In recent years, emotion recognition has received significant attention, presenting a plethora of opportunities for application in diverse fields such as human–computer interaction, psychology, and neuroscience, to name a few. Although unimodal emotion recognition methods offer certain benefits, they have limited ability to encompass the full spectrum of human emotional expression. In contrast, Multimodal Emotion Recognition (MER) delivers a more holistic and detailed insight into an individual's emotional state. However, existing multimodal data collection approaches utilizing contact-based devices hinder the effective deployment of this technology. We address this issue by examining the potential of contactless data collection techniques for MER. In our tertiary review study, we highlight the unaddressed gaps in the existing body of literature on MER. Through our rigorous analysis of MER studies, we identify the modalities, specific cues, open datasets with contactless cues, and unique modality combinations. This further leads us to the formulation of a comparative schema for mapping the MER requirements of a given scenario to a specific modality combination. Subsequently, we discuss the implementation of Contactless Multimodal Emotion Recognition (CMER) systems in diverse use cases with the help of the comparative schema which serves as an evaluation blueprint. Furthermore, this paper also explores ethical and privacy considerations concerning the employment of contactless MER and proposes the key principles for addressing ethical and privacy concerns. The paper further investigates the current challenges and future prospects in the field, offering recommendations for future research and development in CMER. Our study serves as a resource for researchers and practitioners in the field of emotion recognition, as well as those intrigued by the broader outcomes of this rapidly progressing technology.

The technological advancements in wireless communication and miniaturization of sensor nodes have resulted in the development of Wireless Medical Sensor Networks (WMSNs) which can be effectively used for remote patient monitoring. Remote patient monitoring is one such application of wireless sensor networks which is becoming increasingly prevalent in healthcare. The healthcare applications of WMSNs are delay-sensitive and require timely delivery of patient-critical data. However, the frequent exchange of critical data packets results in higher delays, collisions, packet drop and retransmissions. Consequently, it brings a detrimental impact on the performance of WMSNs. In addition, the implanted biomedical sensor nodes produce electromagnetic radiations, pose a serious threat of damaging sensitive tissues in the human body. Protecting tissue damage requires thermal-aware routing protocols. However, most of the thermal-aware routing protocols developed for WBSNs primarily focused on minimizing temperature, while overlooking the energy conservation goal and optimization of route selection. In this paper, we propose a weighted, QoS-based, energy and temperature-aware routing protocol, referred to as (WETRP), for WMSNs that utilizes a composite routing metric by keeping in view temperature, remaining node energy, and link-delay estimation during route selection decisions. The simulation results presented in the paper demonstrates the efficacy of the proposed scheme in terms of preventing temperature rise, dealing with hotspot nodes, and maximizing network’s lifetime.

Wireless Body Sensor Networks (WBSNs) are becoming increasing popular in a number of healthcare applications. A particular requirement of WBSNs in a healthcare system is the transmission of time-sensitive and critical data, captured by heterogeneous biosensors, to a base station while considering the constraints of reliability, throughput, delay and link quality. However, the simultaneous communication among various biosensors also raises the possibility of congestion on nodes or transmission links. Consequently, the likelihood of a number of untoward situations increases, such as disruption (high delays), packet losses, retransmissions, bandwidth exhaustion, and insufficient buffer space. The significant level of interference in the network leads to a higher number of collisions and retransmissions. The selection of an optimized route to cope with these issues and satisfy the QoS requirements of a WBSN has not been well-studied in the relevant literature. In this regard, we propose a multi-constraint, Intra-BAN, QoS-Aware Routing Protocol (referred to as MIQoS-RP) which introduces an improved, multi-facet routing metric to optimize the route selection while satisfying the aforementioned constraints. The performance of the proposed protocol is evaluated in terms of average end-to-end delay, throughput and packet drop ratio. The comparison of MIQoS-RP with the existing routing protocols demonstrates its efficacy in terms of the selected criteria. The results show that the MIQoS-RP achieves improved throughput by 22%, average end-to-end delay by 29% and packet drop ratio performance by 41% as compares to existing schemes.

The rising integration of AI-driven assessment in education holds promise, yet it is crucial to evaluate the correlation between trust in general AI tools, AI-based scoring systems, and future behavioral intention toward using these technologies. This study explores students’ perspectives on AI-assisted assessment in higher education. We constructed a comprehensive questionnaire supported by relevant studies. Several hypotheses grounded in the literature review were formulated. In an experimental setup, the students were tasked to read a designated chapter of a paper, answer an essay question about this chapter, and then have their answers evaluated by an AI-based essay grading tool. A comprehensive data analysis using Bayesian regression was carried out to test several hypotheses. The study finds that remote learners are more inclined to use AI-based educational tools. The students who believe that AI-based essay grading is less effective than teacher feedback have less trust in AI-based essay grading, whereas those who find it more effective perceive more benefit from it. In addition, students’ affinity for technology does not significantly impact trust or perceived benefits in AI-based essay grading.

The sheer volume of movies generated these days requires an automated analytics for efficient classification, query-based search, and extraction of desired information. These tasks can only be efficiently performed by a machine learning based algorithm. We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags. We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model. Subsequently, we propose an efficient shot detection algorithm to find the key frames in the movie. The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame. The tags are then assigned weighted scores and are filtered to generate a compact set of most relevant tags. This process also generates a corpus which is further used to segment a movie based on a selected tag. We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation. Our detailed experiments demonstrate that the proposed technique is not only efficacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy.

In this paper, we propose a technique to recognize multiple actions in a video using deep learning. The proposed approach is concerned with interpreting the overall context of a video and transforming it into one or more appropriate actions. In order to cope with multiple actions in a video, our proposed technique first determines the individual segments/shots in a video using intersections of color histograms. The segmented parts are then fed to the action recognition system comprising a combination of a Convolution Neural Network (CNN) and a Long-Short-Term Memory (LSTM) network trained on our action vocabulary. The segments are then labeled according to their predicted actions and a compact set of distinct actions is produced. Using the corpus generated by the shot detection phase, which includes the location of keyframes in shots, and start/end timestamps of a shot, we can also perform video segmentation based on an action query. Hence, the proposed technique can be used for a number of tasks such as content censoring, on-demand scene retrieval, video summarizing, and query-based scene/video retrieval, to name a few. The proposed technique also stands apart from the existing approaches which either do not take into account the motion information for action prediction or do not perform action-based video segmentation. The experimental results presented in this paper show that the proposed technique not only finds the complete set of actions present in the video but can also find all the relevant parts in a video based on an action query.