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

Davide Carneiro,André Pimenta,José Neves,Paulo Novais

doi:10.1016/j.neucom.2016.05.105

Davide Carneiro, André Pimenta + Show 2 more

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https://doi.org/10.1016/j.neucom.2016.05.105

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Journal: Neurocomputing	Publication Date: Oct 18, 2016
Citations: 9	License type: publisher-specific-oa

Affiliation: University of Minho

Abstract

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.

Highlights

The change in the job offers in the last decades, caused by technological evolution, brings along many significant and broad changes
This study examined how productivity and subjective experiences are affected by Electronic Performance Monitoring (EPM) systems and how the social context of the workplace moderates that influence
This paper presented a distributed architecture for the nonintrusive acquisition of interaction and behavioral features for the classification of human performance

Summary

Introduction

The change in the job offers in the last decades, caused by technological evolution, brings along many significant and broad changes. The worst aspect about this approach is that it only points out a potential decrease of performance after a productivity loss This means that the “damage” is already done and that it is most likely too late for the employee to cope with whatever caused the performance loss. An approach that could point out, in advance, upcoming breaks in performance (e.g. through the observation of behavioral patterns) could allow for preventive interventions rather than reactive [2]. Another major aspect that current approaches fail to consider are the side effects of productivity or performance monitoring in the workplace [3]. In a study conducted in 1995 by researchers of the State University of New Jersey, it was analyzed the impact of electronic performance monitoring and its social context on the productivity and stress of employees [4]

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