Emotion Charting Using Real-time Monitoring of Physiological Signals

Rahim, Aqsa; Sagheer, Amna; Nadeem, Khunsha; Dar, Muhammad Najam; Rahim, Amna; Akram, Usman

doi:10.1109/icrai47710.2019.8967398

Cited by 7 publications

(14 citation statements)

References 8 publications

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“…Experimental results show the superiority of 1D to 2D pre-processing. In [ 89 ], ECG and GSR are converted in 2D scalogram for better emotion recognition. Furthermore, in [ 90 ], ECG and GSR signals are converted in 2D RP images for improved emotion recognition as compared to 1D form signals.…”

Section: Data Pre-processingmentioning

confidence: 99%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

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Section: Data Pre-processingmentioning

confidence: 99%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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“…Experimental results show the superiority of 1D to 2D preprocessing. In [79], ECG and GSR are converted in 2D scalogram for better emotion recognition. Furthermore, in [80], ECG and GSR signals are converted in 2D RP images for improved emotion recognition as compared to 1D form signals.…”

Section: D To 2d Conversionmentioning

confidence: 99%

A Survey on Physiological Signal Based Emotion Recognition

Ahmad¹,

Khan²

2022

Preprint

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Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.

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“…Physiological sensors are commonly present in wearables with embedded technologies, the most common being heart rate-based readers such as: Electrocardiogram (ECG), Heart-rate and Heart-rate-variability (HR, HRV), or Electrodermal activity (EDA), Skin conductance level (SCR), and Skin conductance resistance (SCL) (Brady et al, 2016) (Rahim et al, 2019) (Lazar, 2017). This type of data capture sends signals as numerical list data indicating timestamps and untreated values of the biological reading itself.…”

Section: S3 System Proposalmentioning

confidence: 99%

“…Brady et al (Brady et al, 2016) use an adapted CNN for Emotion Prediction based on physiological data (Wang et al, 2015). Rahim et al (Rahim et al, 2019) present a method for emotion recognition with CNN using HR and GSR sensor signals through a scalogram of images generated by physiological inputs, resulting in a good classification rate. In general, these algorithms work by identifying similar sequences and labeling them, updating the hypothesis of Euclidean variation (timestamp and biological reading), classifying the numerical sequence pattern until defining a set of examples.…”

Section: S3 System Proposalmentioning

confidence: 99%

Design of Socioenactive Systems Based on Physiological Sensors and Robot Behavior in Educational Environments

Gonçalves

Caceffo

Valente

et al. 2021

RBIE

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Computational systems based on ubiquitous and pervasive technology present several challenges related to the interaction of people with scenarios constituted by sensors and actuators, changing the mindset of what we used to understand as interaction with a computer. This also has influence in the ways of considering the design of systems based on contemporary technology for the educational context. To cope with the challenges of ubiquitous computing, the concept of socioenactive system is being constructed as a system in which human and technological aspects are coupled together in a cycle of perceptually guided actions of people interacting with elements of the physical environment and with other people in the same scenario. In this work we address the design of a socioenactive system as an evolution of two previous systems designed and experimented with 5-year-old children in an educational context. The contribution of this paper is twofold: 1. We present an analysis of two different systems tested in educational scenarios, pointing out the lack of elements that should be present in a complete cycle of socioenactive systems, suggesting requirements for a third system; 2. We present an architecture for the third system and a simulation of its usage. Results of the third system and its simulation inform the next activities of bringing it to real life in a practice proposed for the same audience and context as the previous systems.

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Emotion Charting Using Real-time Monitoring of Physiological Signals

Cited by 7 publications

References 8 publications

A Survey on Physiological Signal-Based Emotion Recognition

A Survey on Physiological Signal-Based Emotion Recognition

A Survey on Physiological Signal Based Emotion Recognition

Design of Socioenactive Systems Based on Physiological Sensors and Robot Behavior in Educational Environments

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