A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis

Martínez, Raquel; Salazar-Ramirez, Asier; Arruti, Andoni; Irigoyen, Eloy; Martín, Jesús Izquierdo; Muguerza, Javier

doi:10.1109/access.2019.2908445

Cited by 16 publications

(12 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The GSR values tend to be more chaotic during stressful periods, whereas in relaxed states, the signal stabilizes and resembles a straight line with a constant slope [23], [34]. In this work, this characteristic was collected by measuring the differential area between the GSR signal and its firstorder interpolation (AGSR), as shown in Figure 3.…”

Section: A: Mean Heart Period (Mhp)mentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: ) Extraction Of Relevant Parameterssupporting

confidence: 58%

Section: A: Mean Heart Period (Mhp)mentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Current methods of emotion recognition mainly involve facial expression recognition [3][4][5][6], speech emotion recognition [7][8][9], gesture expression recognition [10], text recognition [11], physiological pattern recognition, and multimodal emotion recognition [12][13][14][15]. In practical applications, the non-contact method of extracting physiological parameters for face imaging has attracted special attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial–Spectral–Temporal Framework for Emotion Recognition

Hong

2020

IEEE Access

View full text Add to dashboard Cite

An emotion recognition method based on multispectral imaging technology and tissue oxygen saturation (StO2) is proposed in this study. This method is called spatial-spectral-temporal adjustment convolutional neural network (SACNN). First, we use the algorithm to extract the StO2 content of an emotionally sensitive nose area through real-time multispectral imaging technology. Compared with facial expression data, StO2 data are more objective and cannot be controlled and changed artificially. Second, we construct a clustering algorithm based on the emotional state by extracting the spectral, StO2, and spatial features of the nose image to obtain accurate signals of emotionally sensitive areas. To utilize the correlation between spectral and spatial signals, we propose an adjustment-based CNN module, which reorganizes the relationship between all previous layers of the feature map, thereby making the relationship among layers close and highly quantitative. The features extracted through this method are consistent with spatial-spectral features. Third, we incorporate the extracted temporal feature signal into the long short-term memory module and finally complete the correlation between the spatial-spectral-temporal features. Experimental results show that the accuracy of the SACNN algorithm in emotional recognition reaches 90%, and the proposed method is more competitive than state-of-the-art approaches. To the best of our knowledge, this study is the first to use time-series StO2 signals for emotion recognition. INDEX TERMS Multispectral imaging, oxygen saturation, spatial-spectral-temporal adjustment convolutional neural network. I.

show abstract

“…IOLOGICAL signals are space, time, or space-time records of biological events such as the heart beating or a muscle contracting [1]. Biological signals including electroencephalogram (EEG) [2], electrocardiogram (ECG) [3], [4], electro-oculography (EOG) [5], surface electromyogram (sEMG) [6], [7], galvanic skin response (GSR) [8], [9] and respiration, are widely used in fields such as clinical disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Classification of Interbeat Interval Time-Series Using Attention Entropy

Yang

Choudhary

Rahardja

et al. 2023

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Classification of interbeat interval time-series which fluctuates in an irregular and complex manner is very challenging. Typically, entropy methods are employed to quantify the complexity of the time-series for classifying. Traditional entropy methods focus on the frequency distribution of all the observations in a time-series. This requires a relatively long time-series with at least a couple of thousands of data points, which limits their usages in practical applications. The methods are also sensitive to the parameter settings. In this paper, we propose a conceptually new approach called attention entropy, which pays attention only to the key observations. Instead of counting the frequency of all observations, it analyzes the frequency distribution of the intervals between the key observations in a time-series. The advantages of the attention entropy are that it does not need any parameter to tune, is robust to the time-series length, and requires only linear time to compute. Experiments show that it outperforms fourteen state-of-the-art entropy methods evaluated by real-world datasets. It achieves average classification accuracy of AUC=0.71 while the second-best method, multiscale entropy, achieves AUC=0.62 when classifying four groups of people with a time-series length of 100.

show abstract

A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis

Cited by 16 publications

References 59 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

Spatial–Spectral–Temporal Framework for Emotion Recognition

Classification of Interbeat Interval Time-Series Using Attention Entropy

Contact Info

Product

Resources

About