4 results about "Heart rate analysis" patented technology

The invention discloses an emotion recognition system and method based on a large model, and relates to the field of data analysis, and the method comprises the steps: collecting facial physiological dynamic data, including specific muscle group motion data and photoelectric volume pulse wave signals, of a person in a calm state, and establishing a reference contraction frequency and a reference heart rate; analyzing data characteristics and calculating an amplification threshold and an enhancement threshold; collecting face data of a target person in real time, and extracting real-time contraction frequency, duration of the real-time contraction frequency exceeding a critical value and real-time heart rate; and judging the emotional state by comparing the amplification and enhancement of the real-time data and the reference value. The system comprises a reference establishment module, a threshold calculation module, a real-time monitoring module, a state identification module and the like. According to the method, non-invasive, objective and accurate emotion recognition is realized through multi-modal physiological signal analysis, and the method has the advantages of high real-time performance and high accuracy.

The invention discloses a relaxation degree calculation method based on dynamic threshold heart rate analysis and a physiological constraint model. The relaxation degree calculation method comprises the steps that a single-sample PPG signal is collected through an ear clip type sensor; performing cleaning and feature extraction on the signals to obtain heart rate basic data and time domain features; further extracting an RR interval sequence, and performing frequency domain conversion and psychological feature evaluation to obtain frequency domain features and psychological features; performing weighted fusion on the time domain and frequency domain features by using a dynamic weighting and physiological constraint module to obtain weighted features; and finally, based on the multi-sample data stored in the annular buffer area, performing feature consistency check, calculating psychological features meeting consistency conditions, and outputting a final psychological relaxation degree value. According to the method, dynamic threshold heart rate analysis and the physiological constraint model are combined, the accuracy and reliability of relaxation degree evaluation are effectively improved, and the method is suitable for real-time and continuous psychological state monitoring.

An object of the system according to the embodiment is to determine the authenticity of the description content of the salesperson and to check the inconsistency between the content of the contract document and the description content.SOLUTION: A system includes a contract document confirmation unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document confirmation unit confirms the contract document. The expression analysis unit analyzes the contents of the contract document confirmed by the contract document confirmation unit. The heart rate analysis part analyzes the expression of the salesman analyzed by the expression analysis part. The summarization part analyzes the heart rate of the salesman analyzed by the heart rate analysis part. The inconsistency checking unit checks the contract document and the description content summarized by the summarizing unit.SELECTED DRAWING: Figure 1

This invention relates to the field of emotion recognition technology, specifically to a method and system for emotion event recognition based on multimodal physiological parameters. The method determines a baseline time window based on the changing trend of the respiratory rate time series; it determines the heart rate analysis interval and blood pressure analysis interval based on the hysteresis response characteristics of the heart rate and blood pressure time series within the baseline time window; the baseline time window, heart rate, and blood pressure analysis intervals constitute the current representation feature; the feature matching degree is determined based on the similarity between the current representation feature and a preset set of historical representation features; the current representation feature is weighted and fused for optimization based on the feature matching degree to determine the optimized representation feature; the optimized representation feature is input into a pre-trained classification model, which outputs a category label corresponding to the emotional event. This makes the representation feature more closely resemble the real emotion-physiological linkage, so that the classification result can take into account the differences in individual physiological characteristics.