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To detect multimodal emotions using Virtual Reality (VR), this research demonstrates the findings and results of using a KNN Classifier by merging Heart Rate and Electrodermography signals. The participants in the study were shown 360-degree videos using a VR headset to elicit their emotional reactions. A wearable that measures skin activity and pulse rate in real time was used to record their emotional response. The experiment had a total of 30 participants, and the KNN classifier was used to classify intra-subject data. With the HR combined with EDG signals paired with KNN as the classifier, the study’s 30 participants’ data went through intra-subject classification where 11 out of 30 participants achieved a peak accuracy of 100%. These findings show that by combining HR and EDG signals, KNN may be used as the classifier to produce highly accurate results. This study’s possible applications include VR rehabilitation, gaming, and entertainment.

This extensive literature review examines virtual reality (VR) and emotional responses, including theories, paradigms, and research gaps. Foundational to VR is its ability to evoke profound emotional involvement through heightened presence. The study synthesizes presence theory, appraisal theory, and transportation theory to explain how they affect virtual emotional experiences. It also examines affective computing and multisensory integration frameworks for emotional design for VR and their importance in creating emotionally engaging VR experiences. Furthermore, the study highlights key research gaps and issues in the field. These include individual variances in emotional responses, the undiscovered long-term effects of repeated VR exposure, and ethical issues surrounding emotion manipulation and user permission. To fill these gaps, computer science, psychology, and allied sciences should work together to comprehend the complex relationship between VR and human emotions. The ethical, theoretical, and practical aspects of VR’s emotional landscape are explored in this synthesis to guide future research.

Emotion identification has received a lot of interest in recent years, with applications in mental health, education and marketing. This systematic literature review aimed to provide an up-to-date overview of trends and technological advancements in the use of media stimuli for emotion recognition. A comprehensive search yielded 720 relevant studies from 2018 to 2023, which employed various media stimuli to induce and measure emotional responses. The main findings indicate that audios and videos are the most used media stimuli for emotion recognition. However, there is a growing trend toward exploring other forms of media, such as physiological signals and wearables. This review highlights the varying ecological validity of different stimulus types and emphasizes the potential of virtual reality for more objective emotion recognition. These findings offer valuable insights for future research and practical applications in the field by synthesizing knowledge to inform advancements in media stimuli for emotion recognition. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the proposed scheme utilises a well-known technique, i.e., Kruskal’s algorithm, to find an optimal path from source to destination wearable devices. Additionally, other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties. Apart from that, every device is bound to hold a supplementary path, preferably a second optimised path, for situations where the current communication path is no longer available, either due to device failure or heavy traffic. Furthermore, the machine learning approach helps enable these devices to update their routing tables simultaneously, and an optimal path could be replaced if a better one is available. The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks. Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios, i.e., 17% and 23%, in terms of end to end delay and packet losses, respectively. Moreover, the proposed scheme has achieved an approximately 21% improvement in the average throughput compared to the existing schemes. [ABSTRACT FROM AUTHOR]

Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices makes it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an offline phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-b secret key, $\lambda _{i}$. Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined timeframe, $i.e., \bigtriangleup T$. Moreover, authenticity of the requesting device is verified through the stored IDs, which are shared in the offline phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs. [ABSTRACT FROM AUTHOR]