A Pressure-Based High-Quality Wrist Pulse Measurement System With an Application in Emotion Analysis

Wrist pulse contains much information about human beings, a typical pulse wave contains four significant morphological features which are related to the cardiovascular system and can be used for disease diagnosis. A high-quality wrist pulse measurement system is essential for the medical application of wrist pulses. In this work, a high-quality wrist pulse measurement system based on a pressure sensor is proposed. The measurement system has a maximum signal-to-noise ratio (SNR) of 53.44 dB and satisfactory ability to resist environmental interferences. The system is applied to measure the wrist pulse signals of seven participants under different emotions, and the signal quality is assessed in three signal quality indices (SQIs): the skewness, the kurtosis, and the perfusion index. The collected dataset presents a better performance in all three SQIs than the DEAP dataset. The measured high-quality wrist pulse data are applied in emotion classification by a long short term memory (LSTM)-gated recurrent unit (GRU) Boosting neural network. The emotion classification accuracy is 89.71% in valence and 80.06% in arousal. We also find that features extracted by the proposed neural network have a strong connection with three morphological features of the wrist pulse, and we show the medical implication of these three morphological features with emotion.