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AKILLI TELEFON VERİLERİ VE MAKİNE ÖĞRENMESİ YÖNTEMLERİ KULLANILARAK STRES TESPİTİ ÇALIŞMALARI ÜZERİNE BİR LİTERATÜR ARAŞTIRMASI

Year 2021, Volume 9, Issue 3, 1030 - 1038, 21.09.2021
https://doi.org/10.21923/jesd.790845

Abstract

Stres, genel olarak olumsuz etkilere sahip bir süreçtir. Bu olumsuz etkileri en aza indirmek için erken tespit edilmesi önemlidir. Buna bağlı olarak stresin tespit edilmesi bir sınıflandırma problemi olarak ele alınabilir. Stres, fizyolojik ve davranışsal veriler kullanılarak tespit edilebilmektedir. Bu çalışmada, sadece akıllı telefon verileri kullanılarak gerçekleştirilen stres tespiti çalışmaları ele alınmış ve stres tespitinde kullanılan veri kaynakları, veri türleri ve sınıflandırmada kullanılan makine öğrenmesi yöntemleri incelenmiştir. Bu çalışmalar kendi içerisinde veri kaynaklarına göre beş başlık altında incelenmiştir. Araştırma sonucunda akıllı telefon uygulamaları, hareket algılayıcıları, arama ve mesaj atma sıklığı gibi bilgilerin stresin tespitinde önemli bir yer tuttuğu görülmüştür.

References

  • Ballı, S., & Sağbaş, E. A. (2017). Akıllı saat algılayıcıları ile insan hareketlerinin sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(3), 980-990.
  • Ballı, S., & Sağbaş, E. A. (2018). Diagnosis of transportation modes on mobile phone using logistic regression classification. IET Software, 12(2), 142-151.
  • Bauer, G., & Lukowicz, P. (2012, March). Can smartphones detect stress-related changes in the behaviour of individuals?. In 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (pp. 423-426). IEEE.
  • Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014, November). Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 477-486).
  • Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of biomedical informatics, 92, 103139.
  • Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2011). Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE transactions on information technology in biomedicine, 16(2), 279-286.
  • Ciman, M., Wac, K., & Gaggi, O. (2015, May). iSenseStress: Assessing stress through human-smartphone interaction analysis. In 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 84-91). IEEE.
  • Ciman, M., & Wac, K. (2016). Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Transactions on Affective Computing, 9(1), 51-65.
  • Ferdous, R., Osmani, V., & Mayora, O. (2015, May). Smartphone app usage as a predictor of perceived stress levels at workplace. In 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 225-228). IEEE.
  • Gao, Y., Bianchi-Berthouze, N., & Meng, H. (2012). What does touch tell us about emotions in touchscreen-based gameplay?. ACM Transactions on Computer-Human Interaction (TOCHI), 19(4), 1-30.
  • Garcia-Ceja, E., Osmani, V., & Mayora, O. (2015). Automatic stress detection in working environments from smartphones’ accelerometer data: a first step. IEEE journal of biomedical and health informatics, 20(4), 1053-1060.
  • Garje, G. V., Inamdar, A., & Mahajan, H. (2016). Stress Detection and Sentiment Prediction: A Survey. Internation Journal of Engineering Applied Science and Technology, (2).
  • Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2017, September). Tapsense: Combining self-report patterns and typing characteristics for smartphone based emotion detection. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 1-12).
  • Ghosh, S., Sahu, S., Ganguly, N., Mitra, B., & De, P. (2019, January). EmoKey: An emotion-aware smartphone keyboard for mental health monitoring. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 496-499). IEEE.
  • Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2019). Review on psychological stress detection using biosignals. IEEE Transactions on Affective Computing.
  • Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2015, July). Automatic detection of perceived stress in campus students using smartphones. In 2015 International Conference on Intelligent Environments (pp. 132-135). IEEE.
  • Greene, S., Thapliyal, H., & Caban-Holt, A. (2016). A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Consumer Electronics Magazine, 5(4), 44-56.
  • Kim, H. J., & Choi, Y. S. (2012, January). Exploring emotional preference for smartphone applications. In 2012 IEEE consumer communications and networking conference (CCNC) (pp. 245-249). IEEE.
  • Lee, H., Choi, Y. S., Lee, S., & Park, I. P. (2012, January). Towards unobtrusive emotion recognition for affective social communication. In 2012 IEEE Consumer Communications and Networking Conference (CCNC) (pp. 260-264). IEEE.
  • Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., ... & Choudhury, T. (2012, September). Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM conference on ubiquitous computing (pp. 351-360).
  • Muaremi, A., Arnrich, B., & Tröster, G. (2013). Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience, 3(2), 172-183.
  • Panicker, S. S., & Gayathri, P. (2019). A survey of machine learning techniques in physiology based mental stress detection systems. Biocybernetics and Biomedical Engineering, 39(2), 444-469.
  • Panure, T., & Sonawani, S. (2019). Stress Detection Using Smartphone and Wearable Devices: A Review. Asian Journal For Convergence In Technology (AJCT).
  • Rastgoo, M. N., Nakisa, B., Rakotonirainy, A., Chandran, V., & Tjondronegoro, D. (2018). A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys (CSUR), 51(5), 1-35.
  • Sağbaş, E. A., & Ballı, S. (2015). Akıllı Telefon Sensörlerinin Kullanımı ve Ham Sensör Verilerine Erişim. Akademik Bilişim Konferansı, 4-6 Şubat 2015, Eskişehir, Türkiye. 180-186.
  • Sağbaş, E. A., Ballı, S., & Yıldız, T. (2016). Giyilebilir Akıllı Cihazlar: Dünü, Bugünü ve Geleceği. Akademik Bilişim Konferansı. 30 Ocak - 5 Şubat 2016. Aydın, Türkiye. 749-756.
  • Sağbaş, E. A., Korukoglu, S., & Balli, S. (2020). Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques. Journal of Medical Systems, 44(4), 1-12.
  • Sharma, N., & Gedeon, T. (2012). Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer methods and programs in biomedicine, 108(3), 1287-1301.
  • Stütz, T., Kowar, T., Kager, M., Tiefengrabner, M., Stuppner, M., Blechert, J., ... & Ginzinger, S. (2015, June). Smartphone based stress prediction. In International Conference on User Modeling, Adaptation, and Personalization (pp. 240-251). Springer, Cham.
  • Sysoev, M., Kos, A., & Pogačnik, M. (2015). Noninvasive stress recognition considering the current activity. Personal and Ubiquitous Computing, 19(7), 1045-1052.
  • Thapliyal, H., Khalus, V., & Labrado, C. (2017). Stress detection and management: A survey of wearable smart health devices. IEEE Consumer Electronics Magazine, 6(4), 64-69.
  • Vildjiounaite, E., Kallio, J., Kyllönen, V., Nieminen, M., Määttänen, I., Lindholm, M., ... & Gimel’farb, G. (2018). Unobtrusive stress detection on the basis of smartphone usage data. Personal and Ubiquitous Computing, 22(4), 671-688.
  • Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., ... & Campbell, A. T. (2014, September). StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 3-14).

A REVIEW OF LITERATURE ON STRESS DETECTION STUDIES USING SMARTPHONE DATA AND MACHINE LEARNING METHODS

Year 2021, Volume 9, Issue 3, 1030 - 1038, 21.09.2021
https://doi.org/10.21923/jesd.790845

Abstract

Stress is a process that generally has negative effects. It is important to be detected early to minimize these negative effects. Accordingly, the detection of stress can be considered as a classification problem. Stress can be detected using physiological and behavioral data. In this study, stress detection studies using only smartphone data were discussed and data sources, data types, and machine learning methods using classification were examined. These studies were handled under five headings according to their data sources. As a result of the research, it has been seen that information such as smartphone applications, motion sensors, frequency of calling and texting has an important place in the detection of stress.

References

  • Ballı, S., & Sağbaş, E. A. (2017). Akıllı saat algılayıcıları ile insan hareketlerinin sınıflandırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(3), 980-990.
  • Ballı, S., & Sağbaş, E. A. (2018). Diagnosis of transportation modes on mobile phone using logistic regression classification. IET Software, 12(2), 142-151.
  • Bauer, G., & Lukowicz, P. (2012, March). Can smartphones detect stress-related changes in the behaviour of individuals?. In 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (pp. 423-426). IEEE.
  • Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014, November). Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 477-486).
  • Can, Y. S., Arnrich, B., & Ersoy, C. (2019). Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. Journal of biomedical informatics, 92, 103139.
  • Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2011). Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE transactions on information technology in biomedicine, 16(2), 279-286.
  • Ciman, M., Wac, K., & Gaggi, O. (2015, May). iSenseStress: Assessing stress through human-smartphone interaction analysis. In 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 84-91). IEEE.
  • Ciman, M., & Wac, K. (2016). Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Transactions on Affective Computing, 9(1), 51-65.
  • Ferdous, R., Osmani, V., & Mayora, O. (2015, May). Smartphone app usage as a predictor of perceived stress levels at workplace. In 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (pp. 225-228). IEEE.
  • Gao, Y., Bianchi-Berthouze, N., & Meng, H. (2012). What does touch tell us about emotions in touchscreen-based gameplay?. ACM Transactions on Computer-Human Interaction (TOCHI), 19(4), 1-30.
  • Garcia-Ceja, E., Osmani, V., & Mayora, O. (2015). Automatic stress detection in working environments from smartphones’ accelerometer data: a first step. IEEE journal of biomedical and health informatics, 20(4), 1053-1060.
  • Garje, G. V., Inamdar, A., & Mahajan, H. (2016). Stress Detection and Sentiment Prediction: A Survey. Internation Journal of Engineering Applied Science and Technology, (2).
  • Ghosh, S., Ganguly, N., Mitra, B., & De, P. (2017, September). Tapsense: Combining self-report patterns and typing characteristics for smartphone based emotion detection. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 1-12).
  • Ghosh, S., Sahu, S., Ganguly, N., Mitra, B., & De, P. (2019, January). EmoKey: An emotion-aware smartphone keyboard for mental health monitoring. In 2019 11th International Conference on Communication Systems & Networks (COMSNETS) (pp. 496-499). IEEE.
  • Giannakakis, G., Grigoriadis, D., Giannakaki, K., Simantiraki, O., Roniotis, A., & Tsiknakis, M. (2019). Review on psychological stress detection using biosignals. IEEE Transactions on Affective Computing.
  • Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2015, July). Automatic detection of perceived stress in campus students using smartphones. In 2015 International Conference on Intelligent Environments (pp. 132-135). IEEE.
  • Greene, S., Thapliyal, H., & Caban-Holt, A. (2016). A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Consumer Electronics Magazine, 5(4), 44-56.
  • Kim, H. J., & Choi, Y. S. (2012, January). Exploring emotional preference for smartphone applications. In 2012 IEEE consumer communications and networking conference (CCNC) (pp. 245-249). IEEE.
  • Lee, H., Choi, Y. S., Lee, S., & Park, I. P. (2012, January). Towards unobtrusive emotion recognition for affective social communication. In 2012 IEEE Consumer Communications and Networking Conference (CCNC) (pp. 260-264). IEEE.
  • Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., ... & Choudhury, T. (2012, September). Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM conference on ubiquitous computing (pp. 351-360).
  • Muaremi, A., Arnrich, B., & Tröster, G. (2013). Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience, 3(2), 172-183.
  • Panicker, S. S., & Gayathri, P. (2019). A survey of machine learning techniques in physiology based mental stress detection systems. Biocybernetics and Biomedical Engineering, 39(2), 444-469.
  • Panure, T., & Sonawani, S. (2019). Stress Detection Using Smartphone and Wearable Devices: A Review. Asian Journal For Convergence In Technology (AJCT).
  • Rastgoo, M. N., Nakisa, B., Rakotonirainy, A., Chandran, V., & Tjondronegoro, D. (2018). A critical review of proactive detection of driver stress levels based on multimodal measurements. ACM Computing Surveys (CSUR), 51(5), 1-35.
  • Sağbaş, E. A., & Ballı, S. (2015). Akıllı Telefon Sensörlerinin Kullanımı ve Ham Sensör Verilerine Erişim. Akademik Bilişim Konferansı, 4-6 Şubat 2015, Eskişehir, Türkiye. 180-186.
  • Sağbaş, E. A., Ballı, S., & Yıldız, T. (2016). Giyilebilir Akıllı Cihazlar: Dünü, Bugünü ve Geleceği. Akademik Bilişim Konferansı. 30 Ocak - 5 Şubat 2016. Aydın, Türkiye. 749-756.
  • Sağbaş, E. A., Korukoglu, S., & Balli, S. (2020). Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques. Journal of Medical Systems, 44(4), 1-12.
  • Sharma, N., & Gedeon, T. (2012). Objective measures, sensors and computational techniques for stress recognition and classification: A survey. Computer methods and programs in biomedicine, 108(3), 1287-1301.
  • Stütz, T., Kowar, T., Kager, M., Tiefengrabner, M., Stuppner, M., Blechert, J., ... & Ginzinger, S. (2015, June). Smartphone based stress prediction. In International Conference on User Modeling, Adaptation, and Personalization (pp. 240-251). Springer, Cham.
  • Sysoev, M., Kos, A., & Pogačnik, M. (2015). Noninvasive stress recognition considering the current activity. Personal and Ubiquitous Computing, 19(7), 1045-1052.
  • Thapliyal, H., Khalus, V., & Labrado, C. (2017). Stress detection and management: A survey of wearable smart health devices. IEEE Consumer Electronics Magazine, 6(4), 64-69.
  • Vildjiounaite, E., Kallio, J., Kyllönen, V., Nieminen, M., Määttänen, I., Lindholm, M., ... & Gimel’farb, G. (2018). Unobtrusive stress detection on the basis of smartphone usage data. Personal and Ubiquitous Computing, 22(4), 671-688.
  • Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., ... & Campbell, A. T. (2014, September). StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 3-14).

Details

Primary Language Turkish
Subjects Computer Science, Information System
Published Date Güz
Journal Section Review Articles
Authors

Ensar Arif SAĞBAŞ (Primary Author)
MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ
0000-0002-7463-1150
Türkiye


Serdar KORUKOĞLU
EGE ÜNİVERSİTESİ
0000-0002-4230-8447
Türkiye


Serkan BALLI
MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ
0000-0002-4825-139X
Türkiye

Publication Date September 21, 2021
Application Date September 5, 2020
Acceptance Date June 9, 2021
Published in Issue Year 2021, Volume 9, Issue 3

Cite

APA Sağbaş, E. A. , Korukoğlu, S. & Ballı, S. (2021). AKILLI TELEFON VERİLERİ VE MAKİNE ÖĞRENMESİ YÖNTEMLERİ KULLANILARAK STRES TESPİTİ ÇALIŞMALARI ÜZERİNE BİR LİTERATÜR ARAŞTIRMASI . Mühendislik Bilimleri ve Tasarım Dergisi , 9 (3) , 1030-1038 . DOI: 10.21923/jesd.790845