Objective To explore the role in sleep staging from blood volume pulse(BVP)and triaxial acceleration(ACC)of body movement obtained by wristband.Methods The BVP and ACC obtained by Empatica E4 wristband were used from all 100 cases of sleep disorder subjects in the DREAMT public database.Two frequency domain characteristics(eS,LF/HF)and one time domain characteristic(vA)of the BVP baseline and the activity counts(CS)of the ACC were used for sleep-awakening classification based on random forest.Results The results of sleep-awakening classification of all 100 cases of sleep disorder subjects were obtained by leaving-one-out strategy.The accuracy is 79.8%and the Kappa coefficient is 0.56 by 4 features from BVP and ACC;the accuracy is 70.4%and the Kappa coefficient is 0.36 by 3 features of BVP;the accuracy is 75.1%and the Kappa coefficient is 0.47 based on activity counts.Conclusion The BVP and ACC obtained by the wristband can be used for the rough estimation of sleep and awakening for sleep disorder subjects,among which the importance of ACC is higher than that of BVP.

Objective In order to simplify the hygiene processing and reduce the load of sleep monitoring,a method of sleep quality assessment on orbit without EEG is explored.Methods Using the open database ISRUC-Sleep with AASM standard,the training set(n=20)and the test set(sleep disorder group(n=10)and health group(n=10))are completely independent.The electrooculogram(EOG)features include the energy,the root mean square,correlation coefficients and phase-locked values between different frequency bands of two-channel EOG.The electromyogram(EMG)features include fractal dimension,root mean square,the mean value,the maximum value and the root mean square of EMG envelope.Linear support vector machine(LSVM)and random forest(RF)were used to classify wakefulness,REM sleep,light sleep and deep sleep.The accuracy was compared with the results that derived from six-channel electroencephalogram(EEG),two-channel EOG and one-channel EMG.Results Using 50 normalized features of EOG(44 features)and EMG(6 features),for sleep disorder group,kappa coefficients were both 0.75 by RF and by LSVM;for healthy group,the kappa coefficients were 0.73 by RF and 0.70 by LSVM.As a reference for AASM standard,using 140 normalized features of EEG(90 features),EOG(44 features)and EMG(6 features),for sleep disorder group,kappa coefficients were 0.78 by RF and 0.79 by LSVM;for healthy group,kappa coefficients were 0.74 by RF and 0.76 by LSVM.Conclusion The accuracy of sleep scoring from two-channel EOG and one-channel EMG is comparable with that of the gold standard,and can be applied to evaluate the sleep quality during manned spaceflight.

Whether it is the long-term health maintenance of the space station in orbit,or the research on aerospace medical issues,multiple physiological indicators reflecting human physiological functions need to be monitored.With the development of wearable physiological detection technology,the requirements for comfort,low load,and human-machine friendly during the continuous collection and detection of physiological information are becoming increasingly high.This paper designs a set of wearable multi-physiological parameter detection system based on flexible electrodes using fabric electrodes as signal sensors.The system includes hardware such as intelligent clothing and physiological signal detection modules,as well as physiological signal display and storage software,which achieves synchronous detection of multiple physiological signals such as ECG,respiration,and EMG.The design ideas,implementation method,key technologies,and results for collecting,detecting,and processing physiological signals under non-steady state conditions are introduced in this paper.The experimental results show that the system can reliably detect physiological signals such as ECG,respiration,and EMG during motion in flight.