In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test ( 2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called ‘DeepCough’ . Two different versions of DeepCough based on the number of tensor dimensions, i.e., DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app ‘CoughDetect’ . Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of $98.80\% \pm 0.83\%$ , sensitivity of $96.43\% \pm 1.85\%$ , and specificity of $96.20\% \pm 1.74\%$ and average AUC of $81.08\% \pm 5.05\%$ for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

Background: The design and use of serious video games for children have increased in recent years. To maximize the effects of these games, it is essential to understand the children’s experiences through playing. Previous studies identified that enjoyment and user experience satisfaction of the players are principal factors that can influence the success of serious video games and the learning of their players. However, research about the relationship between enjoyment and user experience satisfaction with learning in children 8 to 10 years old is sparse. Objective: We examined the relationship of enjoyment and user experience satisfaction with the learning of children aged 8 to 10 years while playing a serious video game for health, FoodRateMaster. This serious video game teaches children about the characteristics of healthy and unhealthy foods and how to identify them in their environment. Methods: Children aged 8 to 10 years were recruited from a primary school in Mexico. Participants completed 12 individual gaming sessions with FoodRateMaster in 6 weeks. A food knowledge questionnaire was administered before and after game play to assess the players’ food knowledge. In addition, after the gaming sessions, the children’s enjoyment and user experience satisfaction were evaluated using the EGameFlow questionnaire and the Game User Experience Satisfaction Scale (GUESS) questionnaire. Results: We found significant positive associations for children’s (n=60) posttest knowledge with enjoyment (r58=0.36, P=.005) and user experience satisfaction (r58=0.27, P=.04). The children’s posttest knowledge scores were also positively correlated with challenge (r58=0.38, P=.003), knowledge improvement (r58=0.38, P=.003), and goal clarity (r58=0.29, P=.02) EGameFlow subscales and with narrative (r58=0.35, P=.006), creative freedom (r58=0.26, P=.04), and visual esthetics (r58=0.32, P=.01) GUESS subscales. Regression analysis indicated that the EGameFlow (F7,52=2.74, P=.02, R2=0.27) and the GUESS (F8,51=2.20, P=.04, R2=0.26) ratings significantly predicted the children’s posttest knowledge scores. EGameFlow challenge (β=0.40, t52=2.17, P=.04) and knowledge improvement (β=0.29, t52=2.06, P=.04) subscales significantly contributed to predicting children’s learning. None of the GUESS subscales significantly contributed to predicting children’s learning. Conclusions: The findings of this study suggest that both enjoyment and user experience satisfaction for children aged 8 to 10 years were positively correlated with their learning and that were significant predictors of it. Challenge, knowledge improvement, narrative, creative freedom, and visual esthetics subscales correlated positively with children’s learning. In addition, challenge and knowledge improvement contributed to predicting their learning. These results are relevant to consider during the design stages of serious games developed for young children’s learning purposes.

Background: The use of health games is a promising strategy for educating and promoting healthy lifestyle behaviors among children. Objective: We aimed to describe the design and development of a serious game, called HelperFriend, and evaluate its feasibility, acceptability, and preliminary effects in children in a pilot study. HelperFriend is a vicarious experiential video game designed to promote 3 lifestyle behaviors among young children: physical activity, healthy eating, and socioemotional wellness. Methods: Participants aged 8 to 11 years were recruited from an elementary school and randomized to receive a healthy lifestyle behavior educational talk (control) or play six 30-minute sessions with HelperFriend (intervention). Assessments were conducted at baseline (T0) and after the intervention (ie, 4 weeks) (T1). The primary outcome was gain in knowledge. The secondary outcomes were intention to conduct healthy behaviors, dietary intake, and player satisfaction. Results: Knowledge scores of intervention group participants increased from T0 to T1 for physical activity (t14=2.01, P=.03), healthy eating (t14=3.14, P=.003), and socioemotional wellness (t14=2.75, P=.008). In addition, from T0 to T1, the intervention group improved their intention to perform physical activity (t14=2.82, P=.006), healthy eating (t14=3.44, P=.002), and socioemotional wellness (t14=2.65, P=.009); and there was a reduction in their intake of 13 unhealthy foods. HelperFriend was well received by intervention group. Conclusions: HelperFriend appears to be feasible and acceptable for young children. In addition, this game seems to be a viable tool to help improve the knowledge, the intention to conduct healthy behaviors, and the dietary intake of children; however, a well-powered randomized controlled trial is needed to prove the efficacy of HelperFriend.