Emotion Recognition in Elderly Based on SpO<sub>2</sub> and Pulse Rate Signals Using Support Vector Machine

Hakim, Lutfi; Wibawa, Adhi Dharma; Pane, Evi Septiana; Purnomo, Mauridhi Hery

doi:10.1109/icis.2018.8466489

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…The results of this study are better than the previous work [7]. We know that the optimal accuracy of the previous work only reached 72.86% using the SVM method.…”

Section: Resultscontrasting

confidence: 65%

“…In this study, we used the dataset of our previous study [7]. It consists of 31 SpO2 and 31 PR data.…”

Section: Datasetmentioning

confidence: 99%

“…In this study, the MSE method was applied to feature extraction of SpO2 and Pulse rate signals for emotion recognition. In the previous work [7], eight statistical methods in the time domain were used as the main features in the recognition of emotion, including mean, maximum, minimum, variance, mode, standard deviation, ratio, and mean absolute deviation calculations.…”

Section: Feature Extraction Using Multiscale Entropymentioning

confidence: 99%

“…only used PR signal to propose emotion recognition [6]. Also, our previous work used SpO2 and PR signals for emotion recognition in elders [7]. However, the results were still not maximum.…”

Section: Introductionmentioning

confidence: 97%

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Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Hakim

Kristanto

Khoirunnisaa

et al. 2020

KINETIK

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Emotion recognition has been special topics which are frequently discussed by researchers and practitioners in the past decade. SpO2 and Pulse rate signals are two of the limited physiological signals used in a study of emotion recognition. In this study, both of the physiological signals were utilized for emotion recognition in elders by using a combination of two feature extraction methods. Multiscale Entropy was used for feature extraction of signals and then reduced dimension of the feature vector by using Multiclass Fisher’s Linear Discriminant Analysis. The dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction by using only SpO2 signal, the second scheme by using pulse rate signal and the third scheme by multimodal signals (a combination of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The Classification used Support Vector Machine with an input feature of the third scheme showed an optimal performance with an accuracy score of 95.24%. This result showed better accuracy than previous works.

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“…The results of this study are better than the previous work [7]. We know that the optimal accuracy of the previous work only reached 72.86% using the SVM method.…”

Section: Resultscontrasting

confidence: 65%

“…In this study, we used the dataset of our previous study [7]. It consists of 31 SpO2 and 31 PR data.…”

Section: Datasetmentioning

confidence: 99%

Section: Feature Extraction Using Multiscale Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Hakim

Kristanto

Khoirunnisaa

et al. 2020

KINETIK

Self Cite

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“…On the contrary, contact emotion recognition seems to present better results. Hakim et al (2018) combined the oxygen saturation (SpO2) and pulse rate (PR) of older people and input the data into a SVM classifier to recognize three emotions, happy, sad and angry. Here they obtained an accuracy of 72.86%.…”

Section: Related Workmentioning

confidence: 99%

Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy

et al. 2022

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Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people.Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment.Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions.Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%.Conclusion: Since the Bi-LSTM model could tap into the influence of “past” and “future” emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

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CNN-LSTM for automatic emotion recognition using contactless photoplythesmographic signals

Mellouk

Handouzi

2023

Biomedical Signal Processing and Control

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Emotion Recognition in Elderly Based on SpO₂ and Pulse Rate Signals Using Support Vector Machine

Cited by 5 publications

References 10 publications

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy

CNN-LSTM for automatic emotion recognition using contactless photoplythesmographic signals

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Emotion Recognition in Elderly Based on SpO2 and Pulse Rate Signals Using Support Vector Machine

Cited by 5 publications

References 10 publications

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy

CNN-LSTM for automatic emotion recognition using contactless photoplythesmographic signals

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Resources

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Emotion Recognition in Elderly Based on SpO₂ and Pulse Rate Signals Using Support Vector Machine