Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

Saganowski, Stanisław; Dutkowiak, Anna; Dziadek, Adam; Dzieżyc, Maciej; Komoszyńska, Joanna; Michalska, Weronika; Polak, Adam G.; Ujma, Michał; Kazienko, Przemysław

doi:10.1109/percomworkshops48775.2020.9156096

Cited by 56 publications

(23 citation statements)

References 46 publications

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“…This is a common method of discretization of Russell’s arousal-valence model [ 8 ]. Table 3 describes which values of arousal/valence where considered as high/low.…”

Section: Methodsmentioning

confidence: 99%

“…One reason is that relations between the wearable sensor data and the human psychophysiological states are not as explicit as is the relation between the wearable sensor data and human physical states. For example, smartphones can count steps and recognize human physical activities (e.g., running vs. walking) [ 6 ] but cannot recognize emotions and related affective states (e.g., cognitive load) with high accuracy [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Dzieżyc

Gjoreski

Kazienko

et al. 2020

Sensors

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To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.

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“…This is a common method of discretization of Russell’s arousal-valence model [ 8 ]. Table 3 describes which values of arousal/valence where considered as high/low.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Dzieżyc

Gjoreski

Kazienko

et al. 2020

Sensors

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“…The K-EmoCon dataset authors provide the up-to-date overview [ 26 ]. Unfortunately, these datasets suffer from a small research sample [ 26 , 27 ]. In review [ 26 ], only the SEMAINE study [ 28 ] has more than 100 participants, while the others have an average of 30 (minimum seven, maximum 64).…”

Section: Emotions and Personality In Intelligent Systemsmentioning

confidence: 99%

Personality-Based Affective Adaptation Methods for Intelligent Systems

Kutt

Drążyk

Bobek

et al. 2020

Sensors

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In this article, we propose using personality assessment as a way to adapt affective intelligent systems. This psychologically-grounded mechanism will divide users into groups that differ in their reactions to affective stimuli for which the behaviour of the system can be adjusted. In order to verify the hypotheses, we conducted an experiment on 206 people, which consisted of two proof-of-concept demonstrations: a “classical” stimuli presentation part, and affective games that provide a rich and controllable environment for complex emotional stimuli. Several significant links between personality traits and the psychophysiological signals (electrocardiogram (ECG), galvanic skin response (GSR)), which were gathered while using the BITalino (r)evolution kit platform, as well as between personality traits and reactions to complex stimulus environment, are promising results that indicate the potential of the proposed adaptation mechanism.

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“…Additionally, good practices in work with SSL models in treating the WESAD data from [12,14] will be used as a starting point for developing a novel SSL methodology. A review of exploited methods for collecting, processing, and evaluating data collected by wearables (smartwatches and bands) is presented in [16]. It provides useful insights into techniques for intelligent algorithms' practical applicability while operating with wearable sensing equipment.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

Alshamrani¹

2021

IJACSA

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Today's busy lifestyle often leads to frequent stress, the accumulation of which may lead to severe consequences for humans. Smartwatches are widely distributed and accessible, and as such deserve intelligent solutions that deal with the processing of such collected data and ensuring the improvement of the quality of life of end-users. The goal of this research is to create a stress detection technology that can correctly, constantly, and unobtrusively monitor psychological stress in real time. Due to the importance of stress detection and prevention, many traditional and advanced techniques have been proposed likewise we provide a unique stress-detection technique that is contextbased. Due to the importance of stress detection and prevention, many traditional and advanced techniques have been proposed. In this research, a novel approach to designing and using a deep neural network for stress detection is presented. To provide a desirable training environment for network development, an open-source data set based on motion and physiological information collected from wrist and chest-worn devices was acquired and exploited. Raw data were analyzed, filtered, and preprocessed to create the best possible training data. For the proposed solution to have wide use value, further focus was placed on the data recorded using only smartwatches. Smartwatches are widely distributed and accessible, and as such deserve intelligent solutions that deals with the processing of such collected data and ensuring the improvement of the quality of life of end-users. Finally, two network types with proven capabilities of processing time series data are examined in detail: a fully convolutional network (FCN) and a ResNet deep learning model. The FCN model showed better empirical performances, and further efforts were made to select an optimal network structure. In the end, the proposed solution demonstrated performance similar to state-of-the-art solutions and significantly better than some traditional machine learning techniques, providing a good foundation for reliable stress detection and further development efforts.

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Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

Cited by 56 publications

References 46 publications

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data

Personality-Based Affective Adaptation Methods for Intelligent Systems

An Advanced Stress Detection Approach based on Processing Data from Wearable Wrist Devices

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