Monitoring and Classification of Emotions in Elderly People

Fonseca, Davi de Miranda; Silva, Katia C. Neles da; Rosa, Renata Lopes; Rodríguez, Demóstenes Zegarra

doi:10.23919/softcom.2019.8903600

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Using this value, the subject-based resampling frequency "fsnc" is evaluated in Row 12. Finally, in Rows [13][14][15], all the signals collected from subject "s" are resampled using the new sampling frequency "fsnc". 1: i ← 0 2: FSNB ← resting state heartbeat frequency in subject normalized domain 3: for each baseline signal "bl" collected from subject "s" do 4:…”

Section: Domain Continuous Time Discrete Time Subject Normalizedmentioning

confidence: 99%

“…Moreover, systems able to recognize the subject's emotional state can also be involved in the medical area. For instance, emotion recognition systems can be used to monitor the health state of convalescent patients [6] or to help elderly subjects during their daily activities [13,14]. In all of these contexts, the development of systems able to recognize, interpret, and simulate human affect can be seen as a necessary step to make technologies user friendly and able to interact actively with people.…”

Section: Introductionmentioning

confidence: 99%

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Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Gasparini

Grossi

Giltri

et al. 2022

Signals

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Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.

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Section: Domain Continuous Time Discrete Time Subject Normalizedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Gasparini

Grossi

Giltri

et al. 2022

Signals

View full text Add to dashboard Cite

show abstract

“…Moreover, systems able to recognize the subject's emotional state can also be involved in the medical area. For instance, emotion recognition systems can be used to monitor the health state of convalescent patients [3] or to help elderly subjects during their daily activities [10,11]. In all these contexts, the development of systems able to recognize, interpret and simulate human affect can be seen as a necessary step to make technologies user-friendly and able to interact actively with people.…”

Section: Introductionmentioning

confidence: 99%

Personalized PPG Normalization based on Subject Heartbeat in Resting State Condition

Gasparini¹,

Grossi²,

Giltri³

et al. 2022

Preprint

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Physiological responses are nowadays widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity is considered in the case of PhotoPlethysmoGraphy (PPG), which is successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is here proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject's heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization is evaluated in comparison with other normalization procedures in a binary classification task, where cognitive load and relaxing state are considered. The results obtained on two different datasets available in the literature confirm that applying the proposed normalization strategy permits to increase classification performance.

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Elderly, Their Emotions and Deep Learning Techniques to Help Their Dignified and Positive Aging

Bastidas

Porras

Gómez³

et al. 2022

Proceedings of the International Conference on Ubiquitous Computing &Amp; Ambient Intelligence (UCAmI 2022)

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Monitoring and Classification of Emotions in Elderly People

Cited by 3 publications

References 27 publications

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Personalized PPG Normalization based on Subject Heartbeat in Resting State Condition

Elderly, Their Emotions and Deep Learning Techniques to Help Their Dignified and Positive Aging

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