Development and Progress in Sensors and Technologies for Human Emotion Recognition.
TL;DR: In this article, the authors present the development and progress in sensors and technologies to detect human emotions and discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency and decentralization.
Abstract: With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.
Citations
More filters
TL;DR: Wang et al. as mentioned in this paper used the alternative approach of facial reading, detecting the facial expressions of urban forest visitors and their relationships with the landscape metrics, and collected facial photos of 2031 people visiting 18 different forest parks across Northern China in 2020.
Abstract: Urban forests are an important green infrastructure that positively impacts human well-being by improving emotions and reducing psychological stress. Questionnaires have been used frequently to study the influence of forest experiences on mental health; however, they have poor controllability and low accuracy for detecting immediate emotions. This study used the alternative approach of facial reading, detecting the facial expressions of urban forest visitors and their relationships with the landscape metrics. Using the microblogging site, Sina Weibo, we collected facial photos of 2031 people visiting 18 different forest parks across Northern China in 2020. We used satellite imagery analysis to assess the elevation and pattern sizes of green space and blue space areas. Age and location were taken as independent variables affecting facial expressions, which were categorized as happy or sad. With increases in green space and intact park areas, people showed a higher frequency of expressing happy scores. The results showed that the forest experience frequently elicited positive emotions, suggesting that creating and maintaining urban green spaces enhance people’s quality of life.
19 citations
TL;DR: The SA-Oriented design (SAOD) process is described as a systematic methodology for developing transparent AI displays for HAT and an example of its application to automated driving in a Tesla is provided as mentioned in this paper .
9 citations
TL;DR: In this article, a multilayer WSN integration and the required algorithms for the closed-loop control of the irrigation system using IoT is presented, where a low-power and long-range sensor node is developed due to the lack of cellular data coverage in rural areas.
Abstract: Water, one of the most valuable resources, is underutilized in irrigated rice production. The yield of rice, a staple food across the world, is highly dependent on having proper irrigation systems. Alternate wetting and drying (AWD) is an effective irrigation method mainly used for irrigated rice production. However, unattended, manual, small-scale, and discrete implementations cannot achieve the maximum benefit of AWD. Automation of large-scale (over 1000 acres) implementation of AWD can be carried out using wide-area wireless sensor network (WSN). An automated AWD system requires three different WSNs: one for water level and environmental monitoring, one for monitoring of the irrigation system, and another for controlling the irrigation system. Integration of these three different WSNs requires proper dimensioning of the AWD edge elements (sensor and actuator nodes) to reduce the deployment cost and make it scalable. Besides field-level monitoring, the integration of external control parameters, such as real-time weather forecasts, plant physiological data, and input from farmers, can further enhance the performance of the automated AWD system. Internet of Things (IoT) can be used to interface the WSNs with external data sources. This research focuses on the dimensioning of the AWD system for the multilayer WSN integration and the required algorithms for the closed loop control of the irrigation system using IoT. Implementation of the AWD for 25,000 acres is shown as a possible use case. Plastic pipes are proposed as the means to transport and control proper distribution of water in the field, which significantly helps to reduce conveyance loss. This system utilizes 250 pumps, grouped into 10 clusters, to ensure equal water distribution amongst the users (field owners) in the wide area. The proposed automation algorithm handles the complexity of maintaining proper water pressure throughout the pipe network, scheduling the pump, and controlling the water outlets. Mathematical models are presented for proper dimensioning of the AWD. A low-power and long-range sensor node is developed due to the lack of cellular data coverage in rural areas, and its functionality is tested using an IoT platform for small-scale field trials.
7 citations
TL;DR: An Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry is developed and confirmed the usefulness of the proposed classification.
Abstract: Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).
7 citations
TL;DR: In this paper , the authors examine shopper response to beacon-triggered promotions and propose a model that would help retail practitioners plan the implementation of beacons in stores via an in-market test to examine the effects of beacon-driven promotion on shopper attention, technology acceptance, and the decision to purchase.
Abstract: This paper studies shopper acceptance for using beacons in the purchase process. The main goal is to examine shopper response to beacon-triggered promotions and propose a model that would help retail practitioners plan the implementation of beacons in stores. The model was evaluated via an in-market test to examine the effects of beacon-triggered promotion on shopper attention, technology acceptance, and the decision to purchase. The test was conducted in Belgrade, Serbia in 10 representative stores where beacons were implemented with 10 twin control stores. The SimplyTastly mobile application was used for sending notifications. Furthermore, two more in-market beacon activations were analysed in Croatia and Bulgaria. The results showed that shoppers accepted beacon technology and that beacon-triggered promotion had a positive impact on shopper attention, purchase behaviour, and the decision to purchase. The results show that the proposed model could serve as a sound basis for the implementation of beacon technology in retail.
6 citations
References
More filters
TL;DR: This survey is directed to those who want to approach this complex discipline and contribute to its development, and finds that still major issues shall be faced by the research community.
12,539 citations
TL;DR: A new model is advanced to describe the form and function of a subset of positive emotions, including joy, interest, contentment, and love, that serve to broaden an individual's momentary thought–action repertoire, which in turn has the effect of building that individual's physical, intellectual, and social resources.
Abstract: This article opens by noting that positive emotions do not fit existing models of emotions. Consequently, a new model is advanced to describe the form and function of a subset of positive emotions, including joy, interest, contentment, and love. This new model posits that these positive emotions serve to broaden an individual's momentary thought-action repertoire, which in turn has the effect of building that individual's physical, intellectual, and social resources. Empirical evidence to support this broadenand-build model of positive emotions is reviewed, and implications for emotion regulation and health promotion are discussed. Even though research on emotions has this new perspective are featured. My hope is flourished in recent years, investigations that that this article will unlock scientific curiosity expressly target positive emotions remain few and far between. Any review of the psychological literature on emotions will show that psychologists have typically favored negative emotions in theory building and hypothesis testing. In so doing, psychologists have inadvertently marginalized the emotions, such as joy, about positive emotions, not only to test the ideas presented here, but also to build other new models that might illuminate the nature and value of positive emotions. Psychology sorely needs more studies on positive emotions, not simply to level the uneven knowledge bases between negative and positive emotions, but interest, contentment, and love, that share a more critically, to guide applications and pleasant subjective feel. To date, then, psychology's knowledge base regarding positive emotions is so thin that satisfying answers to the question "What good are positive emotions?" have yet to be articulated. This is unfortunate. Experiences of positive emotion are central to human nature and contribute richly to the quality of people's lives (Diener & Larsen,
5,198 citations
TL;DR: It is found that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projections and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.
Abstract: The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize the human affective state given four physiological signals. We describe difficult issues unique to obtaining reliable affective data and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-to-day variations: The features of different emotions on the same day tend to cluster more tightly than do the features of the same emotion on different days. To handle the daily variations, we propose new features and algorithms and compare their performance. We find that the technique of seeding a Fisher Projection with the results of sequential floating forward search improves the performance of the Fisher Projection and provides the highest recognition rates reported to date for classification of affect from physiology: 81 percent recognition accuracy on eight classes of emotion, including neutral.
2,172 citations
TL;DR: The capability of the human visual system with respect to these problems is discussed, and it is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
Abstract: Humans detect and interpret faces and facial expressions in a scene with little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categories). A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. The paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
1,872 citations
TL;DR: A meta-analysis examined emotion recognition within and across cultures, finding emotions were universally recognized at better-than-chance levels and cross-cultural accuracy was lower in studies that used a balanced research design, and higher in Studies that used imitation rather than posed or spontaneous emotional expressions.
Abstract: A meta-analysis examined emotion recognition within and across cultures. Emotions were universally recognized at better-than-chance levels. Accuracy was higher when emotions were both expressed and recognized by members of the same national, ethnic, or regional group, suggesting an in-group advantage. This advantage was smaller for cultural groups with greater exposure to one another, measured in terms of living in the same nation, physical proximity, and telephone communication. Majority group members were poorer at judging minority group members than the reverse. Cross-cultural accuracy was lower in studies that used a balanced research design, and higher in studies that used imitation rather than posed or spontaneous emotional expressions. Attributes of study design appeared not to moderate the size of the in-group advantage.
1,629 citations