APPLICATION OF THERMAL IMAGING CAMERAS FOR SMARTPHONE: SEEK THERMAL COMPACT PRO AND FLIR ONE PRO FOR HUMAN STRESS DETECTION – COMPARISON AND STUDY
Abstract
Thermography as an innovative diagnostic technique with non-contact temperature measurement is used in many industries – science, industry, medicine, and security. When using thermography in the field of health, images and images sequences obtained from thermal imaging cameras allow to record the temperature distribution in order to further recognize whether the state of the body is consistent with the defined parameters or whether there are deviations. However, it is worth paying attention to the measurement accuracy of thermal imaging cameras, their specification, and image quality of thermograms. In the case of recording stress states, measurement discrepancies between thermal imaging cameras for smartphone may affect the final results. Therefore, this article focuses on the comparison of the possibility of recording and detecting stress using two smartphone thermal imaging cameras: SEEK THERMAL Compact Pro and FLIR ONE Pro. The specifications of both cameras were compared. At the same time, the possibility of recording stress using smartphone thermal imaging cameras was confirmed on the basis of an exemplary study. The results of the comparison and analysis show that smartphone thermography can be a quick registration and diagnostic method in behavioral-biomedical issues.
Keywords:
thermal cameras, stress detection, bioinformatics, thermal imaging, thermal videoReferences
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Anishchenko, L., & Turetzkaya, A. (2020). Improved Non-Contact Mental Stress Detection via Bioradar. 2020 International Conference on Biomedical Innovations and Applications (BIA). https://doi.org/10.1109/bia50171.2020.924492
Anusha, A., Padmaja, N., D.V.S, M., & Kumar, B.S. (2020). IOT Based Stress Detection and Health Monitoring System. HELIX, 10((2). 161-167. https://doi.org/10.29042/2020-10-2-161-164
Bara C. P., Papakostas M., Mihalcea R. (2020). A Deep Learning Approach Towards Multimodal Stress Detection. Proceedings of the AAAI-20 Workshop on Affective Content Analysis, 2020, New York, USA.
Baran K. (2021). Stress detection and monitoring based on low-cost mobile thermography. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES 2021; Procedia Computer Science, 192, 1102-1110. https://doi.org/10.1016/j.procs.2021.08.113
Baran K. (2021). Thermal Imaging of Stress: A Review. Computational Intelligence, Information Systems and Data Mining. 2021; 95-113.
Bogomilsky, S., Hoffer, O., Shalmon, G., & Scheinowitz, M. (2022). Preliminary study of thermal density distribution and entropy analysis during cycling exercise stress test using infrared thermography. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-18233-5
Campbell J.S., Mead M.N. (2022). Human Medical Thermography. CRC Press. https://doi.org/10.1201/9781003281764
Cardone, D., Perpetuini, D., Filippini, C., Spadolini, E., Mancini, L., Chiarelli, A. M., & Merla, A. (2020). Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal. Applied Sciences, 10(16), 5673. https://doi.org/10.3390/app10165673
Cho, Y., Bianchi-Berthouze, N., & Julier, S. J. (2017). DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). https://doi.org/10.1109/acii.2017.8273639
Cho, Y., Julier, S. J., Marquardt, N., & Bianchi-Berthouze, N. (2017). Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging. Biomedical Optics Express, 8(10), 4480. https://doi.org/10.1364/BOE.8.004480
Gedam, S., & Paul, S. (2020). Automatic stress detection using wearable sensors and machine learning: A review. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). https://doi.org/10.1109/iccnt49239.2020.9225692
Germi, J. W., Mensah-Brown, K. G., Chen, H. I., & Schuster, J. M. (2022). Use of smartphone-integrated infrared thermography to monitor sympathetic dysfunction as a surgical complication. Interdisciplinary Neurosurgery, 28, 101475. https://doi.org/10.1016/j.inat.2021.101475
Gomez de Mariscal, E., Munoz-Barrutia A., de Frutos J., Gonzalez-Marcos A. P., & Ugena Martinez, A. M. (2017). Infrared Thermography Processing to Characterize Emotional Stress: A Pilot Study. 8th International Conference of Pattern Recognition Systems (ICPRS 2017). https://doi.org/10.1049/cp.2017.0148
Hallock, G. G. (2019). Dynamic infrared thermography and smartphone thermal imaging as an adjunct for preoperative, intraoperative, and postoperative perforator free flap monitoring. Plastic and. Aesthetic Research, 2019. https://doi.org/10.20517/2347-9264.2019.029
Kaga, S., & Kato, S. (2019). Extraction of useful features for stress detection using various biosignals doing mental arithmetic. 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) https://doi.org/10.1109/lifetech.2019.8883067
Kanazawa, T., Nakagami, G., Goto, T., Noguchi, H., Oe, M., Miyagaki, T., Hayashi, A., Sasaki, S., & Sanada, H. (2016). Use of smartphone attached mobile thermography assessing subclinical inflammation: a pilot study. Journal of Wound Care, 25(4), 177-182. https://doi.org/10/12968/jowc/2016.25.4.177
Kirimtat, A., Krejcar, O., Selamat, A., & Herrera-Viedma, E. (2020). FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography. BMC Bioinformatics, 21(S2). https://doi.org/10.1186/s12859-020-3355-7
Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F., Osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19(17), 3805. https://doi.org/10.3390/s19173805
Liu, X., Shan, Y., Peng, M., Chen, H., & Chen, T. (2020). Human stress and StO2: database, features, and classification of emotional and physical stress. Entropy, 22(9), 962. https://doi.org/10.3390/e22090962
Liu, X., Xiao, X., Cao, R., & Chen, T. (2020, April). Evolution of facial tissue oxygen saturation and detection of human physical stress. 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). https://doi.org/10.1109/ipec49694.2020.9115140
Luze, H., Nischwitz, S. P., Wurzer, P., Winter, R., Spendel, S., Kamolz, L. P., & Bjelic-Radisic, V. (2022). Assessment of Mastectomy Skin Flaps for Immediate Reconstruction with Implants via Thermal Imaging—A Suitable, Personalized Approach?. Journal of Personalized Medicine, 12(5), 740.https://doi.org/10.3390/jpm12050740
Machado Fernández, J. R., & Anishchenko, L. (2018). Mental stress detection using bioradar respiratory signals. Biomedical Signal Processing and Control, 43, 244-249. https://doi.org/10.1016/j.bspc.2018.03.006
Meshram, S., Babu, R., & Adhikari, J. (2020). Detecting Psychological Stress using Machine Learning over Social Media Interaction. 2020 5th International Conference on Communication and Electronics Systems (ICCES). https://doi.org/10.1109/iccs48766.2020.913793
Morales-Ivorra, I., Narváez, J., Gomez Vaquero, C., Nolla, J. M., Moragues Pastor, C., Grados Canovas, D., Narvaez, J. A., & Marin-López, M. A. (2022). AB1343 on the development of new disease activity scores for remote assessment of patient with rheumatoid arthritis using thermography and machine learning. Annals of the Rheumatic Diseases, 81(Suppl 1). https://doi.org/10.1136/annrheumdis-2022-eular.1567
Moran-Romero, M. A., & López-Mendoza, F. J. (2022). Postoperative Monitoring of Free Flaps Using Smartphone Thermal Imaging May Lead to Ambiguous Results: Three Case Reports. International Microsurgery Journal, 6(1). https://doi.org/10.24983/scitemed.imj.2022.00163
Nassar, A. H., Maselli, A. M., Manstein, S., Shiah, E., Slatnick, B. L., Dowlatshahi, A. S., Cauley, R., & Lee, B. T. (2021). Comparison of various modalities utilized for preoperative planning in microsurgical reconstructive surgery. Journal of Reconstructive Microsurgery, 38(03), 170-180. https://doi.org/10.1055/s-0041-1736316
Nath, R. K., & Thapliyal, H. (2021). Smart wristband-based stress detection framework for older adults with cortisol as stress biomarker. IEEE Transactions on Consumer Electronics, 67(1), 30-39. https://doi.org/10.1109/tce.2021.3057806
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. https://doi.org/10.1016/j.bbw.2019.01.004
Passos, M., & Rocha, A. F. (2022). Evaluation of infrared thermography with a portable camera as a diagnostic tool for peripheral arterial disease of the lower limbs compared with color Doppler ultrasonography. Archives of Medical Sciences – Atherosclerotic Diseases, 7(1), 66–72. https://doi.org/10.5114/amsad/150716
Pereira, N., & Hallock, G. G. (2020). Smartphone thermography for lower extremity local flap perforator mapping. Journal of Reconstructive Microsurgery, 37(01), 059-066. https://doi.org/10.1055/s-0039-3402032
Qin, Q., Nakagami, G., Ohashi, Y., Dai, M., Sanada, H., & Oe, M. (2022). Development of a self-monitoring tool for diabetic foot prevention using smartphone-based thermography: Plantar thermal pattern changes and usability in the home environment. Drug Discoveries & Therapeutics, 16(4), 169-176. https://doi.org/10.5582/ddt.2022.01050
Ring, E. F. J. (2007). The historical development of temperature measurement in medicine. Infrared Physics & Technology, 49(3), 297-301. https://doi.org/10.1016/j.infrared.2006.06.029
Rodríguez-Arce, J., Lara-Flores, L., Portillo-Rodríguez, O., & Martínez-Méndez, R. (2020). Towards an anxiety and stress recognition system for academic environments based on physiological features. Computer methods and programs in biomedicine, 190, 105408. https://doi.org/10.1016/j.cmpb.2020.105408
Shanmugasundaram, G., Yazhini, S., Hemapratha, E., & Nithya, S. (2019). A comprehensive review on stress detection techniques. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). https://doi.org/10.1109/iscan.2019.8878795
Sharma, N., Dhall, A., Gedeon, T., & Goecke, R. (2013). Modeling stress using thermal facial patterns: A spatio-temporal approach. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. https://doi.org/10.1109/acii.2013.70
Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers in Biology and Medicine, 134, 104450. https://doi.org/10.1016/j.compbiomed.2021.104450
Theuma, F., & Cassar, K. (2018). The use of smartphone-attached thermography camera in diagnosis of acute lower limb ischemia. Journal of Vascular Surgery, 67(4), 1297. https://doi.org/10.1016/j.jvs.2017.02.054
Xue, E. Y., Chandler, L. K., Viviano, S. L., & Keith, J. D. (2018). Use of FLIR ONE smartphone thermography in burn wound assessment. Annals of Plastic Surgery, 80(4). https://doi.org/10.1097/Sap.0000000000001363
Akbar, F., Bayraktaroglu, A. E., Buddharaju, P., Da Cunha Silva, D. R., Gao, G., Grover, T., Gutierrez-Osuna, R., Jones, N. C., Mark, G., Pavlidis, I., Storer, K., Wang, Z., Wesley, A., & Zaman, S. (2019). Email makes you sweat. Examining email interruptions and stress using thermal imaging. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300898
Anishchenko, L., & Turetzkaya, A. (2020). Improved Non-Contact Mental Stress Detection via Bioradar. 2020 International Conference on Biomedical Innovations and Applications (BIA). https://doi.org/10.1109/bia50171.2020.924492
Anusha, A., Padmaja, N., D.V.S, M., & Kumar, B.S. (2020). IOT Based Stress Detection and Health Monitoring System. HELIX, 10((2). 161-167. https://doi.org/10.29042/2020-10-2-161-164
Bara C. P., Papakostas M., Mihalcea R. (2020). A Deep Learning Approach Towards Multimodal Stress Detection. Proceedings of the AAAI-20 Workshop on Affective Content Analysis, 2020, New York, USA.
Baran K. (2021). Stress detection and monitoring based on low-cost mobile thermography. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES 2021; Procedia Computer Science, 192, 1102-1110. https://doi.org/10.1016/j.procs.2021.08.113
Baran K. (2021). Thermal Imaging of Stress: A Review. Computational Intelligence, Information Systems and Data Mining. 2021; 95-113.
Bogomilsky, S., Hoffer, O., Shalmon, G., & Scheinowitz, M. (2022). Preliminary study of thermal density distribution and entropy analysis during cycling exercise stress test using infrared thermography. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-18233-5
Campbell J.S., Mead M.N. (2022). Human Medical Thermography. CRC Press. https://doi.org/10.1201/9781003281764
Cardone, D., Perpetuini, D., Filippini, C., Spadolini, E., Mancini, L., Chiarelli, A. M., & Merla, A. (2020). Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal. Applied Sciences, 10(16), 5673. https://doi.org/10.3390/app10165673
Cho, Y., Bianchi-Berthouze, N., & Julier, S. J. (2017). DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings. 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). https://doi.org/10.1109/acii.2017.8273639
Cho, Y., Julier, S. J., Marquardt, N., & Bianchi-Berthouze, N. (2017). Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging. Biomedical Optics Express, 8(10), 4480. https://doi.org/10.1364/BOE.8.004480
Gedam, S., & Paul, S. (2020). Automatic stress detection using wearable sensors and machine learning: A review. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). https://doi.org/10.1109/iccnt49239.2020.9225692
Germi, J. W., Mensah-Brown, K. G., Chen, H. I., & Schuster, J. M. (2022). Use of smartphone-integrated infrared thermography to monitor sympathetic dysfunction as a surgical complication. Interdisciplinary Neurosurgery, 28, 101475. https://doi.org/10.1016/j.inat.2021.101475
Gomez de Mariscal, E., Munoz-Barrutia A., de Frutos J., Gonzalez-Marcos A. P., & Ugena Martinez, A. M. (2017). Infrared Thermography Processing to Characterize Emotional Stress: A Pilot Study. 8th International Conference of Pattern Recognition Systems (ICPRS 2017). https://doi.org/10.1049/cp.2017.0148
Hallock, G. G. (2019). Dynamic infrared thermography and smartphone thermal imaging as an adjunct for preoperative, intraoperative, and postoperative perforator free flap monitoring. Plastic and. Aesthetic Research, 2019. https://doi.org/10.20517/2347-9264.2019.029
Kaga, S., & Kato, S. (2019). Extraction of useful features for stress detection using various biosignals doing mental arithmetic. 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) https://doi.org/10.1109/lifetech.2019.8883067
Kanazawa, T., Nakagami, G., Goto, T., Noguchi, H., Oe, M., Miyagaki, T., Hayashi, A., Sasaki, S., & Sanada, H. (2016). Use of smartphone attached mobile thermography assessing subclinical inflammation: a pilot study. Journal of Wound Care, 25(4), 177-182. https://doi.org/10/12968/jowc/2016.25.4.177
Kirimtat, A., Krejcar, O., Selamat, A., & Herrera-Viedma, E. (2020). FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography. BMC Bioinformatics, 21(S2). https://doi.org/10.1186/s12859-020-3355-7
Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F., Osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19(17), 3805. https://doi.org/10.3390/s19173805
Liu, X., Shan, Y., Peng, M., Chen, H., & Chen, T. (2020). Human stress and StO2: database, features, and classification of emotional and physical stress. Entropy, 22(9), 962. https://doi.org/10.3390/e22090962
Liu, X., Xiao, X., Cao, R., & Chen, T. (2020, April). Evolution of facial tissue oxygen saturation and detection of human physical stress. 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). https://doi.org/10.1109/ipec49694.2020.9115140
Luze, H., Nischwitz, S. P., Wurzer, P., Winter, R., Spendel, S., Kamolz, L. P., & Bjelic-Radisic, V. (2022). Assessment of Mastectomy Skin Flaps for Immediate Reconstruction with Implants via Thermal Imaging—A Suitable, Personalized Approach?. Journal of Personalized Medicine, 12(5), 740.https://doi.org/10.3390/jpm12050740
Machado Fernández, J. R., & Anishchenko, L. (2018). Mental stress detection using bioradar respiratory signals. Biomedical Signal Processing and Control, 43, 244-249. https://doi.org/10.1016/j.bspc.2018.03.006
Meshram, S., Babu, R., & Adhikari, J. (2020). Detecting Psychological Stress using Machine Learning over Social Media Interaction. 2020 5th International Conference on Communication and Electronics Systems (ICCES). https://doi.org/10.1109/iccs48766.2020.913793
Morales-Ivorra, I., Narváez, J., Gomez Vaquero, C., Nolla, J. M., Moragues Pastor, C., Grados Canovas, D., Narvaez, J. A., & Marin-López, M. A. (2022). AB1343 on the development of new disease activity scores for remote assessment of patient with rheumatoid arthritis using thermography and machine learning. Annals of the Rheumatic Diseases, 81(Suppl 1). https://doi.org/10.1136/annrheumdis-2022-eular.1567
Moran-Romero, M. A., & López-Mendoza, F. J. (2022). Postoperative Monitoring of Free Flaps Using Smartphone Thermal Imaging May Lead to Ambiguous Results: Three Case Reports. International Microsurgery Journal, 6(1). https://doi.org/10.24983/scitemed.imj.2022.00163
Nassar, A. H., Maselli, A. M., Manstein, S., Shiah, E., Slatnick, B. L., Dowlatshahi, A. S., Cauley, R., & Lee, B. T. (2021). Comparison of various modalities utilized for preoperative planning in microsurgical reconstructive surgery. Journal of Reconstructive Microsurgery, 38(03), 170-180. https://doi.org/10.1055/s-0041-1736316
Nath, R. K., & Thapliyal, H. (2021). Smart wristband-based stress detection framework for older adults with cortisol as stress biomarker. IEEE Transactions on Consumer Electronics, 67(1), 30-39. https://doi.org/10.1109/tce.2021.3057806
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. https://doi.org/10.1016/j.bbw.2019.01.004
Passos, M., & Rocha, A. F. (2022). Evaluation of infrared thermography with a portable camera as a diagnostic tool for peripheral arterial disease of the lower limbs compared with color Doppler ultrasonography. Archives of Medical Sciences – Atherosclerotic Diseases, 7(1), 66–72. https://doi.org/10.5114/amsad/150716
Pereira, N., & Hallock, G. G. (2020). Smartphone thermography for lower extremity local flap perforator mapping. Journal of Reconstructive Microsurgery, 37(01), 059-066. https://doi.org/10.1055/s-0039-3402032
Qin, Q., Nakagami, G., Ohashi, Y., Dai, M., Sanada, H., & Oe, M. (2022). Development of a self-monitoring tool for diabetic foot prevention using smartphone-based thermography: Plantar thermal pattern changes and usability in the home environment. Drug Discoveries & Therapeutics, 16(4), 169-176. https://doi.org/10.5582/ddt.2022.01050
Ring, E. F. J. (2007). The historical development of temperature measurement in medicine. Infrared Physics & Technology, 49(3), 297-301. https://doi.org/10.1016/j.infrared.2006.06.029
Rodríguez-Arce, J., Lara-Flores, L., Portillo-Rodríguez, O., & Martínez-Méndez, R. (2020). Towards an anxiety and stress recognition system for academic environments based on physiological features. Computer methods and programs in biomedicine, 190, 105408. https://doi.org/10.1016/j.cmpb.2020.105408
Shanmugasundaram, G., Yazhini, S., Hemapratha, E., & Nithya, S. (2019). A comprehensive review on stress detection techniques. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). https://doi.org/10.1109/iscan.2019.8878795
Sharma, N., Dhall, A., Gedeon, T., & Goecke, R. (2013). Modeling stress using thermal facial patterns: A spatio-temporal approach. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. https://doi.org/10.1109/acii.2013.70
Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers in Biology and Medicine, 134, 104450. https://doi.org/10.1016/j.compbiomed.2021.104450
Theuma, F., & Cassar, K. (2018). The use of smartphone-attached thermography camera in diagnosis of acute lower limb ischemia. Journal of Vascular Surgery, 67(4), 1297. https://doi.org/10.1016/j.jvs.2017.02.054
Xue, E. Y., Chandler, L. K., Viviano, S. L., & Keith, J. D. (2018). Use of FLIR ONE smartphone thermography in burn wound assessment. Annals of Plastic Surgery, 80(4). https://doi.org/10.1097/Sap.0000000000001363
BARAN, K. (2024). APPLICATION OF THERMAL IMAGING CAMERAS FOR SMARTPHONE: SEEK THERMAL COMPACT PRO AND FLIR ONE PRO FOR HUMAN STRESS DETECTION – COMPARISON AND STUDY . Applied Computer Science, 20(1), 122–138. https://doi.org/10.35784/acs-2024-08
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