Bioseñales en entornos laborales y su aplicación en primeros intervinientes
Contenido principal del artículo
Resumen
La medición de señales psicofisiológicas de trabajadores en el desempeño de sus tareas es útil para detectar estados psicofisiológicos que les impidan desarrollar adecuadamente su labor y pongan en peligro su integridad física. Para una detección efectiva de estos estados es necesario una selección adecuada de las bioseñales a monitorizar, acorde a la labor realizada, y un procesamiento correcto de las mismas. También es necesario establecer una verdad fundamental que permita el desarrollo de algoritmos de aprendizaje automático efectivas. Este artículo revisa las bioseñales y herramientas de procesamiento y predicción utilizadas en la detección de estados psicofisiológicos peligrosos para los trabajadores y expone una aplicación de monitorización de las bioseñales con primeros intervinientes durante ejercicios de alta fidelidad.
Palabras clave:
Detalles del artículo
Citas
Arsalan, A., Majid, M., Anwar, S. M., Bagci, U., 2019. Classification of per- ceived human stress using physiological signals. In: 2019 41st Annual In- ternational Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 1247–1250. DOI: 10.1109/EMBC.2019.8856377
Batista, D., da Silva, H. P., Fred, A., Moreira, C., Reis, M., Ferreira, H. A., 2019. Benchmarking of the BITalino biomedical toolkit against an esta- blished gold standard. Healthcare Technology Letters 6 (2), 32 – 36. DOI: 10.1049/htl.2018.5037
Batistatos, M. C., Kourtis, M.-A., Xilouris, G. K., Santorinaios, D., Oikono- makis, A., Kourtis, A., 2022. A technological framework for leveraging first responders’ efficiency and safety. In: 2022 Panhellenic Conference on Electronics & Telecommunications (PACET). pp. 1–6. DOI: 10.1109/PACET56979.2022.9976349
Bhoja, R., Guttman, O. T., Fox, A. A., Melikman, E., Kosemund, M., Gin- grich, K. J., 2020. Psychophysiological stress indicators of heart rate variability and electrodermal activity with application in healthcare simulation research. Simulation in Healthcare 15 (1), 39 – 45. DOI: 10.1097/SIH.0000000000000402
Bobade, P., Vani, M., 2020. Stress detection with machine learning and deep learning using multimodal physiological data. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIR- CA). pp. 51–57. DOI: 10.1109/ICIRCA48905.2020.9183244
Bustos, D., Cardoso, F., Rios, M., Vaz, M., Guedes, J., Torres Costa, J., Santos Baptista, J., Fernandes, R. J., 2023. Machine learning approach to mo- del physical fatigue during incremental exercise among firefighters. Sen- sors 23 (1). DOI: 10.3390/s23010194
Choi, J., Ahmed, B., Gutierrez-Osuna, R., 2012. Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Transac- tions on Information Technology in Biomedicine 16 (2), 279 – 286. DOI: 10.1109/TITB.2011.2169804
Das, D., Datta, S., Bhattacharjee, T., Choudhury, A. D., Pal, A., 2018. Elimi- nating individual bias to improve stress detection from multimodal physiological data. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2018, 5753 – 5758. DOI: 10.1109/EMBC.2018.8513680
Daviaux, Y., Bonhomme, E., Ivers, H., de Sevin, E., Micoulaud-Franchi, J.- A., Bioulac, S., Morin, C. M., Philip, P., Altena, E., 2020. Event-related electrodermal response to stress: results from a realistic driving simulator scenario. Human Factors 62 (1), 138 – 151. DOI: 10.1177/0018720819842779
Filippini, C., Di Crosta, A., Palumbo, R., Perpetuini, D., Cardone, D., Ceccato, I., Di Domenico, A., Merla, A., 2022. Automated affective computing based on bio-signals analysis and deep learning approach. Sensors 22 (5). DOI: 10.3390/s22051789
Gabbi, M., Cornia, L., Villani, V., Sabattini, L., 2024. Understanding fatigue through biosignals: a comprehensive dataset. In: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. Asso- ciation for Computing Machinery, New York, NY, USA, p. 901–905. DOI: 10.1145/3610977.3637485
Giaume, L., Le Roy, B., Daniel, Y., Lauga Cami, H., Jost, D., Travers, S., Trousselard, M., 2024. Psychological, cognitive, and physiological impact of hazards casualties’ trainings on first responders: the example of a chemical and radiological training. an exploratory study. Frontiers in Psychology 15. DOI: 10.3389/fpsyg.2024.1336701
Grothe, J., Tucker, S., Blake, A., Achutan, C., Medcalf, S., Suwondo, T., Fruh- ling, A., Yoder, A., 2023. Exploring first responders’ use and perceptions on continuous health and environmental monitoring. International Journal of Environmental Research and Public Health 20 (6). DOI: 10.3390/ijerph20064787
Gutiérrez, A., Blanco, P., Ruiz, V., Chatzigeorgiou, C., Oregui, X., Álvarez, M., Navarro, S., Feidakis, M., Azpiroz, I., Izquierdo, G., Larraga-Garc´ıa, B., Kasnesis, P., Olaizola, I. G., Álvarez, F., 2023. Biosignals monitoring of first responders for cognitive load estimation in real-time operation. Ap- plied Sciences 13 (13). DOI: 10.3390/app13137368
Halim, Z., Rehan, M., 2020. On identification of driving-induced stress using electroencephalogram signals: a framework based on wearable safety- critical scheme and machine learning. Information Fusion 53, 66 – 79. DOI: 10.1016/j.inffus.2019.06.006
Han, H. J., Labbaf, S., Borelli, J. L., Dutt, N., Rahmani, A. M., 2020. Objective stress monitoring based on wearable sensors in everyday settings. Journal of Medical Engineering and Technology 44 (4), 177 – 189. DOI: 10.1080/03091902.2020.1759707
Healey, J. A., Picard, R. W., 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Trans- portation Systems 6 (2), 156 – 166. DOI: 10.1109/TITS.2005.848368
Hosseini, S., Gottumukkala, R., Katragadda, S., Bhupatiraju, R. T., Ashkar, Z., Borst, C. W., Cochran, K., 2022. A multimodal sensor dataset for continuous stress detection of nurses in a hospital. Scientific Data 9 (1). DOI: 10.1038/s41597-022-01361-y
Hu, X., Lodewijks, G., 2020. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: the value of differentiation of sleepi- ness and mental fatigue. Journal of Safety Research 72, 173 – 187. DOI: 10.1016/j.jsr.2019.12.015
Kang, D.-H., Kim, D.-H., 2022. 1D convolutional autoencoder-based PPG and GSR signals for real-time emotion classification. IEEE Access 10, 91332 – 91345. DOI: 10.1109/ACCESS.2022.3201342
Koldijk, S., Neerincx, M. A., Kraaij, W., 2018. Detecting work stress in offices by combining unobtrusive sensors. IEEE Transactions on Affective Computing 9 (2), 227 – 239. DOI: 10.1109/TAFFC.2016.2610975
Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A., Kraaij, W., 2014. The SWELL knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction. Association for Computing Machinery, New York, NY, USA, p. 291–298. DOI: 10.1145/2663204.2663257
Koo, H. R., Lee, J.-W., Lee, J. H., 2018. Garment function module to reduce motion artifacts in heart-activity-sensing clothing based on a magnetic-induced conductivity sensing method. Textile Research Journal 88 (19), 2190 – 2205. DOI: 10.1177/0040517517716909
Kutt, K., Binek, W., Misiak, P., Nalepa, G. J., Bobek, S., 2018. Towards the development of sensor platform for processing physiological data from wearable sensors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinforma- tics) 10842 LNAI, 168 – 178. DOI: 10.1007/978-3-319-91262-2 16
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). DOI: 10.3390/s19173805
Liu, J. J. W., Reed, M., Vickers, K., 2019. Reframing the individual stress response: balancing our knowledge of stress to improve responsivity to stressors. Stress and Health 35 (5), 607 – 616. DOI: 10.1002/smi.2893
Malik, M., Camm, A., Bigger Jr., J., Breithardt, G., Cerutti, S., Cohen, R., Coumel, P., Fallen, E., Kennedy, H., Kleiger, R., Lombardi, F., Malliani, A., Moss, A., Rottman, J., Schmidt, G., Schwartz, P., Singer, D., 1996. Heart rate variability. standards of measurement, physiological interpretation, and clinical use. European Heart Journal 17 (3), 354 – 381. DOI: 10.1093/oxfordjournals.eurheartj.a014868
McDuff, D., Gontarek, S., Picard, R., 2014. Remote measurement of cognitive stress via heart rate variability. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. pp. 2957–2960. DOI: 10.1109/EMBC.2014.6944243
Menghini, L., Gianfranchi, E., Cellini, N., Patron, E., Tagliabue, M., Sarlo, M., 2019. Stressing the accuracy: wrist-worn wearable sensor validation over different conditions. Psychophysiology 56 (11). DOI: 10.1111/psyp.13441
Mizuno, K., Tanaka, M., Yamaguti, K., Kajimoto, O., Kuratsune, H., Watana- be, Y., 2011. Mental fatigue caused by prolonged cognitive load associated with sympathetic hyperactivity. Behavioral and Brain Functions 7. DOI: 10.1186/1744-9081-7-17
Mohino-Herranz, I., Gil-Pita, R., Garc´ıa-Go´mez, J., Rosa-Zurera, M., Seoane, F., 2020. A wrapper feature selection algorithm: an emotional assessment using physiological recordings from wearable sensors. Sensors 20 (1). DOI: 10.3390/s20010309
Nkurikiyeyezu, K., Yokokubo, A., Lopez, G., 2020. Effect of person-specific biometrics in improving generic stress predictive models. Sensors and Materials 32 (2), 703 – 722. DOI: 10.18494/SAM.2020.2650
Pakarinen, T., Pietila¨, J., Nieminen, H., 2019. Prediction of self-perceived stress and arousal based on electrodermal activity. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 2191–2195. DOI: 10.1109/EMBC.2019.8857621
Paletta, L., Pszeida, M., Schneeberger, M., Dini, A., Reim, L., Kallus, K. W., 2022. Cognitive-emotional stress and risk stratification of situational awareness in immersive first responder training. In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). pp. 1–4. DOI: 10.1109/BHI56158.2022.9926805
Pan, J., Tompkins, W. J., 1985. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering BME-32 (3), 230 – 236. DOI: 10.1109/TBME.1985.325532
Pinto, R. J., Silva, P. M., Duarte, R. P., Marinho, F. A., Gouveia, A. J., Goncçalves, N. J., Coelho, P. J., Zdravevski, E., Lameski, P., Garcia, N. M., Pires, I. M., 2023. Preliminary study on the identification of diseases by electrocardiography sensors’ data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13919 LNBI, 292 – 304. DOI: 10.1007/978-3-031-34953-9 23
Pratas, P., Bustos, D., Guedes, J., Mendes, J., Baptista, J. S., Vaz, M., 2023. Physiological monitoring systems for fatigue detection within firefighters: a brief systematic review. Studies in Systems, Decision and Control 449, 469 – 486. DOI: 10.1007/978-3-031-12547-8 38
Rodríguez-Arce, J., Lara-Flores, L., Portillo-Rodíguez, 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. DOI: 10.1016/j.cmpb.2020.105408
Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K., 2018. Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conferen- ce on Multimodal Interaction. Association for Computing Machinery, New York, NY, USA, p. 400–408. DOI: 10.1145/3242969.3242985
Schmidt, P., Reiss, A., Du¨richen, R., Laerhoven, K. V., 2019. Wearable-based affect recognition—a review. Sensors 19 (19). DOI: 10.3390/s19194079
Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tro¨ster, G., Ehlert, U., 2010. Discriminating stress from cognitive load using a wearable EDA device. IEEE Transactions on Information Technology in Biomedicine 14 (2), 410 – 417. DOI: 10.1109/TITB.2009.2036164
Sun, F.-T., Kuo, C., Cheng, H.-T., Buthpitiya, S., Collins, P., Griss, M., 2012. Activity-aware mental stress detection using physiological sensors. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 76 LNICST, 211 – 230. DOI: 10.1007/978-3-642-29336-8 12
Tonacci, A., Billeci, L., Burrai, E., Sansone, F., Conte, R., 2019. Comparative evaluation of the autonomic response to cognitive and sensory stimulations through wearable sensors. Sensors 19 (21). DOI: 10.3390/s19214661
Toral, V., Romero, F. J., Castillo, E., Morales, D. P., Rivadeneyra, A., Salinas- Castillo, A., Parrilla, L., Garc´ıa, A., 2022. A versatile wearable based on reconfigurable hardware for biomedical measurements. Measurement: Journal of the International Measurement Confederation 201. DOI: 10.1016/j.measurement.2022.111744
Torku, A., Chan, A. P., Yung, E. H., Seo, J., 2022. Detecting stressful ol- der adults-environment interactions to improve neighbourhood mobility: a multimodal physiological sensing, machine learning, and risk hotspot analysis-based approach. Building and Environment 224. DOI: 10.1016/j.buildenv.2022.109533
PLUX, 2020. BITalino Core BT datasheet. Available online: https://support.pluxbiosignals.com/wp-content/uploads/2021/11/bitalino-core-datasheet.pdf, accessed on May 31, 2024.
van Dooren, M., de Vries, J.-J., Janssen, J. H., 2012. Emotional sweating across the body: comparing 16 different skin conductance measurement locations. Physiology and Behavior 106 (2), 298 – 304. DOI: 10.1016/j.physbeh.2012.01.020
Vavrinsky, E., Stopjakova, V., Kopani, M., Kosnacova, H., 2021. The concept of advanced multi-sensor monitoring of human stress. Sensors 21 (10). DOI: 10.3390/s21103499
Vera-Ortega, P., Vázquez-Martín, R., Fernandez-Lozano, J., García-Cerezo, A., Mandow, A., 2023. Enabling remote responder bio-signal monitoring in a cooperative human–robot architecture for search and rescue. Sensors 23 (1). DOI: 10.3390/s23010049
Wagner, R. E., da Silva, H. P., Gramann, K., 2021. Validation of a low-cost electrocardiography (ECG) system for psychophysiological research. Sensors 21 (13). DOI: 10.3390/s21134485