北京生物醫(yī)學(xué)工程

基于心率變異率的人體疲勞度評(píng)估模型

Assessment modle based on heart rate variability for human fatigue

作者：於鵬嚴(yán)良文陳佳樂(lè) 余越曹可樂(lè) 黃閃董旭東

單位：上海大學(xué)機(jī)電工程與自動(dòng)化學(xué)院（上海 200072）

關(guān)鍵詞：隱馬爾科夫模型； PPG； HRV； Baum-Welch； Viterbi；疲勞評(píng)估

分類(lèi)號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2021·40·1（46-54）

摘要：

目的構(gòu)建一種信號(hào)采集方便、準(zhǔn)確度高的人體疲勞度評(píng)估模型，以幫助人們及時(shí)地做出疲勞預(yù)警。方法首先使用MAX30102傳感器采集包含大量與人體生理和病理相關(guān)信息的光電容積脈搏波（photoplethysmography, PPG）信號(hào)；然后對(duì)PPG信號(hào)進(jìn)行消除噪聲干擾和基線漂移的預(yù)處理，再運(yùn)用差分閾值法提取心率變異率（heart rate variability, HRV）信息，利用隱馬爾可夫模型（hidden Markov model, HMM）理論建立基于HRV的人體疲勞評(píng)估模型；最后隨機(jī)選取10位自愿者的數(shù)據(jù)利用Baum-Welch算法對(duì)模型參數(shù)進(jìn)行訓(xùn)練優(yōu)化后得到狀態(tài)轉(zhuǎn)移概率矩陣和觀測(cè)值概率矩陣，隨機(jī)選取3位精神狀態(tài)轉(zhuǎn)變模式不同的自愿者數(shù)據(jù)并運(yùn)用Viterbi算法求得最優(yōu)狀態(tài)，與實(shí)際狀態(tài)進(jìn)行對(duì)比。結(jié)果所建模型對(duì)3位自愿者的疲勞評(píng)估準(zhǔn)確度都達(dá)到了80%以上，具有較高的準(zhǔn)確度。結(jié)論運(yùn)用HMM理論構(gòu)建的基于HRV的人體疲勞評(píng)估模型能夠準(zhǔn)確評(píng)估人體的精神狀態(tài)，具有廣闊的應(yīng)用前景。

Objective Mental fatigue has become a social problem. A method of evaluating the fatigue degree with high accuracy and convenient signal collection should be developed, which can help people to give early warning of fatigue in time. Methods Firstly, the MAX30102 sensor was used to collect photoplethysmography (PPG) signals containing a large amount of information related to human physiology and pathology; then the PPG signal was preprocessed to eliminate noise interference and baseline drift, which extracted heart rate variability (HRV) information by applying the method of difference threshold, using Hidden Markov model (HMM) theory to establish a human fatigue assessment model based on HRV; at last, by using randomly selected 10 volunteers’ data to train model parameters for state transition probability matrix and observation probability matrix by Baum - Welch algorithm, data of three volunteers with different mental state transition modes were randomly selected and the optimal state was obtained by using Viterbi algorithm, which was compared with the actual state. Results The accuracy of the fatigue assessment of the three volunteers reached more than 80%, indicating that the model has high accuracy. Conclusion The human fatigue assessment model based on HRV constructed by HMM theory can accurately assess the mental state of human bodies and has a broad application prospect.

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