Physiological mouse: toward an emotion-aware mouse

Fu, Eugene Yujun; Leong, Hong Va; Ngai, Grace; Huang, Michael Xuelin; Chan, Sewin

doi:10.1007/s10209-016-0469-9

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…They also suggested that music could be used to relieve psychological pressure. Fu et al [55] presented an approach using the human heart rate as a new form of modality to determine human emotions with a physiological mouse measuring photoplethysmographic data and experimentally evaluated the accuracy of the approach. Hu and Li [56] collected 140 signal samples of ECG triggered by Self-Assessment Manikin emotion self-assessment experiments using the International Affective Picture System, and used Wasserstein generative adversarial network with gradient penalty to add different numbers of samples for different classes.…”

Section: Research and Application Of Eda Ecg And Emgmentioning

confidence: 99%

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Lin

2023

Applied Sciences

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People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation.

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Section: Research and Application Of Eda Ecg And Emgmentioning

confidence: 99%

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Lin

2023

Applied Sciences

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“…Emotional intelligence systems are AI tools designed to recognize, interpret, and respond to human emotions (Bilquise et al, 2022). Through several means including NLP, sentiment analysis, interpretation of facial expression, tone of voice, or body language, AI systems are increasingly able to interpret and adapt to delivering communications that best fits emotional states (Fu et al, 2017; Gil et al, 2015; Yadegaridehkordi et al, 2019). As AI is increasingly able to effectively deal with human emotions it can help with both excitement and sorrow (Hughes et al, 2019).…”

Section: Ai Domains Most Relevant To Pidmentioning

confidence: 99%

Artificial Intelligence and Pay Information Disclosure: Changing How Pay is Communicated

Nyberg

Cragun

Conroy

2023

Compensation & Benefits Review

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Pay information disclosure (PID), or communicating pay information between and among actors, affects employees, organizations, and societies. Disruptions resulting from artificial intelligence (AI) will also change how pay is communicated. Based on AI, AI and human resources (HR), and PID, as well as anecdotal data involving organizations that are integrating AI in their pay practices, we introduce areas of AI relevant to PID and describe opportunities and challenges. HR should play a critical role in developing employee trust in AI systems by protecting employee privacy, training AI on high-quality data, and ensuring AI algorithms are ethical. AI can transform PID by supporting advanced pay methodologies, reducing barriers to sharing information, and educating employees. However, research needs to be conducted on all of these areas and practitioners should strive to keep an open, but cautious mind about both the benefits and challenges of integrating AI into HR practices.

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“…Biometric sensors provide essential data that can be used to implement various solutions for recognizing users’ physiological and psychological states during the interaction, which can be used in various HCI scenarios. Emotion-detection can be implemented, for example, by processing data from electroencephalography (ECG) sensor [ 86 ], galvanic skin response (GSR) sensor [ 86 ], electromyographic (EMG) sensor [ 86 ], photoplethysmography (PPG) sensor [ 86 , 87 , 88 ], multi-biological sensor (e.g., PolyG-I (LAXTHA Inc., Daejeon, Korea) [ 30 ], BIOPAC MP150 [ 89 ]) providing different physical signals including EEG, ECG, EMG, PPG, GSR, and respiration (RESP). Biometric sensors were also successfully applied for implementing hand gesture-recognition solutions based on the eEMG sensor (e.g., [ 54 , 85 ]) and solutions for human-health monitoring [ 70 ].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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Physiological mouse: toward an emotion-aware mouse

Cited by 11 publications

References 22 publications

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Artificial Intelligence and Pay Information Disclosure: Changing How Pay is Communicated

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

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