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PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

100%

Sugiono S., Prasetya R. P., Fanani A. A., Cahyawati A. N.

Acta Neuropsychologica

2022

vol. 20(1)

1-15

Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.

A NEW CONCEPT OF PRODUCT DESIGN BY INVOLVING EMOTIONAL FACTORS USING EEG: A CASE STUDY OF COMPUTER MOUSE DESIGN

76%

Sugiono S., Putra A. S., P R., Fanani A. A., Cahyawati A. N., Oktavianty O.

Product design has long been developed based on reliability and usability, but has neglected the objective measurement in terms of pleasurable experience. This paper presents a new concept of product design, with application in computer mouse design, which not only considers the performance of its functional factor but also emotional factor. A survey involving 153 respondents showed that 75.16% of respondents consider ergonomic / comfort factor as the most important factor, followed by precision factor with 58.17%, and noise factor with 15.03%. Furthermore, a survey of pairewise comparisons were conducted to assess the level of importance of the emotional factor. Analytical Hierarchy Process (AHP) was used to process weigh- tage, resulting in stress = 0.27, focus = 0.279, engagement = 0.29, and interest = 0.265. Finally, the emotional level of 5 different mouse units was assessed through experiments using the EEG Emotiv 16 Channels system 10-20. There are three stages in assessing the mouse which were carried out using the 5 samples, namely the level of interest, the stage of using (ergonomics, focus) t, and the stage of user experience (engagement). From the average measurement of the EEG value, it was found that interest = 57.8 (scale 0-100) on a mouse that has an elegant shape, striking color, and with wifi connectivity, focus & stress because the size fits the shape of the hand and the level of cursor precision, while engagement follows the other three emotional factors. It can be concluded that brain signal exploration through Emotiv’s EEG is able to quantify the emotional factor in product selection through the phase of attraction, use and experience. ------------------------------------------------------------------------------------------------------------------------------------

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2 Acta Neuropsychologica

1 2022

1 2021

2 Cahyawati A. N.

2 Fanani A. A.

2 Sugiono S.

1 Oktavianty O.

1 P R.

1 Prasetya R. P.

1 Putra A. S.

Search results

PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

A NEW CONCEPT OF PRODUCT DESIGN BY INVOLVING EMOTIONAL FACTORS USING EEG: A CASE STUDY OF COMPUTER MOUSE DESIGN