Proceedings of 1st International Symposium for Color Science and Art 2019

Development of a Smart LED Lighting System for
Preventing Unconcentrated State
Masayuki Sekiguchi, Rezenko Roman Yurievich, Zixun He,Yuta Ogai, Yousun Kang, and Duk ShinTokyo Polytechnic Univ., 1583 Iiyama, Atsugi, Kanagawa 243-0297, JapanABSTRACT

These three states can be judged by measuring the lineof sight. We aimed to develop a smart LED lighting systemthat improves declining concentrativeness of students byusing eye-tracking device. In this study, we focused mostly
on the state of "Wakimi".

In recent years, there have been many ‘unconcentrated’students on the class such as ‘inattention’, ‘reverie’, and‘drowsy’ state students. We especially focused on‘inattention’ state in this research. This study developed asmart LED lighting system that improves declining
concentrativeness of students by using eye-tracking
device. Eleven volunteers participated in this study. As theresults, the values of accuracy, sensitivity and specificity
were 98.4%, 97%, and 99.5%, relatively. This proposedsystem can be utilized in the educational field soon.

2. METHODS
2.1 The smart LED lighting system
The proposed system consists of an eye-trackingdevice (Tobii eye tracker 4c, Tobii Technology Co., Ltd.),
a self-made LED control box by using Arduino, and an LED
light on the monitor. The Tobii eye-tracker 4c is a device
that can perform eye tracking and head tracking at thesame time. You can measure data on the line of sight,
adjusting the offset depending on the position of the head.
The eye is detected by using a near infrared beam(wavelength from 0.7 to 2.5 μm), as well as a method ofreflecting light through the cornea. [3]. We setup thedistance to 50 ~ 95 cm between the participant and themonitor. The operating frequency is 90Hz. In addition, we
created the “Wakimi” detection program using the SDK ofthis equipment.
2.2 Experiments
Eleven participants (10 male and 1 female; 22.5±1.3years) with normal vision were participated in this study.
Participants asked to sit and monitor in front of the display.
Calibration was performed prior to the experiment to adjust
the difference in the position of the eyeball individual. To
test the proposed system, we studied the reaction of theLEDs to the state of "wakimi" in accordance with theinstruction (40 ‘Wakimi’ epochs and 40 ‘Normal’ epochs).
First, it is necessary to specify the region of interest for
determining whether inattentive or not. The size of themonitor display is specified as attentive gaze region. Then,the proposed system finds the X and Y coordinate valuesof the line of sight of the participant. If the X and Y values
did not exist within the range of interest and maintained
this state for 5 seconds, system will judge this as ‘Wakimi’and prompted the warning LED lamp to light up. The user
also can arbitrarily set the time to judge as a ‘Wakimi’ stare.
The color of the warning LED changes purple at normal to
flickering-red at ‘Wakimi’.

1. INTRODUCTION
Improvement of student’s ability to train academic
performance at educational sites is a big issue. Recently,
there are many students who do not concentrate on theirclasses due to the development of portable gamecontroller (including smart phones). Therefore, we aim todevelop a system to improve the concentrativeness ofstudents in this research.
We classified the state of unconcentrated states into
three categories: "Inattention (i.e. Wakimi) [1]", "Reverie",and "Drowsy [2]." A strike issue should be in an inattention
state when many students usually watch somethingirrelevant to the class and attention decreases: shown in
Fig. 1. Reverie is a state of abstracted musing;
daydreaming. Drowsy is a state in which consciousness
falls: sleeping and sitting unconsciously.

Fig. 1 The smart LED lighting system

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Proceedings of 1st International Symposium for Color Science and Art 2019Table 1. Performance metrix in the System
ActualPredict
LED ONTP (hit)FP (false alarm)

LED OFFFN (miss)

TN (correct negative)

Inattention

Attention

2.3 Analysis
Critical Success Index (CSI) is one of the indicators to
predict a certain event from the actual state and evaluate
the predict performance of the system in binary categories[3]. The number of cases is shown in Table 1.
TP (True Positive) represents the number of cases
where the system operated correctly when the participant
remained in ‘Wakimi’ state. FN (False Negative) is missing
and represents the number of cases that did not workwhen the participant was at ‘Wakimi’ state. FP (FalsePositive) is a false alarm, which represents the number ofcases in which the system had excessively operated. TN(True Negative) represents the number of cases where thesystem does not operate properly when he/she should beat normal state.
Accuracy (agreement), sensitivity, and specificity arecalculated as follows;

agreement =Fig. 2 Relative operating characteristic (ROC)
4. DISCUSION
In this research, we developed a focus concentration
improvement from ‘Wakimi’ state. ROC curve measures
the ability of the forecast to discriminate between hit rate
and false alarm. The red-dotted line lie along the diagonalin Fig. 2 means that hit rates equal to the false alarm rates
and the system, therefore, has no skill. The ROC of theproposed system was close to an ideal curve.
The Tobii eye-tracker did not wear on the body but wasinstalled on the bottom corner of the monitor. Therefore,
the physical burden on the user is reduced.

TP + TN×100
allepochs

TP×100TP + FNTNspecificity =×100TN + FP
sensitivity =5. CONCLUSION
In this research, we developed a smart LED lighting
system that improves declining concentrativeness ofstudents by using the Tobii eye-tracking device. The
results of eleven participants demonstrated that theproposed system could judge the ‘Wakimi’ state with aprobability of 98.4%. Moreover, it can be said that thesmart LED lighting system is a high-performance systemclose to the ideal state from the ROC curve. Therefore, thissystem can be utilized in the educational field soon.

The values of accuracy, sensitivity, specificity, and theRelative operating characteristic (ROC) curve werecalculated from the evaluation experimental data using
MATLAB.
3. RESULTS
Calculate accuracy, sensitivity, and specificity from thecounts of TN, FN, TN and FP. The value of accuracy,
sensitivity, and specificity were 98.4%, 97.0%, and 99.5%,respectively. The accuracy indicated that the system could
be operated with a probability close to 100%. The
sensitivity (hit rate) showed the verification that thewarning from the system effectively urged participants that
tried to stare at a smartphone to remain in concentrated
state. The specificity could be said that the false alarm rateof the proposed system showed barely about 0.5%. Thefalse alarm from system error is so very little that theproposed system does not break user’s concentration.
We also derive the ROC curve as the probabilistic
performance of the system as shown in Fig 2. The area
under curve (AUC) value was 0.9873.

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