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

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.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. The work of Davide Carneiro is supported by a Post-Doctoral Grant by FCT (SFRH/BPD/109070/2015).info:eu-repo/semantics/publishedVersio

Deteção de Fadiga através de uma abordagem baseada em eventos

O advento da Indústria 5.0, introduz na Europa uma abordagem centrada no ser humano, que pretende garantir o bem-estar dos trabalhadores nos processos produtivos, incluindo uma especial preocupação com a monitorização e gestão da sua saúde física e mental, recorrendo a novas tecnologias como as de interação homem máquina, os Digital Twins e a inteligência artificial. A literatura indica que se um trabalhador estiver cansado, é mais suscetível a cometer erros e tem uma pior performance, assim é importante avaliar o seu estado de fadiga. Apesar de existirem várias investigações relacionando a fadiga com os tempos de reação em testes Go/NoGo, não existem detetores baseados neste sistema. Este trabalho reporta a criação de um detetor de fadiga baseado neste sistema, recorrendo à aprendizagem automática. Os dados recolhidos indicam uma maior correlação da tarefa NoGo com a fadiga, notando-se uma redução do tempo de reação para níveis de fadiga elevados. O modelo final, de previsão da fadiga em três classes apresenta uma taxa de acerto de 57%. Mas um modelo alternativo de deteção de fadiga muito elevada apresenta uma taxa de acerto de 84%, revelando uma adequação do teste Go/NoGo a este propósito.The advent of Industry 5.0 introduces a human-centered approach in Europe, which aims to ensure the well-being of workers in production processes, including a special concern with the monitoring and management of their physical and mental health. Using new technologies such as human-machine interaction, Digital Twins and artificial intelligence. The literature indicates that if a worker is tired, he is more susceptible to making mistakes and has a worse performance, so it is important to assess his state of fatigue. Although there are several investigations relating fatigue with reaction times in Go/NoGo tests, there are no detectors based on this system. This work reports the creation of a fatigue detector based on this system, using machine learning. The collected data indicates a greater correlation of the NoGo task with fatigue than the reaction time of the Go task, and a reduction in the reaction time for high levels of fatigue. The final model, predicting fatigue in three classes, has an accuracy of 57%. But an alternative model of very high fatigue detection has an accuracy of 84%, revealing the suitability of the Go/NoGo test for this purpose