Non-intrusive quantification of performance and its relationship to mood

Abstract

The number of jobs that takes place entirely or partially in a computer is nowadays very signi cant. These workplaces, as many others, often o er the key ingredients for the emergence of stress and the performance drop of its long-term e ects: long hours sitting, sustained cognitive e ort, pressure from competitiveness, among others. This has a toll on productivity and work quality, with signi cant costs for both organizations and workers. Moreover, a tired workforce is generally more susceptible to negative feelings and mood, which results in a negative environment. This paper contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. We propose a framework that assesses worker performance and a case study in which this approach was validated. We also show the relationship between performance and mood.

A Review of Computational Modeling in Wastewater Treatment Processes

“…In the case of regression, the value to be predicted by the program follows a continuous spectrum and allows answering questions such as “how many are there” or “how much does it cost” . Decision trees (DTs), SVMs, and ANNs are some models present in this type of learning. − ANNs, one of the models most used in the simulation and prediction of the performance of biological treatment in WWTP, use models composed of several artificial neurons, connected by links of variable weight, to form black box representations of pseudoneurological systems . Each neuron receives input signals from other neurons, processes them, and sends out the output, which in turn is passed on as input to subsequent neurons .…”