Acquisition and Fuzzy Processing of Physiological Signals to Obtain Human Stress Level Using Low Cost Portable Hardware

Zalabarria, Unai; Irigoyen, Eloy; Martínez, Raquel; Arechalde, Javier

doi:10.1007/978-3-319-67180-2_7

Cited by 3 publications

(10 citation statements)

References 17 publications

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“…A total of 5 physiological parameters were extracted: mean HP, variation in the HP, variation in the GSR, differential area between the GSR and its first-order interpolation and the product between the root mean square of HP and the standard deviation of the frequency correlation of the RSP. These parameters are closely related to stress and relaxation events, as demonstrated in previous works [2], [4], [17], [23], [24], [27], [34].…”

Section: ) Extraction Of Relevant Parameterssupporting

confidence: 58%

“…Considering the importance of some physiological signals in the estimation of stress and relaxation, heart period (HP), GSR and RSP were monitored using noninvasive sensors [10], [17], [20], [21], [27], [28]. Figure 2 shows the placement of the sensors that are utilized to collect these signals with the BIOPAC MP36 hardware.…”

Section: A Experimental Stagementioning

confidence: 99%

“…Thus, patterns that indicate the activation of stress and relaxation states can be detected. Subsequently, these patterns were modeled in the second phase of parameter extraction, in which a fuzzy logic model was applied to estimate the stress and relaxation level by applying expert knowledge via well-defined rules [17], [27], [33].…”

Section: B Software Developmentmentioning

confidence: 99%

“…This work also proposes the use of the differential area between the GSR signal and its first-order interpolation as a substitute for raw GSR to avoid differences in thresholds for different subjects during the same stress situations. The results obtained in the proposal presented in [17] are improved by adding the standard deviation of the RSP frequencies, which is calculated using a fast Fourier transform of reduced size to avoid excessive energy consumption due to its relatively high computational load.…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches were studied to produce a solution that combines a low-cost platform with wireless implementation [18]- [21]. Considering size, popularity and price, Arduino Micro (18e) and Raspberry Pi Zero (20e) devices have proven to be suitable for a robust and low-cost prototype [14]- [17]. Both platforms were combined to obtain a miniaturized solution: Arduino Micro for real-time physiological data acquisition and Raspberry Pi Zero for running the developed algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

et al. 2020

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This paper proposes a reliable stress and relaxation level estimation algorithm that is implemented in a portable, low-cost hardware device and executed in real time. The main objective of this work is to offer an affordable and ''ready-to-go'' solution for medical and personal environments, in which the detection of the arousal level of a person is crucial. Methods: To achieve meaningful identification of stress and relaxation, a fuzzy algorithm based on expert knowledge is built according to parameters extracted from physiological records. In addition to the heart rate, parameters extracted from the galvanic skin response and breath are employed to extend the results. Moreover, this algorithm achieves accurate results with a restricted computational load and can be implemented in a miniaturized low-cost prototype. The developed solution includes standard and actively shielded electrodes that are connected to an Arduino device for acquisition, while parameter extraction and fuzzy processing are conducted with a more powerful Raspberry Pi board. The proposed solution is validated using real physiological registers from 42 subjects collected using BIOPAC MP36 hardware. Additionally, a real-time acquisition, processing and remote cloud storage service is integrated via IoT wireless technology. Results: Robust identification of stress and relaxation is achieved, with F1 scores of 91.15% and 96.61%, respectively. Moreover, processing is performed using a 20-second sliding window; thus, only a small frame of context is required. Significance: This work presents a reliable solution for identifying stress and relaxation levels in real time, which can lead to the production of low-cost commercial devices for use in medical and personal environments.

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Section: ) Extraction Of Relevant Parameterssupporting

confidence: 58%

Section: A Experimental Stagementioning

confidence: 99%

Section: B Software Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

et al. 2020

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Performance of different membership functions in stress classification with fuzzy logic

Bülbül

2022

Bitlis Eren University Journal of Science and Technology

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Stress has become an indispensable part of today's world. Stress can have a very serious negative impact on human health. Knowing the intensity of stress on people is important in order to cope with it. In this study, 4 different Fuzzy Logic (FL) structures were used to classify human stress through sleep. In the established structures, the human stress detection data set in sleep and through sleep obtained from Kaggle was used. In the FL structures created, blood oxygen level and respiratory rate were taken as input and stress classification was made accordingly. Their performance in the classification of sleep stress was evaluated by using different membership functions in 4 different structures. As a result of experimental studies, the F model established with the generalized bell showed more successful results than the models established with other membership functions.

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The study of Arduino Uno feasibility for DAQ purposes

Barański¹,

Galewski²,

Nitkiewicz³

2019

Diagnostyka

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Acquisition and Fuzzy Processing of Physiological Signals to Obtain Human Stress Level Using Low Cost Portable Hardware

Cited by 3 publications

References 17 publications

A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

A Low-Cost, Portable Solution for Stress and Relaxation Estimation Based on a Real-Time Fuzzy Algorithm

Performance of different membership functions in stress classification with fuzzy logic

The study of Arduino Uno feasibility for DAQ purposes

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