Alessandro Floris

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction.

While demand response programs inherently depend on consumer acceptance in order to be successful, consumer behavior is often overlooked when designing such programs. This paper addresses the impact of consumer flexibility in terms of appliance use on the success of a demand response program, measured through the overall grid stability assessed by the demand’s peak-to-average ratio. We employ a bootstrapping approach to simulate energy communities from real-life consumer data and implement a state-of-the-art demand response system. Results suggest that higher consumer flexibility under real-time energy tariffs implies higher degrees of grid stability, with real-time pricing decreasing the average peak-to-average ratio by 4.6% compared to time-of-use tariff and with highly flexible consumers showing a 23% lower peak-to-average ratio than regular consumers on average. Yet, it is possible for higher flexibility to be detrimental to grid stability by increasing the peak-to-average ratio under a time-of-use tariff. This result highlights the importance of understanding the interplay between different factors that influence energy consumer behavior, a research stream that has been under investigated thus far.