Emotion Based Music Recommendation System Using Wearable Physiological Sensors

Ayata, Değer; Yaslan, Yusuf; Kamasak, Mustafa E.

doi:10.1109/tce.2018.2844736

Cited by 215 publications

(106 citation statements)

References 14 publications

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“…In our first study on this topic, we investigated emotion recognition only from GSR signals [13,14]. Later, we used PPG and GSR signals together and proposed a data fusion based emotion recognition method for music recommendation engines [15]. This wearable music recommendation framework utilizes not only the user's demographics but also his/her emotion state at the time of recommendation.…”

Section: Introductionmentioning

confidence: 99%

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Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

2020

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The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems. In this work, physiological signals are collected from a respiratory belt (RB), photoplethysmography (PPG), and fingertip temperature (FTT) sensors. These signals are used as their collection becomes easy with the advance in ergonomic wearable technologies. Methods Arousal and valence levels are recognized from the fused physiological signals using the relationship between physiological signals and emotions. This recognition is performed using various machine learning methods such as random forest, support vector machine and logistic regression. The performance of these methods is studied. Results Using decision level fusion, the accuracy improved from 69.86 to 73.08% for arousal, and from 69.53 to 72.18% for valence. Results indicate that using multiple sources of physiological signals and their fusion increases the accuracy rate of emotion recognition. Conclusion This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature. It is shown that decision level fusion from multiple classifiers (one per signal source) improved the accuracy rate of emotion recognition both for arousal and valence dimensions.

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Section: Introductionmentioning

confidence: 99%

“…In the mentioned previous studies, the combination of RB, PPG, and FTT signals has never been used together in a similar framework. Also, we have used decision level fusion as opposed to feature level fusion [15].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

2020

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“…There are multiple studies for emotion recognition while using videos, images, or music as an emotional stimulus [37][38][39]. Moreover, a recent survey showed that many studies that use machine leaning to detect stress and anxiety are creating stress stimuli through mental tasks in a laboratory setup [40].…”

Section: Vret As Stimulimentioning

confidence: 99%

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

et al. 2019

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Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.

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“…These four areas are interwoven because each parameter in a domain might be influenced by the parameter(s) in the other domains; thus, extensive data measurement, collection, fusion, and integration are necessary to calculate the mutual impact among different parameters and to assign a weight to each. This means that adequate decision-making and identification of a medical diagnosis are the functions of proper algorithm development, which require effective data, collection, fusion, and integration, which are impacted by continuous monitoring [30].…”

Section: Definition Of Wearables Applications and Our Contributionsmentioning

confidence: 99%

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

Haghi

Deserno

2020

Chemosensors

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We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated.

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Emotion Based Music Recommendation System Using Wearable Physiological Sensors

Cited by 215 publications

References 14 publications

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

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