TY - GEN
T1 - Emotion Recognition by Point Process Characterization of Heartbeat Dynamics
AU - Ravindran, Akshay Sujatha
AU - Nakagome, Sho
AU - Wickramasuriya, Dilranjan S.
AU - Contreras-Vidal, Jose L.
AU - Faghih, Rose T.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Recognizing human emotion from heartbeat information alone is a challenging but ongoing research area. Here, we utilize a point process model to characterize heartbeat dynamics and use it to extract instantaneous heart rate variability (HRV) features. These features are then fed into a convolutional neural network (CNN) to characterize different emotional states from small windows. On average, we achieved over 60% classification accuracy and as high as 77% in some subjects. This is comparable to other studies that use a combination of physiological signals as opposed to only HRV measures as done here. Informative features were identified for the different affective states. These findings enable the possibility of augmenting electrocardiogram or photoplethysmogram monitoring wearable devices with automated human emotion recognition capabilities for mental health applications. They also allow for the use of instantaneous estimation of HRV features to be used in combination with models that use other types of physiological signals for instantaneous emotion recognition.
AB - Recognizing human emotion from heartbeat information alone is a challenging but ongoing research area. Here, we utilize a point process model to characterize heartbeat dynamics and use it to extract instantaneous heart rate variability (HRV) features. These features are then fed into a convolutional neural network (CNN) to characterize different emotional states from small windows. On average, we achieved over 60% classification accuracy and as high as 77% in some subjects. This is comparable to other studies that use a combination of physiological signals as opposed to only HRV measures as done here. Informative features were identified for the different affective states. These findings enable the possibility of augmenting electrocardiogram or photoplethysmogram monitoring wearable devices with automated human emotion recognition capabilities for mental health applications. They also allow for the use of instantaneous estimation of HRV features to be used in combination with models that use other types of physiological signals for instantaneous emotion recognition.
KW - emotion
KW - heart rate
KW - point process modeling
UR - http://www.scopus.com/inward/record.url?scp=85077992187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077992187&partnerID=8YFLogxK
U2 - 10.1109/HI-POCT45284.2019.8962886
DO - 10.1109/HI-POCT45284.2019.8962886
M3 - Conference contribution
AN - SCOPUS:85077992187
T3 - 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
SP - 13
EP - 16
BT - 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2019
Y2 - 20 November 2019 through 22 November 2019
ER -