Analysis on Human Heart Signal during Sad Video Stimuli using Heart Rate Variability Triangular Index (HRVi)

Rumpa, Lantana Dioren; Wibawa, Adhi Dharma; Attamimi, Muhammad; Sampelawang, Petrus; Purnomo, Mauridhi Hery; Palelleng, Srivan

doi:10.1109/cenim.2018.8711288

Cited by 4 publications

(1 citation statement)

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“…In order to obtain the window length, the first limit found in the literature review is imposed by feature calculation. There are some features that require a minimum window length to be calculated, such as, HRV triangular index, which takes at least 20 min to be calculated [33][34][35], Standard Deviation of NN intervals (SDNN) index, calculated as mean standard deviations of all NN intervals for all 5 min segments of the entire recording [34], and for all derivatives (Standard Deviation of Successive Differences (SDSD), Standard Deviation of sequential 5-min RR interval (SDANN)) found in [34]. In our case, we decided to use window lengths lower than 60 s, as the database could be largely cut down, which would change the study.…”

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confidence: 99%

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Mohíno-Herranz

Gil-Pita

Rosa-Zurera

et al. 2019

Sensors

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Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 s window length.

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confidence: 99%

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Mohíno-Herranz

Gil-Pita

Rosa-Zurera

et al. 2019

Sensors

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Operationalization of the IAE Model Entities: Relevant Dimensions in the Context of this Work

Spang

2024

T-Labs Series in Telecommunication Services

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Development of ECG sensor using arduino uno and e-health sensor platform: mood detection from heartbeat

Rumpa

Suluh

Ramopoly

et al. 2020

J. Phys.: Conf. Ser.

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Many researcher said that emotion or mood can be detected from physiological changes like the heartbeat. In order to measure human heart activity, we were using tools like Electrocardiograph. The changes on human emotion or mood affect physiology. This paper is part of our research that talked about Analysis of Mood State from Heart Signal during Playing Flappy Bird. In this paper, we will explain how we design ECG tools that could measure or detect the human heartbeat especially that affect by mood changes. E-Health Sensor Platform v2.0 and Arduino Uno were used to build this system. E-Health Sensor Platform v2.0 contains several sensors that can measure the biological state of humans such as heart rate, breathing, skin conductance and many others. This device can operate when connected with Arduino Uno as a microcontroller. Arduino uno role as a liaison between the Platform and the PC via a serial port. We were using C programming languange on Arduino Uno. 100 Hz were sett in programming code so we can read all data from ECG sensor clearly. To visualize the heartbeat signals we were using KSTPlot. This system has successfully read the heart signals of 20 participants. Although there is signal noise but it does not affect the data. The noise can also be removed with a filter.In our next study, the raw data will be analyzed using the HRV method, but this will be discussed in another paper.

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Analysis on Human Heart Signal during Sad Video Stimuli using Heart Rate Variability Triangular Index (HRVi)

Cited by 4 publications

References 8 publications

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study

Operationalization of the IAE Model Entities: Relevant Dimensions in the Context of this Work

Development of ECG sensor using arduino uno and e-health sensor platform: mood detection from heartbeat

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