disp2ppg: Pulse Wave Generation to PPG Sensor using Display

Fujii, Atsuhiro; Murao, Kazuya; Matsuhisa, Naoji

doi:10.1145/3460421.3478823

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…Anderson et al proposed that facial muscle movements can represent emotional states, in which the support vector machine (SVM) was used to identify six basic emotions commonly associated with facial expressions [ 5 ]. The second type of research is based on the neurophysiological state, that is, the acquisition of various physiological signals [ 6 , 7 , 8 , 9 , 10 ], such as electrocardiogram (ECG), photoplethysmography (PPG), and electroencephalogram (EEG), among many others. Although this type of research requires subjects to wear certain appropriate physiological signal acquisition equipment, compared with the former external behavioral research, focusing on neurophysiological states is a more objective method of representing emotions.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Zhu

Rong

Zhao

et al. 2022

Sensors

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Understanding learners’ emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

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Section: Introductionmentioning

confidence: 99%

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Zhu

Rong

Zhao

et al. 2022

Sensors

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“…The SMART R F-18 is equipped with a proprietary OS developed by the manufacturer, and we refer to it here as "SMART OS." Heart rate data collected by this smartwatch can be viewed with the WearHealth app for Android 9 and iPhone 10 .…”

Section: ) Smart Osmentioning

confidence: 99%

“…This paper is an extended version of our 4-page international conference paper [10] 1 . In the previous paper we experimented with the combination of four smartwatches and three displays and the combination of one smartwatch and four displays to evaluate the proposed method within a limited heart rate range.…”

Section: Introductionmentioning

confidence: 99%

Pulse Wave Generation Method for PPG by Using Display

2023

Self Cite

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The extensive research on wearable devices has led to devices with various shapes and mounting locations. Wearable devices are often used to record the user's biometric information, and methods have been proposed to detect physical abnormalities from the acquired data. Among various kinds of biometric data, pulse data has been used in methods such as heart rate monitoring and emotion recognition. The most common type of pulse sensor uses photoplethysmography (PPG), which irradiates a green LED on the skin and measures pulse data from changes in the light reflected from blood vessels. PPG sensors have been implemented in commercially available wearable devices such as smartwatches. However, a PPG sensor requires blood flow for data acquisition, and when a smartwatch is worn on an artificial body part such as a prosthetic hand or a robotic arm, data cannot be acquired because there is no blood flow. In this study, we propose a method that enables a PPG sensor to measure arbitrary pulse data by using a display. If this method is successful, it will enable pulse data measured at the junction of a living limb and an artificial limb to be input to the display; then, a smartwatch attached to the artificial limb will read the same pulse data. In this paper, we focus on the heart rate and report the results of an experiment in which a target heart rate was input and the display was controlled accordingly to determine whether the target heart rate could be obtained by a smartwatch. We implemented a display drawing program and conducted the evaluation with five kinds of smartwatches and four kinds of displays. The results showed that the error between the target heart rate and the heart rate acquired by the smartwatch was within 3 beats per minute (bpm) in many cases when the target heart rate was set to 60-100 bpm. When the target heart rate was set to 40-55 and 105-200 bpm, the heart rate could also be input to the smartwatch with a small error under certain conditions. Moreover, when generated PPG data was imported into heart rate variability (HRV) analysis software, it was recognized as a pulse wave in the same way as real PPG data obtained from a person. We compared the heart rate, RR interval, and SDNN calculated from the real and generated PPG data, and we confirmed that the proposed method could generate stable PPG data. On the other hand, when the waveforms were compared, the generated PPG waveform differed greatly from the real PPG waveform, which indicated that the software could calculate the heart rate, RR interval, SDNN, and LF/HF ratio regardless of the waveform. This result suggests that calculation of these parameters without verifying the waveform would be vulnerable to an attack with fake PPG data.

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“…Our future works include the assessment of PA by generating and transmitting the signals to the PPG sensor. Fujii et al investigated a technique to modulate the light intensity to a PPG sensor to get the target HR from smartwatches [40]. We expect that the similar technique may contribute to the PA against the PPG-based authentication, which spoofs the sensor to impersonate a victim.…”

Section: ) Authentication Algorithmmentioning

confidence: 99%