  Emotion recognition or detection is broadly utilized in patient-doctor
interactions for diseases such as schizophrenia and autism and the most typical
techniques are speech detection and facial recognition. However, features
extracted from these behavior-based emotion recognitions are not reliable since
humans can disguise their emotions. Recording voices or tracking facial
expressions for a long term is also not efficient. Therefore, our aim is to
find a reliable and efficient emotion recognition scheme, which can be used for
non-behavior-based emotion recognition in real-time. This can be solved by
implementing a single-channel electrocardiogram (ECG) based emotion recognition
scheme in a lightweight embedded system. However, existing schemes have
relatively low accuracy. Therefore, we propose a reliable and efficient emotion
recognition scheme - exploitative and explorative grey wolf optimizer based SVM
(X - GWO - SVM) for ECG-based emotion recognition. Two datasets, one raw
self-collected iRealcare dataset, and the widely-used benchmark WESAD dataset
are used in the X - GWO - SVM algorithm for emotion recognition. This work
demonstrates that the X - GWO - SVM algorithm can be used for emotion
recognition and the algorithm exhibits superior performance in reliability
compared to the use of other supervised machine learning methods in earlier
works. It can be implemented in a lightweight embedded system, which is much
more efficient than existing solutions based on deep neural networks.
