Key result
AT2GRU model with MDHM surpasses LSTM and GRU models in emotion recognition accuracy.
Why the study?
Device heterogeneity negatively impacts classification of healthcare data, necessitating methods to mitigate this effect for reliable multi-device physiological signal measurement.
The AT2GRU model with heterogeneity mitigation improves emotion recognition accuracy from multi-modal physiological signals compared to standard LSTM and GRU models.
May enhance multi-device physiological signal reliability in healthcare; leaves open prospective clinical validation of MDHM and AT2GRU.
Device heterogeneity can cause a detrimental impact on the classification of healthcare data. In this work, we propose the Maximum Difference-based Heterogeneity Mitigation (MDHM) method to address device heterogeneity. Mitigating heterogeneity increases the reliability of using multiple devices from different manufacturers for measuring a particular physiological signal. Further, we propose an attention-based bilevel GRU (Gated Recurrent Unit) model, abbreviated as AT2GRU, to classify multi-modal healthcare time-series data for human emotion recognition. The physiological signals of Electroencephalogram (EEG) and Electrocardiogram (ECG) for twenty-three persons are leveraged from the DREAMER dataset for emotion recognition. Also, from the DEAP dataset, the biosignals namely EEG, Galvanic Skin Response (GSR), Respiration Amplitude (RA), Skin Temperature (ST), Blood Volume (BV), Electromyogram (EMG) and Electrooculogram (EOG) of thirty-two persons are used for emotion recognition. The EEG and the other biosignals are denoised by the wavelet filters for enhancing the model's classification accuracy. A multi-class classification is carried out considering valence, arousal, and dominance for each person in the datasets. The classification accuracy is validated against the self-assessment obtained from the respective person after watching a movie/video. The proposed AT2GRU model surpasses the other sequential models namely Long Short Term Memory (LSTM) and GRU in performance.
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Khan et al. (2021) studied Human emotion recognition (n=55). AT2GRU model with Maximum Difference-based Heterogeneity Mitigation vs. LSTM and GRU models was evaluated on Classification accuracy for valence, arousal, and dominance. The proposed AT2GRU model with Maximum Difference-based Heterogeneity Mitigation surpassed LSTM and GRU models in classification accuracy for human emotion recognition.
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