A novel ECG-based real-time detection method of negative emotions in wearable applications

Abstract

Emotion recognition especially negative emotion detection has become a topic of considerable concern in both scientific research and practical applications. The inherent limitation is that a number of physiological signals are difficult to be monitored in daily life activities. This paper presents a novel method on negative emotion detection via feature fusion from only one-channel electrocardiogram (ECG) signal, which is able to instantaneously assess the subject's state even in real-time events. A series of features have been calculated from the original ECG signal and its derived heart rate variability (HRV), including linear-derived features, nonlinear-derived features, time domain (TD) features, and time-frequency domain (T-F D) features, which are then fused for classification using SVM. The new method was implemented on the Bio Vid Emo DB dataset for evaluation, where the highest accuracy of 79.51% was achieved with minor time cost of 0.13ms in the classification of positive and negative emotion states. It exhibited a better performance than the relevant studies in the comparison experiments. This method is applicable for wearable negative emotion detection due to its acceptable accuracy, real-time performance, as well as the convenience of wearable one-channel ECG acquisition in daily activities.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 48 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.