RESUMEN
This paper addresses the issue of evaluating the Quality of Experience (QoE) for Internet of Things (IoT) applications, with particular attention to the case where multimedia content is involved. A layered IoT architecture is firstly analyzed to understand which QoE influence factors have to be considered in relevant application scenarios. We then introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a use case related to the remote monitoring of vehicles during driving practices, which is used to validate the proposed layered model, and we discuss a second use case for smart surveillance, to emphasize the generality of the proposed framework. The effectiveness in evaluating classes of influence factors separately is demonstrated.
RESUMEN
The current pandemic situation has led to an extraordinary increase in remote working activities all over the world. In this paper, we conducted a research study with the aim to investigate the Quality of Remote Working Experience (QRWE) of workers when conducting remote working activities and to analyse its correlation with implicit emotion responses estimated from the speech of video-calls or discussions with people in the same room. We implemented a system that captures the audio when the worker is talking and extracts and stores several speech features. A subjective assessment has been conducted, using this tool, which involved 12 people that were asked to provide feedback on the QRWE and assess their sentiment polarity during their daily remote working hours. ANOVA results suggest that speech features may be potentially observed to infer the QRWE and the sentiment polarity of the speaker. Indeed, we have also found that the perceived QRWE and polarity are strongly related.