A multi-modal architecture for non-intrusive analysis of performance in the workplace

详细信息    查看全文

• 作者：Davide Carneiro^a ; ^b ; ^{dcarneiro@di.uminho.pt} ; Author Vitae ; André ; Pimenta^a ; ^{apimenta@di.uminho.pt} ; Author Vitae ; José ; Neves^a ; ^{jneves@di.uminho.pt} ; Author Vitae ; Paulo Novais^a ; ^{pjon@di.uminho.pt} ; Author Vitae
• 关键词：Performance ; Monitoring ; Neural network ; Non-intrusive
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：29 March 2017
• 年：2017
• 卷：231
• 期：Complete
• 页码：41-46
• 全文大小：650 K
• 卷排序：231

文摘

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.