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A psychological stress detection method based on heart rate and social media microblogging

A technology of social media and detection methods, applied in psychological devices, diagnostic recording/measurement, medical science, etc., can solve problems such as discontinuity, abnormal ECG signal, and uncertainty

Active Publication Date: 2020-12-15
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, individuals may post relevant microblogs before or after stressful events, and microblogs show non-real-time psychological states
[0006] 2. Continuous and intermittent, posting Weibo is a random behavior, which may be discontinuous, intermittent, and uncertain; while ECG data is continuous
[0008] 4. Stress and non-stress, in addition to stress, excitement and pleasure can also lead to abnormal ECG signals

Method used

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  • A psychological stress detection method based on heart rate and social media microblogging
  • A psychological stress detection method based on heart rate and social media microblogging
  • A psychological stress detection method based on heart rate and social media microblogging

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0108] Example 1 Case Study of SDNN Fluctuations Due to Stress and Positive Events

[0109] To study the effect of stress or positive events on SDNN signals, a 4-month case study (April 2017-July 2017) was conducted on 10 college students aged between 19 and 25, none of whom had any History of heart disease. Each participant wears a professional heart rate monitoring vest between 9:00-11:00 and 14:00-16:00 from Monday to Friday (except public holidays and weekends). The device can record the user's ECG signal in real time, from which the user's daily SDNN value can be calculated.

[0110] Since there are differences in the ECG signals of different users, in order to alleviate this difference, the value of SDNN is normalized, that is, the absolute value of the difference between the calculated SDNN value and the standard SDNN value 141 and the entire time period T = [t 1 , t 2 ,...t |T| ], mapping the SDNN value to the [0,1] interval. Wherein, the time granularity is days...

Embodiment 2

[0116] Embodiment 2, emotional abnormal interval

[0117] The user’s positive or stress intervals are collectively referred to as emotional abnormal intervals. For a Poisson process in which the frequency of posting positive microblogs is constant r, the number N of positive microblogs posted within a period of time D days is a Poisson random variable with a mean value of r*N . Therefore, the probability that a user publishes n positive microblogs within D days can be estimated as: (where n=0, 1, . . . , ∞). In the Poisson process model, the number of positive tweets in non-overlapping periods is an independent random variable. Order r 1 is the frequency of posting positive microblogs during positive events, r 0 is the frequency of positive tweets during non-positive events. Further, let N 1 for the length D 1 The number of positive microblogs published in the positive interval of , N 0 for the length D 0 The number of positive microblogs published in the non-positiv...

Embodiment 3

[0125] Embodiment 3 Abnormal Heart Rate Interval

[0126] Such as Figure 4 As shown in Figure 5, (a) the pressure and positive intervals detected from user microblogs, where SP 1 , SP 2 , SP 3 are three pressure intervals, EP 1 ,EP 2 ,EP 3 are three positive intervals; we have detected the abnormal heart rate interval from the user's ECG signal, and detected the user's stress and positive emotion intervals on the same timeline from the user's Weibo. (b) The abnormal heart rate interval detected from the user's SDNN, where HP 1 , HP 2 , HP 3 There are 3 abnormal heart rate intervals;

[0127] Although the measurement of heart rate variability is more objective compared to the subjective expression in user Weibo, the detection of abnormal heart rate lacks a verbal description of whether the abnormality is caused by a stressful event or a positive event.

[0128] To calculate HP i and PP j The trend similarity of the HP needs to be i and PP j into two trend series:...

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Abstract

The invention discloses a mental stress detection method based on heart rate and social media micro-blog and belongs to the technical field of life stress detection. In the invention, mental stress detection is carried out, on the basis of the heart rate and social media micro-blog, in the manner of: firstly, acquiring a heart rate from an electrocardiosignal; according to the Poisson probabilitymodel, detecting and recognizing an abnormal microblogging (stress / excitement) zone from the micro-blog; then correlating every heart rate abnormal zone with the abnormal microblogging zone in time-synchronized and highly-matched manner; finally on the basis of the matching result, determining whether the heart rate abnormal zone belongs to stress zone or excitement zone, and the stress source event or positive event, which cause the abnormal zone. The analysis make everybody to be aware of influence on health from stress caused by threat, damage or challenge, so that everybody can have a natural mental status to deal with the stress or calm down from the stress source event. For the first time, the mental stress detection is achieved on the basis of the heart rate and social media micro-blog.

Description

technical field [0001] The invention belongs to the technical field of life pressure detection, in particular to a method for detecting psychological pressure based on heart rate and social media microblogs. Background technique [0002] With the rapid development of economy and society, stress has become an important health problem around the world. Previous work demonstrated the discriminative power of electrocardiogram (ECG) and social media for stress detection. However, there are limitations to stress detection using single-source data; everyone feels stressed from time to time. Stress is a psychological process triggered by a threat, injury or challenge that can affect health. Therefore, it is very important to be aware of stress, cope with it, and recover from stressor events. The effectiveness of detecting stress and stressor events through people's language on Weibo is proved in literature [1]-[10]. However, stress detection via microblogging has disadvantages d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/16
CPCA61B5/165A61B5/7246
Inventor 冯铃李凝云冯卓楠
Owner TSINGHUA UNIV
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