Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers
Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
|---|---|
| Enthalten in: |
Diagnostics (Basel, Switzerland) - 13(2023), 12 vom: 16. Juni |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Alam, Aftab [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Electrocardiogram (ECG) |
|---|
| Anmerkungen: |
Date Revised 01.07.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3390/diagnostics13122097 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM358723035 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM358723035 | ||
| 003 | DE-627 | ||
| 005 | 20231226075340.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3390/diagnostics13122097 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1195.xml |
| 035 | |a (DE-627)NLM358723035 | ||
| 035 | |a (NLM)37370991 | ||
| 035 | |a (PII)2097 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Alam, Aftab |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 01.07.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a RF | |
| 650 | 4 | |a SVM | |
| 650 | 4 | |a electrocardiogram (ECG) | |
| 650 | 4 | |a emotion classifier | |
| 650 | 4 | |a emotion recognition system | |
| 650 | 4 | |a human-machine interaction (HMI) | |
| 700 | 1 | |a Urooj, Shabana |e verfasserin |4 aut | |
| 700 | 1 | |a Ansari, Abdul Quaiyum |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Diagnostics (Basel, Switzerland) |d 2011 |g 13(2023), 12 vom: 16. Juni |w (DE-627)NLM250111136 |x 2075-4418 |7 nnns |
| 773 | 1 | 8 | |g volume:13 |g year:2023 |g number:12 |g day:16 |g month:06 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3390/diagnostics13122097 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 13 |j 2023 |e 12 |b 16 |c 06 | ||