Biosignals for Everyone

Silva, Hugo Plácido da; Fred, Ana; Martins, Ruben

doi:10.1109/mprv.2014.61

Cited by 65 publications

(30 citation statements)

References 7 publications

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“…In the scope of our work we used five publicly available datasets for ER, commonly used in previous work for benchmarking: IT Multimodal Dataset for Emotion Recognition (ITMDER) [ 7 ]: contains the physiological signals of interest to our work (EDA, RESP, ECG, and BVP) of 18 individuals using two devices based on the BITalino system [ 50 , 51 ] (one placed on the arm and the other on the chest of the participants), collected while the subjects watched seven VR videos to elicit the emotions: Boredom, Joyfulness, Panic/Fear, Interest, Anger, Sadness and Relaxation. The ground-truth annotations were obtained by the subjects self-report per video using the Self-Assessment Manikin (SAM), in the Valence-Arousal space.…”

Section: Resultsmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

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Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

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Section: Resultsmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

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“…Therefore, arousal or relaxation of the volunteer can be detected via the skin conductivity [ 14 ]. The hardware was the BITalino [ 15 ] (PLUX wireless biosignals S.A., Portugal). The analog signals of the sensors were converted to digital signals (10 bits) and sent to the central computer.…”

Section: Methodsmentioning

confidence: 99%

Unconscious physiological response of healthy volunteers to dynamic respiration-synchronized couch motion

et al. 2017

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BackgroundIntrafractional motion can be a substantial uncertainty in precision radiotherapy. Conventionally, the target volume is expanded to account for the motion. Couch-tracking is an alternative, where the patient is moved to compensate for the tumor motion. However, the couch motion may influence the patient’s stress and respiration behavior decreasing the couch-tracking effectiveness.MethodsIn total, 100 volunteers were positioned supine on a robotic couch, which moved dynamically and respiration synchronized. During the measurement, the skin conductivity, the heartrate, and the gaze location were measured indicating the volunteer’s stress. Volunteers rated the subjective motion sickness using a questionnaire. The measurement alternated between static and tracking segments (three cycles), each 1 min long.ResultsThe respiration amplitude showed no significant difference between tracking and static segments, but decreased significantly from the first to the last tracking segment (p < 0.0001). The respiration frequency differed significantly between tracking and static segments (p < 0.0001), but not between the first and the last tracking segment. The physiological parameters and the questionnaire showed mild signals of stress and motion sickness.ConclusionGenerally, people tolerated the couch motions. The interaction between couch motion and the patient’s breathing pattern should be considered for a clinical implementation.Trial registrationThe study was registered at ClinicalTrials.gov (NCT02820532) and the Swiss national clinical trials portal (SNCTP000001878) on June 20, 2016.Electronic supplementary materialThe online version of this article (10.1186/s13014-017-0925-6) contains supplementary material, which is available to authorized users.

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“…The most common means to detect muscle tension is by way of the electromyogram (EMG) signal, a series of microvolt electrical impulses generated by the nervous system to command and cause muscle contraction. Technologies of interfacing to the human body via the EMG signal have emerged from the biomedical sphere to be widely available today in the DIY community with systems like the Bitalino, 4 and even in consumer products for multimodal, hands free interaction (da Silva et al 2014). These products build upon interaction research with the EMG (Saponas et al 2010) that sought to make such signals from the body practical for applications in HCI.…”

Section: Biomusementioning

confidence: 99%

Embodied Musical Interaction

Tanaka

2019

New Directions in Music and Human-Computer Interaction

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Music is a natural partner to human-computer interaction, offering tasks and use cases for novel forms of interaction. The richness of the relationship between a performer and their instrument in expressive musical performance can provide valuable insight to human-computer interaction (HCI) researchers interested in applying these forms of deep interaction to other fields. Despite the longstanding connection between music and HCI, it is not an automatic one, and its history arguably points to as many differences as it does overlaps. Music research and HCI research both encompass broad issues, and utilize a wide range of methods. In this chapter I discuss how the concept of embodied interaction can be one way to think about music interaction. I propose how the three "paradigms" of HCI and three design accounts from the interaction design literature can serve as a lens through which to consider types of music HCI. I use this conceptual framework to discuss three different musical projects-Haptic Wave, Form Follows Sound, and BioMuse.

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Biosignals for Everyone

Cited by 65 publications

References 7 publications

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Unconscious physiological response of healthy volunteers to dynamic respiration-synchronized couch motion

Embodied Musical Interaction

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