53-biometric interface for drivers stress detection and awareness

1、Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 18), September 2325, 2018, Toronto, Canada.Biometric Interface for DriversStress Detection and AwarenessAbstractOne of the factors for stress reduction amon

2、g vehicle drivers is to be aware that stress is present. This project presents a biometric interface for stress detection in drivers, built with open source sensors and hardware. In two series of experiments, we induce stress in test subjects by making them drive progressively difficult scenarios in

3、 a simulator. Using the C4.5 classification algorithm, we classified thesubjects biometric data in order to determine whether the subject was stressed or not. In another series of experiments, we tested the efficacy of two driver feedback systems, a haptic one and a visual one.Identifying a stressfu

4、l situation allows real-time feedback to drivers, so they can be aware of their stressed state, thus being able to take corrective actions on time, and avoid behavior leading to an accident.Juan Manuel MadridUniversidad Icesi Cali, Colombia .coFabian Lasso Universidad Icesi Cali, Col

5、Carlos A Arce-LoperaUniversidad Icesi Cali, Colombia .coPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commerc

6、ial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prio

7、r specific permission and/or a fee. Request permissions fromP.Author KeywordsBiometric Sensor; Stress; Emotional States; Road Accidents; Arduino; J48 Algorithm.CCS Concepts Applied computingConsumer health Human-centered computingDisplays and imagers Human-centered computingHaptic d

8、evicesAutomotiveUI 18 Adjunct, September 2325, 2018, Toronto, ON, Canada 2018 Association for Computing Machinery. ACM ISBN 978-1-4503-5947-4/18/09$15.00/10.1145/3239092.3265970Work-in-ProgressAutomotiveUI 18, Toronto, CanadaIntroductionThousands of people are injured or killed every y

9、ear in road accidents 15. To face this social issue, car manufacturers and academia do research in order to develop new devices and techniques for improving road safety, such as automatic braking, lane-keeping assistance, blind spot monitoring and driver fatigue detection, among others 13. The field

10、 of detection of emotional changes in drivers is currently very active, because altered awareness states can lead to impulsive, aggressive or distracted behaviors, leading to risks in road safety 7.Heart rate: We used a Grove heart rate sensor 4 installed in the steering wheel, such that drivers can

11、 touch it with their left thumb.Galvanic skin response (GSR) 3: We used a Grove GSR sensor 5, attached to the drivers index and middle fingers.Pressure sensors: We used these to detect the drivers position in the seat, since a bad sitting position, i.e. leaning forward or backwards, may induce stres

12、s. We used five load cells 12, fourmounted on the seats bottom (two in front, two in back), and one on the seats back. When drivers are correctly seated, pressure is sensed in all cells, whereas a leaning position leaves some cells without pressure.Our research aims to use biometric sensors to detec

13、t emotional states linked to stress in a driver. Sensor data is fed to an artificial intelligence (AI) algorithm, which determines if the driver is stressed. Finally, the driver is made aware of this by means of stimuli, making him/her aware of the stress, thus potentially avoiding a road accident.I

14、nterface ArchitectureFigure 1 shows the architecture for our proposed biometric interface. A brief description of each one of the main modules follows.Our system was tested with a driving simulator. We chose OpenDS 8 for this task, for its realistic features, its simplicity for defining driving scen

15、arios with distinctive features, and compatibility with the steering wheel / pedals / gear shift input device we used (Logitech G-Force G920 6).Figure 2: Placement of sensors and feedback system.The monitoring system consists of an Arduino Uno microcontroller board 2, and a decision algorithm runnin

16、g into a computer. For the decision logic we used WEKAs J48 algorithm 14, an open-source version of the C4.5 classification algorithm 9, 10. This logic usesFor detecting stress in the driver, we decided to use the following sensors:133Figure 1: Biometric interface architectureWork-in-ProgressAutomot

17、iveUI 18, Toronto, Canadaclassification trees to determine if the driver is stressed, according to the value of the sensor inputs.applied one at a time, and the instants in which they are applied are tagged in the results. Considering also “no task” as a baseline, this yields nine possible scenario/

18、task combinations. Each test subject drives through the nine combinations in a random order.There were 7 test subjects for this experiment. Biometric data is sampled every ten seconds.The feedback system features haptic and visual cues, to inform drivers that they are stressed: Visual feedback is do

19、ne via a NeoPixel RGB LED ring 1, mounted in the steering wheels center. Haptic feedback is provided by three vibration motors 11, mounted on the strap of the safety belt which crosses the chest.Figure 2 shows placement of the sensors and feedback system, with respect to the driver.Stress experiment

20、 2This experiment involves driving in a city setting, competing against a computer-controlled car. In the first scenario, the computer-controlled car drives slowly (low difficulty), while the second scenario features bad weather, careless drivers and pedestrians, and traffic accidents; the computer-

21、controlled car also drives faster (high difficulty). There were 6 test subjects for this experiment. Biometric data is sampled here every five seconds.Experimental designThe experiments are divided into two groups. The first group aims to induce stress in the driver, which in turn can be measured th

22、rough our system. The second group tests the efficacy of the feedback systems. Our test subjects were undergrad students, 25 years old in average.Feedback system efficacy experimentFor running this experiment, the test subject drives the high difficulty scenario described in Stress experiment1. For

23、the haptic feedback system, the driver receives several previously known vibration patterns along the drive; at the end, drivers are asked whether they were able to identify the patterns or not. For the visual feedback system, the LED ring in the steering wheel changes colors (between red, yellow an

24、d green) during the drive. At the end, users tell if they were able to see any color changes in the ring while driving. It is worth noting that vibrations and color changes are not related to stress in this experiment; we only want to know which feedback system is more noticeable to the user.Stress

25、experiment 1This experiment features three simulation scenarios: a test track without obstacles (used as a baseline); a city with moving vehicles and pedestrians (medium difficulty); and a city with more vehicles and pedestrians, and low visibility due to bad weather (high difficulty). We expect to

26、notice stress-related changes in the monitored variables of our test subjects as the difficulty of the scenario increases.The scenarios are combined with two stress-inducing tasks: 1) Receiving a phone call, not being able to answer it; 2) solving simple mathematical operations, presented and respon

27、ded to verbally. These tasks are134Work-in-ProgressAutomotiveUI 18, Toronto, CanadaResults and discussionStress experiment 1Average time for completing the driving scenarios was5.7 minutes for the first one, 9.4 minutes for the second one, and 12.3 minutes for the last one. Thus, more difficult scen

28、arios take a longer time to complete. With regard to the biometric signals, Table 1 shows that both GSR and heart rate changed during the driving tasks. Finally, the most common posture of our subjects was “well seated”, followed by “leaningforward” in a few cases.Stress experiment 2Table 3 shows re

29、sults for data classification, using new decision trees generated for each test subject. This table shows an improvement in accuracy of the decision tree, only by modifying the experiment (no hardware changes were performed). Decision tree size was also smaller (11 levels on average), leading to a m

30、ore efficient processing of data. A possible reason for this is that competitive scenarios generate greater variation in the biometric variables associated to stress.Table 1: Statistics for monitored biometricsCorrectInstancesCorrectinstancesSubjectInstancesLevelsTree sizeSubjectInstancesLevelsTree

31、size1234565145775256845114251610713711291913251221482 (93.8%)541 (93.8%)478 (91%)653 (95.5%)455 (89%)379 (89.2%)123456782768512046138678687983719401929121273377437572323619 (74.8%)556 (81.2%)1050 (87.2%)472 (77%)674 (77.7%)656 (75.6%)674 (84.5%)Table 3. Data classification for Stress experiment 2Fee

32、dback system efficacy experimentResults from these experiments show that drivers were more receptive to the visual feedback system, with an accuracy of 80.65%; while the accuracy of the vibration system was 59.52%. Figure 3 summarizes results for this experiment. Well continue to use the visual feed

33、back system, and experiment with its location, in order to find out a place allowing easy installation on a car, while being unobtrusive to the driver.Table 2. Data classification for Stress experiment 1Table 2 shows data classification for the experiment, using the J48 algorithm. The number of inst

34、ances varies according to the time spent by each subject to complete the driving tasks. Since biometrics for each test subject behave differently, it was not possible to generate a general decision tree. Results show that the algorithm was able to correctly determine whether the driver was stressed

35、or not, at least with a 74.8% accuracy. Decision trees had 24 levels, on average.Current workOur current work is currently oriented to tackle three issues. First, although the accuracy of the sensors is good, the heart rate sensor is not conveniently located, which can introduce noise to data. We ar

36、e comparingFigure 3. Comparison of visual and vibration feedback accuracy135ParameterStandard AverageDeviationGSR369122Heart rate6819.2Work-in-ProgressAutomotiveUI 18, Toronto, Canadahttp:/www.dgt.es/PEVI/documentos/catalogo_recu rsos/didacticos/did_adultas/estres.pdfOpenDS. Retrieved November 11, 2

37、017 from https:/www.opends.eu/software/featuresS. Ruggieri. Efficient C4.5 classification algorithm. In IEEE Transactions on Knowledge and Data Engineering, 14, 2, pp. 438-444, Mar/Apr 2002.S. Sathyadevan and R.R. Nair. Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest. I

38、n Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi, 2015Sparkfun. LilyPad Vibe Board. Retrieved July 13, 2018 from /products/11008Sparkfun. Load Sensor 50 kg. Retrieved July 13, 2018 from https:/www.s

39、/products/10245Alejandro P. Taddia, et al. Fortaleciendo el sector acadmico para reducir los siniestros de trnsito en Amrica Latina: Investigaciones y casos de estudio en seguridad vial. 2014. Retrieved July 13, 2018 from /handle/11319/6476University of Waikato

40、. WEKA 3: Data Mining Software in Java. Retrieved July 13, 2018 from https:/www.cs.waikato.ac.nz/ml/weka/World Health Organization. World report on road traffic injury prevention. 2004. Retrieved August 10, 2018 from /violence_injury_prevention/pu blications/road_traffic/world_report/enthe data obtained