance in the workplace
Author(s)Carneiro, Davide Rua
Pimenta, André
Neves, José
Novais, Paulo
KeywordsMonitoring
Neural network
Non-intrusive
Performance
Issue date29-Mar-2017
PublisherElsevier B.V.
JournalNeurocomputing
Abstract(s)Human performance, in all its different dimensions, is a very complex and interesting topic. In this paper we focus on performance in the workplace which, asides from complex is often controversial. While organizations and generally competitive working conditions push workers into increasing performance demands, this does not necessarily correlates positively to productivity. Moreover, existing performance monitoring approaches (electronic or not) are often dreaded by workers since they either threat their privacy or are based on productivity measures, with specific side effects. We present a new approach for the problem of performance monitoring that is not based on productivity measures but on the workers’ movements while sitting and on the performance of their interaction with the machine. We show that these features correlate with mental fatigue and provide a distributed architecture for the non-intrusive and transparent collection of this data. The easiness in deploying this architecture, its non-intrusive nature, the potential advantages for better human resources management and the fact that it is not based on productivity measures will, in our belief, increase the willingness of both organizations and workers to implement this kind of performance management initiatives.
TypeArticle
URIhttp://hdl.handle.net/1822/55124
DOI10.1016/j.neucom.2016.05.105
ISSN0925-2312
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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