Understanding Driving Stress in Urban Bangladesh: An Exploratory Study, Wearable Development and Experiment
Article No.: 14, Pages 1 - 28
Abstract
Driving stress significantly impacts driving behavior primarily from roadside factors, where driving is more challenging in developing countries (i.e., Bangladesh) for unique cultural and infrastructural setups. We conduct an exploratory study (Qualitative n = 26, and Subjective Feedback n = 80) and a correlational analysis involving professional and private car drivers in urban Bangladesh. The study reveals drivers' demography and driving stress factors on the road. These findings motivate us to identify driving stress from physiological factors by developing a low-cost wearable, Stress Wear. This can detect stress from varying Heart Rates, validated by expensive commercial wearables. Between subject experiments on drivers (total n = 14 in two phases) with wearables, we also found that road factors are responsible for driving stress. Therefore, the developed system is helpful for these drivers to self-sense their stress.
References
[1]
S. T. Iqbal, E. Horvitz, Y. C. Ju, and E. Mathews. 2011. Hang on a sec! Effects of proactive mediation of phone conversations while driving. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 463–472.
[2]
S. T. Iqbal, Y. C. Ju, and E. Horvitz. 2010. Cars, calls, and cognition: Investigating driving and divided attention. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 1281–1290.
[3]
C. F. Choudhury and M. E. Ben-Akiva. 2013. Modelling driving decisions: A latent plan approach. Transportmetrica A: Transport Science 9, 6 (2013), 546–566.
[4]
M. M. Islam and C. F. Choudhury. 2012. A violation behavior model for non-motorized vehicle drivers in heterogeneous traffic streams (No. 12-3192). Report on Transportation Research Board 91st Annual Meeting.
[5]
K. Mahmud, K. Gope, and S. M. R. Chowdhury. 2012. Possible causes & solutions of traffic jam and their impact on the economy of Dhaka City. J. Mgmt. & Sustainability 2 (2012), 112.
[6]
E. Gulian, A. I. Glendon, G. Matthews, D. R. Davies, and L. M. Debney. 1990. The stress of driving: A diary study. Work & Stress 4, 1 (1990), 7–16.
[7]
T. Toledo, H. N. Koutsopoulos, and M. Ben-Akiva. 2009. Estimation of an integrated driving behavior model. Transportation Research Part C: Emerging Technologies 17, 4 (2009), 365–380.
[8]
C. Miyajima, P. Angkititrakul, and K. Takeda. 2013. Behavior signal processing for vehicle applications. APSIPA Transactions on Signal and Information Processing 2 (2013).
[9]
E. Ericsson. 2001. Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment 6, 5 (2001), 325–345.
[10]
J. Van Mierlo, G. Maggetto, E. Van de Burgwal, and R. Gense. 2004. Driving style and traffic measures-influence on vehicle emissions and fuel consumption. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 218, 1 (2004), 43–50.
[11]
T. A. Ranney. 1994. Models of driving behavior: A review of their evolution. Accident Analysis & Prevention 26, 6 (1994), 733–750.
[12]
P. Bonsall, R. Liu, and W. Young. 2005. Modelling safety-related driving behaviour—Impact of parameter values. Transportation Research Part A: Policy and Practice 39, 5 (2005), 425–444.
[13]
Z. Sarsenbayeva, N. van Berkel, D. Hettiachchi, W. Jiang, T. Dingler, E. Velloso, V. Kostakos, and J. Goncalves. 2019. Measuring the effects of stress on mobile interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 1 (2019), 1–18.
[14]
P. E. Paredes, Y. Zhou, N. A. H. Hamdan, S. Balters, E. Murnane, W. Ju, and J. A. Landay. 2018. Just breathe: In-car interventions for guided slow breathing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1–23.
[15]
P. E. Paredes, S. Balters, K. Qian, E. L. Murnane, F. Ordóñez, W. Ju, and J. A. Landay. 2018. Driving with the fishes: Towards calming and mindful virtual reality experiences for the car. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 1–21.
[16]
A. Mehrotra, F. Tsapeli, R. Hendley, and M. Musolesi. 2017. MyTraces: Investigating correlation and causation between users’ emotional states and mobile phone interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1–21.
[17]
C. Feustel, S. Aggarwal, B. Lee, and L. Wilcox. 2018. People like me: Designing for reflection on aggregate cohort data in personal informatics systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1–21.
[18]
J. A. Healey and R. W. Picard. 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6, 2 (2005), 156–166.
[19]
X. Zhang, Y. Lyu, X. Luo, J. Zhang, C. Yu, H. Yin, and Y. Shi. 2018. Touch sense: Touch screen based mental stress sense. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2 (2018), 1–18.
[20]
Bangladesh: Alarming rise in road crashes. 2020. https://www.aa.com.tr/en/asia-pacific/bangladesh-alarming-rise-in-road-crashes/1692643
[21]
N. Ahmed, R. J. Rony, M. T. Mushfique, M. M. Rahman, N. E. S. Tahsin, S. Azad, S. Raiyan, S. Al Hasan, S. S. Khan, P. A. D'Costa, and S. A. U. Rahman. 2019. Ambient intelligence in systems to support wellbeing of drivers. Guide to Ambient Intelligence in the IoT Environment: Principles, Technologies and Applications, 217--247.
[22]
E. Gulian, G. Matthews, A. I. Glendon, D. R. Davies, and L. M. Debney. 1989. Dimensions of driver stress. Ergonomics 32, 6 (1989), 585–602.
[23]
E. Paschalidis, C. F. Choudhury, and S. Hess. 2018. Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors. Transportation Research Part F: Traffic Psychology and Behaviour 59, 418–435.
[24]
E. De Valck, E. De Groot, and R. Cluydts. 2003. Effects of slow-release caffeine and a nap on driving simulator performance after partial sleep deprivation. Perceptual and Motor Skills 96, 1 (2003), 67–78.
[25]
T. L. Webb, P. Sheeran, P. Totterdell, E. Miles, W. Mansell, and S. Baker. 2012. Using implementation intentions to overcome the effect of mood on risky behaviour. British Journal of Social Psychology 51, 2 (2012), 330–345.
[26]
T. Lajunen and H. Summala. 1995. Driving experience, personality, and skill and safety-motive dimensions in drivers' self-assessments. Personality and Individual Differences 19, 3 (1995), 307–318.
[27]
A. T. McCartt, V. I. Shabanova, and W. A. Leaf. 2003. Driving experience, crashes and traffic citations of teenage beginning drivers. Accident Analysis & Prevention 35, 3 (2003), 311–320.
[28]
J. Cestac, F. Paran, and P. Delhomme. 2011. Young drivers’ sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention: How risk-taking motivations evolve with gender and driving experience. Safety Science 49, 3 (2011), 424–432.
[29]
Elite HRV Academy. 2021. Accessed from, https://www.hrvcourse.com/heart-rate-variability-vs-heart-rate/
[30]
A. J. Camm, M. Malik, J. T. Bigger, G. Breithardt, S. Cerutti, R. J. Cohen, P. Coumel, E.L. Fallen, H. L. Kennedy, R. E. Kleiger, and F. Lombardi. 1996. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology.
[31]
Heartrate variability- How to analyze ECG data. 2021. Accessed from, https://imotions.com/blog/heart-rate-variability/
[32]
M. N. Rastgoo, B. Nakisa, A. Rakotonirainy, V. Chandran, and D. Tjondronegoro. 2018. A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys (CSUR) 51, 5 (2018), 1–35.
[33]
A. Zafiroglu, J. Healey, and T. Plowman. 2012. Navigation to multiple local transportation futures: Cross-interrogating remembered and recorded drives. In Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 139–146.
[34]
R. J. Rony and N. Ahmed. 2020. Development of a low cost wearable with mobile sensing to monitor driving stress through HRV. In Proceedings of the 2020 Symposium on Emerging Research from Asia and on Asian Contexts and Cultures. 37–40.
[35]
I. M. Jonsson, C. Nass, J. Endo, B. Reaves, H. Harris, J. L. Ta, N. Chan, and S. Knapp. 2004. Don't blame me I am only the driver: Impact of blame attribution on attitudes and attention to driving task. In CHI'04 Extended Abstracts on Human Factors in Computing Systems. 1219–1222.
[36]
T. E. Boyce and E. S. Geller. 2002. An instrumented vehicle assessment of problem behavior and driving style: Do younger males really take more risks?. Accident Analysis & Prevention 34, 1 (2002), 51–64.
[37]
N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. 2010. A survey of mobile phone sensing. IEEE Communications Magazine 48, 9 (2010), 140–150.
[38]
R. Sen, B. Raman, and P. Sharma. 2010. Horn-OK-Please. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. 137–150.
[39]
V. Douangphachanh and H. Oneyama. 2014. A study on the use of smartphones under realistic settings to estimate road roughness condition. EURASIP Journal on Wireless Communications and Networking 2014, 1 (2014), 114.
[40]
J. Stutts, J. Feaganes, D. Reinfurt, E. Rodgman, C. Hamlett, K. Gish, and L. Staplin. 2005. Driver's exposure to distractions in their natural driving environment. Accident Analysis & Prevention 37, 6 (2005), 1093–1101.
[41]
A. Razzaque. Last accessed July 2015. Driver distraction management using sensor data cloud. http://driverdistraction.gnrbd.net/
[42]
S. Taamneh, P. Tsiamyrtzis, M. Dcosta, P. Buddharaju, A. Khatri, M. Manser, T. Ferris, R. Wunderlich, and I. Pavlidis. 2017. A multimodal dataset for various forms of distracted driving. Scientific Data 4, 1 (2017), 1–21.
[43]
C. D. Katsis, G. Rigas, Y. Goletsis, and D. I. Fotiadis. 2015. Emotion recognition in car industry. Emotion Recognition: A Pattern Analysis Approach. 515–544.
[44]
5 kills as reckless driving on. 2018. Accessed from, https://www.thedailystar.net/city/angry-mob-sets-bus-ablaze-it-runs-over-youth-one-killed-dhaka-road-accident-1615435
[45]
Bus hits home ministers car, helper was driving. 2018. Accessed from, https://www.thedailystar.net/news/city/bus-hits-home-minister-car-helper-was-driving-bangladesh-traffic-week-2018-1619212
[46]
K. Soman, V. Alex, and C. Srinivas. 2013. Analysis of physiological signals in response to stress using ECG and respiratory signals of automobile drivers. In 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s). IEEE, 574–579.
[47]
Students stop police cars, drivers fails to show license. 2018. Accessed from, https://bdnews24.com/bangladesh/2018/08/01/students-stop-police-car-driver-fails-to-show-licence
[48]
Bus driver who lost his daughter to road crash calls for traffic discipline. 2018. Accessed from, https://bdnews24.com/bangladesh/2018/08/01/bus-driver-who-lost-his-daughter-to-road-crash-calls-for-traffic-discipline,
[49]
https://www.bbc.com/news/world-asia-45080129, Accessed on January, 2018
[50]
Fitbit Charge 2: Activity Tracker. https://www.amazon.in/Fitbit-Wireless-Activity-TrackerWristband/dp/B01K9S260E/ref=sr_1_1?s=hpc&ie=UTF8&qid=1537101253&sr=1-1&keywords=fitbit. Accessed January, 2020.
[51]
V. Mishra, G. Pope, S. Lord, S. Lewia, B. Lowens, K. Caine, S. Sen, R. Halter, and D. Kotz. 2018. The case for a commodity hardware solution for stress detection. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1717–1728.
[52]
Bangladesh: Road traffic accidents. 2018. Accessed from, https://www.daily-sun.com/printversion/details/327901/2018/08/08/Bangladesh:-Road-TrafficAccidents
[53]
Killed in 3 years. 2018. Accessed from, https://en.prothomalo.com/bangladesh/news/181012/25-120-killed-on-roads-in-3-years
[54]
‘Ghosts’ driving 10 lakh vehicles. 2017. Accessed from, https://www.thedailystar.net/frontpage/ghosts-driving-10-lakh-vehicles-1371037
[55]
Study: Bangladesh has second worst roads in Asia. 2018. Accessed from, https://www.dhakatribune.com/what-the-world-says/2018/02/28/study-bangladeshsecond-worst-roads-asia
[56]
The Global Competitiveness Report 2017-2018. World Economic Forum. ISBN-13: 978-1-944835-11-8.
[57]
Teenagers bring parts of Bangladesh to a halt with bus death protests. 2018. Accessed from, https://www.theguardian.com/world/2018/aug/03/teenagersbring-parts-of-bangladesh-to-a-halt-with-bus-death-protests
[58]
Dhaka Population. 2018. Accessed from, http://worldpopulationreview.com/world-cities/dhaka-population/
[59]
Dhakatribune.com. 2013. Congestion costs Tk200bn every year: Survey. http://www.dhakatribune.com/bangladesh/2013/jul/14/congestion-coststk200bn-every-year-survey
[60]
R. J. Rony and N. Ahmed. 2019. Understanding drivers wellbeing: Quantitative study analysis and wearable experiment. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 1170–1173.
[61]
R. J. Rony. 2018. How well our drivers on road? Discomfort is obvious in drivers daily life. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 1754–1756.
[62]
[63]
M. J. Caruso and L. S. Withanawasam. 1999. Vehicle detection and compass applications using AMR magnetic sensors. In Sensors Expo Proceedings 477 (1999), p. 39.
[64]
V. Markevicius, D. Navikas, M. Zilys, D. Andriukaitis, A. Valinevicius, and M. Cepenas. 2016. Dynamic vehicle detection via the use of magnetic field sensors. Sensors 16, 1 (2016), 78.
[65]
Rotoview. Accessed from, https://www.rotoview.com/magnetometer.htm
[66]
T. Chandola, E. Brunner, and M. Marmot. 2006. Chronic stress at work and the metabolic syndrome: Prospective study. BMJ 332, 7540 (2006), 521–525.
[67]
A. L. Wheat and K. T. Larkin. 2010. Biofeedback of heart rate variability and related physiology: A critical review. Applied Psychophysiology and Biofeedback 35, 3 (2010), 229–242.
[68]
B. M. Appelhans and L. J. Luecken. 2006. Heart rate variability as an index of regulated emotional responding. Review of General Psychology 10, 3 (2006), 229–240.
[69]
B. Yu, J. Hu, M. Funk, and L. Feijs. 2018. DeLight: Biofeedback through ambient light for stress intervention and relaxation assistance. Personal and Ubiquitous Computing 22, 4 (2018), 787–805.
[70]
S. Moosavi, B. Omidvar-Tehrani, R. B. Craig, A. Nandi, and R. Ramnath. 2017. Characterizing driving context from driver behavior. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 1–4.
[71]
Cardiovascular disease. 2020. http://www.who.int/cardiovascular_diseases/global-hearts/Global_hearts_initiative/en/
[72]
C. Wang, L. Xie, W. Wang, Y. Chen, Y. Bu, and S. Lu. 2018. RF-ECG: Heart rate variability assessment based on COTS RFID tag array. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2 (2018), 1–26.
[73]
R. Ma and D. B. Kaber. 2007. Situation awareness and driving performance in a simulated navigation task. Ergonomics 50, 8 (2007), 1351–1364.
[74]
A. H. Chan and K. Chen. 2011. Driving Aptitude Test (DAT): A new set of aptitude tests for occupational drivers. In International MultiConference of Engineers and Computer Scientists (2011), IMECS 2011. 1322–1326.
[75]
A. Bonde, S. Pan, Z. Jia, Y. Zhang, H. Y. Noh, and P. Zhang. 2018. VVRRM: Vehicular vibration-based heart RR-interval monitoring system. In Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications. 37–42.
[76]
H. Cohen, M. Kotler, M. A. Matar, Z. Kaplan, U. Loewenthal, H. Miodownik, and Y. Cassuto. 1998. Analysis of heart rate variability in posttraumatic stress disorder patients in response to a trauma-related reminder. Biological Psychiatry 44, 10 (1998), 1054–1059.
[77]
M. Pagani, R. Furlan, P. Pizzinelli, W. Crivellaro, S. Cerutti, and A. Malliani. 1989. Spectral analysis of RR and arterial pressure variabilities to assess sympatho-vagal interaction during mental stress in humans. Journal of Hypertension 7 (1989), S14–15.
[78]
C. Hwang and S. Pushp. 2018. A mobile system for investigating the user's stress causes in daily life. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. 66–69.
[79]
K. Erickson, M. McMahon, L. E. Dunne, C. Larsen, B. Olmstead, and J. Hipp. 2016. Design and analysis of a sensor-enabled in-ear device for physiological monitoring. Journal of Medical Devices 10, 2 (2016).
[80]
R. McCraty, M. Atkinson, W. A. Tiller, G. Rein, and A. D. Watkins. 1995. The effects of emotions on short-term power spectrum analysis of heart rate variability. American Journal of Cardiology 76, 14 (1995), 1089–1093.
[81]
G. Deininger and G. Loudon. 2011. Correlation between coherent heart rate variability and divergent thinking. In Proceedings of the 8th ACM Conference on Creativity and Cognition. 329–330.
[82]
B. Yu, M. Funk, J. Hu, and L. Feijs. 2017. StressTree: A metaphorical visualization for biofeedback-assisted stress management. In Proceedings of the 2017 Conference on Designing Interactive Systems. 333–337.
[83]
Y. Lyu, X. Luo, J. Zhou, C. Yu, C. Miao, T. Wang, Y. Shi, and K. I. Kameyama. 2015. Measuring photoplethysmogram-based stress-induced vascular response index to assess cognitive load and stress. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 857–866.
[84]
M. Manseer and A. Riener. 2014. Evaluation of driver stress while transiting road tunnels. In Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 1–6.
[85]
S. Vhaduri, A. Ali, M. Sharmin, K. Hovsepian, and S. Kumar. 2014. Estimating drivers' stress from GPS traces. In Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 1–8.
[86]
A. Benoit, L. Bonnaud, A. Caplier, P. Ngo, L. Lawson, D. G. Trevisan, V. Levacic, C. Mancas, and G. Chanel. 2009. Multimodal focus attention and stress detection and feedback in an augmented driver simulator. Personal and Ubiquitous Computing 13, 1 (2009), 33–41.
[87]
D. A. Hennessy, D. L. Wiesenthal, and P. M. Kohn. 2000. The influence of traffic congestion, daily hassles, and trait stress susceptibility on state driver stress: An interactive perspective 1. Journal of Applied Biobehavioral Research 5, 2 (2000), 162–179.
[88]
G. Matthews. 1996. Individual differences in driver stress and performance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 40, No. 12, pp. 579–583). SAGE Publications, Los Angeles, CA.
[89]
A. Riener, A. Ferscha, and M. Aly. 2009. Heart on the road: HRV analysis for monitoring a driver's affective state. In Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 99–106.
[90]
D. MacLean, A. Roseway, and M. Czerwinski. 2013. MoodWings: A wearable biofeedback device for real-time stress intervention. In Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments. 1–8.
[91]
J. Hernandez, D. McDuff, X. Benavides, J. Amores, P. Maes, and R. Picard. 2014. AutoEmotive: Bringing empathy to the driving experience to manage stress. In Proceedings of the 2014 Companion Publication on Designing Interactive Systems. 53–56.
[92]
R. J. Rony and N. Ahmed. 2019. Monitoring driving stress using HRV. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS). IEEE, 417–419.
[93]
N. Ahmed and R. J. Rony. 2021. Understanding self-reported stress among drivers and designing stress monitor using heart rate variability. Quality and User Experience 6, 1 (2021), 1–21.
[94]
H. Storm. 2000. Skin conductance and the stress response from heel stick in preterm infants. Archives of Disease in Childhood-Fetal and Neonatal Edition 83, 2 (2000), F143–F147.
[95]
P. H. Venables and M. J. Christie. 1980. Electrodermal activity. Techniques in Psychophysiology 54, 3 (1980).
[96]
R. Edelberg. 1967. Electrical properties of the skin. In Brown CC, (Ed). Methods in Psychophysiology. Williams Wilkin, Baltimore (1967), 1–53.
[97]
G. Bini, K. E. Hagbarth, P. T. Hynninen, and B. G. Wallin. 1980. Thermoregulatory and rhythm-generating mechanisms governing the sudomotor and vasoconstrictor outflow in human cutaneous nerves. The Journal of Physiology 306, 1 (1980), 537–552.
[98]
H. Storm, K. Myre, M. Rostrup, O. Stokland, M. D. Lien, and J. C. Raeder. 2002. Skin conductance correlates with perioperative stress. Acta Anaesthesiologica Scandinavica 46, 7 (2002), 887–895.
[99]
Are drivers alone responsible for road accidents in Bangladesh? 2018. Accessed from, https://www.dhakatribune.com/bangladesh/2018/04/24/drivers-aloneresponsible-road-accidents-bangladesh/
[100]
H. P. Lin, H. Y. Lin, W. L. Lin, and A. C. W. Huang. 2011. Effects of stress, depression, and their interaction on heart rate, skin conductance, finger temperature, and respiratory rate: Sympathetic-parasympathetic hypothesis of stress and depression. Journal of Clinical Psychology 67, 10 (2011), 1080–1091.
[101]
D. C. Fowles, M. J. Christie, R. Edelberg, W. W. Grings, D. T. Lykken, and P. H. Venables. 1981. Committee report. Publication recommendations for electrodermal measurements. Psychophysiology 18 (1981), 232–239
[102]
Empatica. 2020. Accessed from, https://support.empatica.com/hc/en-us/articles/202581999-E4-wristband-technical-specifications
[103]
V. Braun and V. Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101.
[104]
Seeedstudio. 2020. Accessed from, https://wiki.seeedstudio.com/Grove-Ear-clip_Heart_Rate_Sensor/#projects
[105]
Thomas Certification. 2020. Accessed from, https://certifications.thomasnet.com/certifications/glossary/other-certification_registration/european-commission/rohs-compliant/
[106]
F. Shahid, W. Rahman, A. B. Islam, N. Paul, N. Khan, M. S. Rahman, M. M. Haque, and A. A. Al Islam. 2019. Two tell-tale perspectives of PTSD: Neurobiological abnormalities and Bayesian regulatory network of the underlying disorder in a refugee context. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1–45.
[107]
A. Alabdulgade, R. Maccraty, M. Atkinson, A. Vainoras, K. Berškienė, V. Mauricienė, A. Daunoraviciene, Z. Navickas, R. Smidtaite, and M. Landauskas. 2015. Human heart rhythm sensitivity to earth local magnetic field fluctuations. Journal of Vibroengineering 17, 6 (2015), 3271–3278.
[108]
C. V. Jawahar and V. N. Padmanabhan. 2019. Technology interventions for road safety and beyond. Communications of the ACM 62, 11 (2019), 58–59.
[109]
R. Sen, A. Cross, A. Vashistha, V. N. Padmanabhan, E. Cutrell, and W. Thies. 2013. Accurate speed and density measurement for road traffic in India. In Proceedings of the 3rd ACM Symposium on Computing for Development. 1–10.
[110]
M. M. Islam and C. F. Choudhury. 2012. A violation behavior model for non-motorized vehicle drivers in heterogeneous traffic streams (No. 12-3192).
[111]
I. Dua, A. U. Nambi, C. V. Jawahar, and V. N. Padmanabhan. 2019. Evaluation and visualization of driver inattention rating from facial features. IEEE Transactions on Biometrics, Behavior, and Identity Science 2, 2 (2019), 98–108.
[112]
World Bank report. 2020. Accessed from, http://databank.worldbank.org/data/reports.aspx?source=poverty-and-equity-database
[113]
J. Reason, A. Manstead, S. Stradling, J. Baxter, and K. Campbell. 1990. Errors and violations on the roads: A real distinction?. Ergonomics 33, 10-11 (1990), 1315–1332.
[114]
Automated traffic signal: Money thrown down the drain. 2020. Accessed from, https://www.thedailystar.net/news/bangladesh/news/automated-traffic-signal-money-thrown-down-the-drain-2939261
[115]
S. Misra, O. Osogba, and M. Powers. 2019. Unsupervised outlier detection techniques for well logs and geophysical data. Mach. Learn. Subsurface Charact., 1.
[116]
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, and S. J. Van Der Walt. 2020. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods 17, 3 (2020), 261–272.
[117]
Khalid K. Al-jabery, Tayo Obafemi-Ajayi, Gayla R. Olbricht, and Donald C. Wunsch II. 2020. Evaluation of cluster validation metrics. Computational Learning Approaches to Data Analytics in Biomedical Applications. 189–208.
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June 2024
421 pages
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Online AM: 14 February 2024
Accepted: 01 January 2024
Revised: 21 July 2023
Received: 14 February 2023
Published in ACMJCSS Volume 2, Issue 2
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