Research suggests that music has a powerful effect on the human mind and body. This paper explores the impact of music as an intervention. For this purpose, the X-System technology is used to curate relaxing and enlivening music playlists designed to positively impact wellbeing and emotional state during the COVID-19 pandemic. A wellbeing model grounded in autopoietic theory of self-organisation in living systems is developed to inform the evaluation of the impact of the intervention and ensure the reliability of the data. More specifically, data quality is enhanced by focusing the participants' awareness on their immediate embodied experience of physical, emotional and relational wellbeing and sense of pleasure/displeasure prior to and after listening to a preferred playlist. The statistical analysis shows significant positive changes in emotional wellbeing, valence and sense of meaning (p < 0.001) with a medium effect size. It also reveals a statistically significant change for physical wellbeing (p = 0.009) with a small effect size. With the relaxing playlists leading to decrease in arousal levels and the enlivening playlists to an increase in activation, it is also concluded that appropriately curated playlists may be able to lead the listener to positive relaxation or activation states or indeed to positive mood change that may have health benefits.
PurposeA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In our pilot study published in, Sakkos:SKIMA 2019, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly but also features semantic transformations of illumination which enhance the generalisation of the model.Design/methodology/approachIn our pilot study published in SKIMA 2019, the proposed framework successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. In this paper, we further enhance the data augmentation framework by proposing new variations in image appearance both locally and globally.FindingsExperimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place.Originality/valueSuch data augmentation allows us to effectively train an illumination-invariant deep learning model for BGS. We further propose a post-processing method that removes noise from the output binary map of segmentation, resulting in a cleaner, more accurate segmentation map that can generalise to multiple scenes of different conditions. We show that it is possible to train deep learning models even with very limited training samples. The source code of the project is made publicly available at https://github.com/dksakkos/illumination_augmentation
BackgroundThe detrimental impact of Covid-19 has led to an urgent need to support the wellbeing of UK National Health Service and care workers. This research develop an online diary to support the wellbeing of staff in public healthcare in real-time, allowing the exploration of population wellbeing and pro-active responses to issues identified. MethodsThe diary was co-produced by NHS and care stakeholders and university researchers. It was based on an integrative model of mental health and wellbeing. Diary users were encouraged to reflect on their experience confidentially, empowering them to monitor their wellbeing. The data collected was analysed using Mann-Whitney-Wilcoxon and Kruskal-Wallis statistical tests to determine any significant wellbeing trends and issues. ResultsA statistically significant decline in wellbeing (P<2.2E-16), and a significant increase in symptoms (P=1.2E-14) was observed. For example, indicators of post-traumatic stress, including, flashbacks, dissociation, and bodily symptoms (Kruskal-Wallis P=0.00081, 0.0083, and 0.027, respectively) became significantly worse and users reported issues with sleeping (51%), levels of alertness (46%), and burnout (41%). ConclusionsThe wellbeing diary demonstrated the value of population-based wellbeing data driven by an integrative model of wellbeing. It successfully demonstrated the capability to distinguish trends and wellbeing problems. Thus, informing how staff wellbeing services can determine and respond to need with timely interventions. The results particularly emphasised the pressing need for interventions that help staff with burnout, self-compassion, and flashbacks.
Background The detrimental impact of Covid-19 has led to an urgent need to support the wellbeing of UK National Health Service and care workers. This research develops an online diary to support the wellbeing of staff in public healthcare in real-time, allowing the exploration of population wellbeing and pro-active responses to issues identified. Methods The diary was co-produced by NHS and care stakeholders and university researchers. It was based on an integrative model monitoring mental health symptoms as well as wellbeing indicators. Diary users were encouraged to reflect on their experience confidentially, empowering them to monitor their wellbeing. The data collected was analysed using Mann-Whitney-Wilcoxon and Kruskal-Wallis statistical tests to determine any significant wellbeing trends and issues. Results A statistically significant decline in wellbeing (P < 2.2E-16), and a significant increase in symptoms (P = 1.2E-14) was observed. For example, indicators of post-traumatic stress, including, flashbacks, dissociation, and bodily symptoms (Kruskal-Wallis P = 0.00081, 0.0083, and 0.027, respectively) became significantly worse and users reported issues with sleeping (51%), levels of alertness (46%), and burnout (41%). Conclusions The wellbeing diary indicated the value of providing ways to distinguish trends and wellbeing problems, thus, informing how staff wellbeing services can determine and respond to need with timely interventions. The results particularly emphasised the pressing need for interventions that help staff with burnout, self-compassion, and intrusive memories.
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