nonintrusive human’s functional state monitoring system · 2018. 1. 4. · nonintrusive human’s...

Nonintrusive human’s functional state monitoring system A.B. Kogan Research Institute for Neurocybernetics KRINC Rostov-on-Don, Russia A. Vtyurina, S. Anishchenko, M. Petrushan [email protected] Abstract The overall aim of this research is to investigate correlation between visual features and human’s functional state, i.e. level of fatigue, . alertness Motivation Great responsibility lies with those people who control machines. Such a routine job may eventually decrease the alertness level, which in its turn may cause accidents. Any human monitoring system should not make people feel uncomfortable. From here we got the idea of creating a system which will allow us to accomplish nonintrusive human state control while working Nowadays the number of automatic devices has increased dramatically comparing to those of the previous decade. . Feature detection RGB picture is used to find face location with Viola-Jones algorithm. RGB picture and depth map from Kinect sensor are put together in order to get depth map of face area. Using the Point Cloud Library 3D face model is built. RGB picture is used to locate face and eyes. k-NN and HOG are used to recognize the state of an eye – whether it is open or closed. Faculty of athematics, and Computer Science, M Mechanics Southern Federal University, Rostov-on-Don, Russia Using heuristic algorithms we estimate head orientation and position. We compute vector directed from the head center to nose tip. Blinking frequency grows when fatigue occurs. Maximum peaks occur before lunch time and at the end of the day. Low variance of head position distribution at the beginning and at the end of the day. Correlation between fatigue and face direction change frequency was found with the help of spectral analysis. Different frequency prevails in different periods. Finding correlation Conclusion The results obtained show the correlation between visual facial features and human functional state. Having used this features it is possible to create a human functional state monitoring system. Time (hours) E y e blinking frequency (per minut e) Face direction change frequency Frequency prevalence Average face position variance on Y-axis Average face position variance on X-axis

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Nonintrusive human’s functional state monitoring system

A.B. Kogan Research Institutefor Neurocybernetics

KRINCRostov-on-Don, Russia

A. Vtyurina, S. Anishchenko, M. Petrushan [email protected]

AbstractThe overall aim of this research is to investigate correlation between visual features and human’s functional state, i.e. level of fatigue, .alertness

Motivation

Great responsibility lies with those people who control machines. Such a routine job may eventually decrease the alertness level, which in its turn may cause accidents. Any human monitoring system should not make peoplefeel uncomfortable. From here we got the idea of creating a system which will allow us to accomplish nonintrusive human state control while working

Nowadays the number of automatic devices has increased dramatically comparing to those of the previous decade.

Feature detection

RGB picture is used to find face location with Viola-Jones algorithm.

RGB picture and depth map from Kinect sensor are put together in order to get depthmap of face area.

Using the Point Cloud Library 3D face model is built.

RGB picture is used to locate faceand eyes. k-NN and HOG are used to recognize the state of an eye – whether it is open or closed.

Faculty of athematics, and Computer Science,

M Mechanics

Southern Federal University,Rostov-on-Don, Russia

Using heuristic algorithms we estimate head orientation and position. We compute vector directed from the head center to nose tip.

Blinking frequency grows when fatigue occurs. Maximum peaksoccur before lunch timeand at the end of the day.

Low variance of head position distribution at the beginning and at the end of the day.

Correlation between fatigue and face directionchange frequency was found with the help ofspectral analysis. Different frequency prevailsin different periods.

Finding correlation

Conclusion

The results obtained show the correlation between visual facial features and human functional state. Having used this features it is possible to create a human functional state monitoring system.

Time (hours)

Eye

blin

kin

g fr

equ

ency

er m

inu

te)

Face direction change frequency

Freq