This paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. The ...algorithm aims to reduce data transmission and storage requirements, hurdles in the adoption of emotion recognition in these mediums. The early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research.
This paper presents a novel real‐time facial feature extraction algorithm, producing a small feature set, suitable for implementing emotion recognition with online game and metaverse avatars. Early results presented show a facial emotion recognition accuracy of up to 92% on one benchmark dataset, with an overall accuracy of 77.2% across a wide range of datasets, demonstrating the early promise of the research.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
With the metaverse here to stay, we are seeing ever more advances in capability. This includes the ability to incorporate real-time body and facial animation, so that personalised avatars can display ...our changing expressions, allowing those we meet to gauge our mood. Given this advance, the opportunity now exists to utilise emotion recognition from avatars. This positional paper explores the benefits of utilising emotion prediction within the metaverse.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper presents an introduction to the use of affective computing in at-risk educational environments, such as those that schools located in areas where there is armed conflict and for the ...safeguarding of children and at-risk adults more generally. This paper has discussed the improvement of the EigenFace based facial emotion recognition by continually streamlining the facial dataset used and its application in at-risk educational environments. One of these mimics the authors' EigenFaces library and appears to have better performance in poor lighting and poor camera situations, making it possibly better for drone use. It is therefore paramount that as the authors develop the system further that they keep each component separate, in case it is decided to utilise commercial libraries (e.g. Microsoft Project Oxford) for certain aspects. A structure such as VoisOver that allows for third party technologies to be plugged in would mean the authors' code can remain separate from that of third party plug-ins, namely specific algorithms for identifying the core 12 emotion sets the authors have devised in contexts that might not even have been considered yet. Such algorithms could work with the system described in this paper to make its operation in at-risk educational environments even more possible.
This systematic review and meta-analysis evaluated the validity of tests / markers of athletic readiness to predict physical performance in elite team and individual sport athletes. Ovid MEDLINE, ...Embase, Emcare, Scopus and SPORT Discus databases were searched from inception until 15 March 2023. Included articles examined physiological and psychological tests / markers of athletic readiness prior to a physical performance measure. 165 studies were included in the systematic review and 27 studies included in the meta-analysis. 20 markers / tests of athletic readiness were identified, of which five were meta-analysed. Countermovement jump (CMJ) jump height had a large correlation with improved 10m sprint speed / time (r = 0.69; p = .00), but not maximal velocity (r = 0.46; p = .57). Non-significant correlations were observed for peak power (r = 0.13; p = .87) and jump height (r = 0.70; p = .17) from squat jump, and 10m sprint speed / time. CMJ jump height (r = 0.38; p = .41) and salivary cortisol (r = -0.01; p = .99) did not correlate with total distance. Sub-maximal exercise heart rate (r = -0.65; p = .47) and heart rate variability (r = 0.66; p = .31) did not correlate with Yo-Yo Intermittent Recovery Test 1 performance. No correlation was observed between blood C-reactive protein and competition load (r = 0.33; p = .89). CMJ jump height can predict sprint and acceleration qualities in elite athletes. The validity of the other readiness tests / markers meta-analysed warrants further investigation.
