Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities

Pietilä, Julia; Mehrang, Saeed; Tolonen, Johanna; Helander, Elina; Jimison, Holly; Pavel, Misha; Korhonen, Ilkka

doi:10.1007/978-981-10-5122-7_37

Cited by 46 publications

(37 citation statements)

References 4 publications

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“…The accuracy of the E4 wristband has been assessed by only a few preliminary studies, mainly focused on stress and emotion recognition (e.g., Ragot, Martin, Em, Pallamin, & Diverrez, 2017). Two studies focused on the PPG signal and suggested satisfactory data quality (McCarthy, Pradhan, Redpath, & Adler, 2016) and acceptable measurement error in mean HR and some HRV measures (Pietilä et al, 2017). However, high data quality was achieved only in conditions involving no hand movements, whereas motion artifacts represent exactly the kind of error an ambulatory sensor is expected to manage.…”

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confidence: 99%

Stressing the accuracy: Wrist‐worn wearable sensor validation over different conditions

et al. 2019

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Wearable sensors are promising instruments for conducting both laboratory and ambulatory research in psychophysiology. However, scholars should be aware of their measurement error and the conditions in which accuracy is achieved. This study aimed to assess the accuracy of a wearable sensor designed for research purposes, the E4 wristband (Empatica, Milan, Italy), in measuring heart rate (HR), heart rate variability (HRV), and skin conductance (SC) over five laboratory conditions widely used in stress reactivity research (seated rest, paced breathing, orthostatic, Stroop, speech task) and two ecological conditions (slow walking, keyboard typing). Forty healthy participants concurrently wore the wristband and two gold standard measurement systems (i.e., electrocardiography and finger SC sensor). The wristband accuracy was determined by evaluating the signal quality and the correlations with and the Bland‐Altman plots against gold standard‐derived measurements. Moreover, exploratory analyses were performed to assess predictors of measurement error. Mean HR measures showed the best accuracy over all conditions. HRV measures showed satisfactory accuracy in seated rest, paced breathing, and recovery conditions but not in dynamic conditions, including speaking. Accuracy was diminished by wrist movements, cognitive and emotional stress, nonstationarity, and larger wrist circumferences. Wrist SC measures showed neither correlation nor visual resemblance with finger SC signal, suggesting that the two sites may reflect different phenomena. Future studies are needed to assess the responsivity of wrist SC to emotional and cognitive stress. Limitations and implications for laboratory and ambulatory research are discussed.

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confidence: 99%

Stressing the accuracy: Wrist‐worn wearable sensor validation over different conditions

et al. 2019

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“…It has been shown previously that IBI extracted from PPG corresponds closely with IBI extracted from ECG [67], [68]. This allowed us to exploit existing high-quality ECG datasets for this study.…”

Section: Discussionmentioning

confidence: 57%

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Harper

Southern

2022

IEEE Trans. Affective Comput.

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Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities − audio, visual, and physiological − to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in realworld domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.

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“…e study concludes that wrist-worn devices such as the E4 are accurate and reliable for heart rate detection when hand movement is not excessive [47]. e use of a wrist-worn monitoring device, specifically the Empatica E4 at 64 Hz sample rate, is deemed sufficient for this study, since the participants are supposed to be sitting down with only moderate movement.…”

Section: Heart Rate Datamentioning

confidence: 97%

Creating an Audio Story with Interactive Binaural Rendering in Virtual Reality

Geronazzo

Rosenkvist

Eriksen

et al. 2019

Wireless Communications and Mobile Computing

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The process of listening to an audiobook is usually a rather passive act that does not require an active interaction. If spatial interaction is incorporated into a storytelling scenario, can open. Possibilities of a novel experience which allows an active participation might affect the user-experience. The aim of this paper is to create a portable prototype system based on an embedded hardware platform, allowing listeners to get immersed in an interactive audio storytelling experience enhanced by dynamic binaural audio rendering. For the evaluation of the experience, a short story based on the horror narrative of Stephen King’s Strawberry Springs is adapted and designed in virtual environments. A comparison among three different listening experiences, namely, (i) monophonic (traditional audio story), (ii) static binaural rendering (state-of-the-art audio story), and (iii) our prototype, is conducted. We discuss the quality of the experience based on usability testing, physiological data, emotional assessments, and questionnaires for immersion and spatial presence. Results identify a clear trend for an increase in immersion with our prototype compared to traditional audiobooks, showing also an emphasis on story-specific emotions, i.e., terror and fear.

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Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities

Cited by 46 publications

References 4 publications

Stressing the accuracy: Wrist‐worn wearable sensor validation over different conditions

Stressing the accuracy: Wrist‐worn wearable sensor validation over different conditions

A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat

Creating an Audio Story with Interactive Binaural Rendering in Virtual Reality

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