{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:01:14Z","timestamp":1772910074714,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2022-291"],"award-info":[{"award-number":["075-15-2022-291"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware\u2013software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.<\/jats:p>","DOI":"10.3390\/s23125591","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T02:28:56Z","timestamp":1686796136000},"page":"5591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessment of a Person\u2019s Emotional State Based on His or Her Posture Parameters"],"prefix":"10.3390","volume":"23","author":[{"given":"Yulia","family":"Shichkina","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]},{"given":"Olga","family":"Bureneva","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]},{"given":"Evgenii","family":"Salaurov","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3056-3560","authenticated-orcid":false,"given":"Ekaterina","family":"Syrtsova","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Saint Petersburg Electrotechnical University \u201cLETI\u201d, 197022 Saint Petersburg, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,15]]},"reference":[{"key":"ref_1","first-page":"2223","article-title":"Info-computational Constructivism and Cognition","volume":"9","year":"2014","journal-title":"Constr. Found."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tao, J., Tan, T., and Picard, R.W. (2005). Affective Computing and Intelligent Interaction, Springer.","DOI":"10.1007\/11573548"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1177\/0539018405058216","article-title":"What are emotions? And how can they be measured?","volume":"44","author":"Scherer","year":"2005","journal-title":"Soc. Sci. Inf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dzedzickis, A., Kaklauskas, A., and Bucinskas, V. (2020). Human Emotion Recognition: Review of Sensors and Methods. Sensors, 20.","DOI":"10.3390\/s20030592"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. (2018). A Review of Emotion Recognition Using Physiological Signals. Sensors, 18.","DOI":"10.3390\/s18072074"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions Recognition Using EEG Signals: A Survey","volume":"10","author":"Fonseca","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TAFFC.2018.2890636","article-title":"A Review on Nonlinear Methods Using Electroencephalographic Recordings for Emotion Recognition","volume":"12","author":"Alcaraz","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1650015","DOI":"10.4015\/S1016237216500150","article-title":"Dynamical analysis of emotional states from electroencephalogram signals","volume":"28","author":"Goshvarpour","year":"2016","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hasnul, M.A., Aziz, N.A.A., Alelyani, S., Mohana, M., and Aziz, A.A. (2021). Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review. Sensors, 21.","DOI":"10.3390\/s21155015"},{"key":"ref_10","unstructured":"Nikolova, D., Petkova, P., Manolova, A., and Georgieva, P. (2018, January 15\u201317). ECG-Based Emotion Recognition: Overview of Methods and Applications. Proceedings of the Advances in Neural Networks and Applications, St. Konstantin and Elena Resort, Varna, Bulgaria."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mithbavkar, S.A., and Shah, M.S. (2021, January 28\u201330). Analysis of EMG Based Emotion Recognition for Multiple People and Emotions. Proceedings of the IEEE 3rd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), Tainan, Taiwan.","DOI":"10.1109\/ECBIOS51820.2021.9510858"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13229","DOI":"10.1109\/ACCESS.2022.3146729","article-title":"Emotion Recognition with Audio, Video, EEG, and EMG: A Dataset and Baseline Approaches","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","unstructured":"Pavlidis, I., Levine, J., and Baukol, P. (2001, January 7\u201310). Thermal Image Analysis for Anxiety Detection. Proceedings of the IEEE International Conference on Image Processing, Thessaloniki, Greece."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, J.-M., An, Y.-E., Bak, E., and Pan, S. (2022). Improvement of Negative Emotion Recognition in Visible Images Enhanced by Thermal Imaging. Sustainability, 14.","DOI":"10.3390\/su142215200"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Udovi\u010di\u0107, G., Derek, J., Russo, M., and Sikora, M. (2017, January 23). Wearable Emotion Recognition System Based on GSR and PPG Signals. Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care, Mountain View, CA, USA.","DOI":"10.1145\/3132635.3132641"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1113\/expphysiol.2008.042424","article-title":"Breathing rhythms and emotions","volume":"93","author":"Homma","year":"2008","journal-title":"Exp. Physiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ritsert, F., Elgendi, M., Galli, V., and Menon, C. (2022). Heart and Breathing Rate Variations as Biomarkers for Anxiety Detection. Bioengineering, 9.","DOI":"10.3390\/bioengineering9110711"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1037\/emo0000612","article-title":"Emotion-related variations in motor tremor: Magnitude, time course, and links to emotional temperament","volume":"20","author":"Klein","year":"2020","journal-title":"Emotion"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bureneva, O., and Safyannikov, N. (2022). Strain Gauge Measuring System for Subsensory Micromotions Analysis as an Element of a Hybrid Human\u2013Machine Interface. Sensors, 22.","DOI":"10.3390\/s22239146"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.bspc.2016.06.020","article-title":"Stress and anxiety detection using facial cues from videos","volume":"31","author":"Giannakakis","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ko, B.C. (2018). A Brief Review of Facial Emotion Recognition Based on Visual Information. Sensors, 18.","DOI":"10.3390\/s18020401"},{"key":"ref_22","first-page":"1","article-title":"Deep learning-based facial emotion recognition for human\u2013computer interaction applications","volume":"2021","author":"Chowdary","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Babu, E.K., Mistry, K., Anwar, M.N., and Zhang, L. (2022). Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition. Sensors, 22.","DOI":"10.3390\/s22228635"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117327","DOI":"10.1109\/ACCESS.2019.2936124","article-title":"Speech emotion recognition using deep learning techniques: A review","volume":"7","author":"Khalil","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47795","DOI":"10.1109\/ACCESS.2021.3068045","article-title":"A Comprehensive Review of Speech Emotion Recognition Systems","volume":"9","author":"Wani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","first-page":"315","article-title":"Review of Research on Speech Emotion Recognition","volume":"Volume 438","author":"Jiang","year":"2022","journal-title":"Machine Learning and Intelligent Communications, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/T-AFFC.2012.16","article-title":"Affective Body Expression Perception and Recognition: A Survey","volume":"4","author":"Kleinsmith","year":"2013","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11761","DOI":"10.1109\/ACCESS.2019.2963113","article-title":"Emotion recognition from body movement","volume":"8","author":"Ahmed","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"989860","DOI":"10.3389\/frai.2022.989860","article-title":"Emotional characteristic analysis of human gait while real-time movie viewing","volume":"5","author":"Jianwattanapaisarn","year":"2022","journal-title":"Front. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1023\/B:JONB.0000023655.25550.be","article-title":"Attributing Emotion to Static Body Postures: Recognition Accuracy, Confusions, and Viewpoint Dependence","volume":"28","author":"Coulson","year":"2004","journal-title":"J. Nonverbal Behav."},{"key":"ref_31","unstructured":"Laban, R. (1971). The Mastery of Movement, Macdonald & Evans. [3rd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1111\/cgf.12598","article-title":"Emotion Analysis and Classification: Understanding the Performers\u2019 Emotions Using the LMA Entities","volume":"36","author":"Aristidou","year":"2015","journal-title":"Comput. Graph. Forum"},{"key":"ref_33","first-page":"1070","article-title":"Research on body-gesture affective computing method based on BGRU-FUS-NN neural network","volume":"32","author":"Fu","year":"2020","journal-title":"J. Comput. Aided Des. Comput. Graph."},{"key":"ref_34","first-page":"529","article-title":"Emotion Recognition Based on Static Human Posture Features","volume":"Volume 920","author":"Shmaliy","year":"2022","journal-title":"Lecture Notes in Electrical Engineering, Proceedings of the 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering, Hangzhou, China, 16\u201317 October 2021"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pal, S., Mukhopadhyay, S., and Suryadevara, N. (2021). Development and Progress in Sensors and Technologies for Human Emotion Recognition. Sensors, 21.","DOI":"10.3390\/s21165554"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Elvitigala, D.S., Matthies, D.J.C., and Nanayakkara, S. (2020). StressFoot: Uncovering the Potential of the Foot for Acute Stress Sensing in Sitting Posture. Sensors, 20.","DOI":"10.3390\/s20102882"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bourahmoune, K., Ishac, K., and Amagasa, T. (2022). Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion. Sensors, 22.","DOI":"10.3390\/s22145337"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/JSEN.2009.2037330","article-title":"Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification","volume":"10","author":"Meyer","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1109\/JBHI.2020.3030096","article-title":"Developing and Evaluating a Mixed Sensor Smart Chair System for Real-Time Posture Classification: Combining Pressure and Distance Sensors","volume":"25","author":"Jeong","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TITB.2009.2035498","article-title":"What Does Your Chair Know About Your Stress Level?","volume":"14","author":"Arnrich","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8007","DOI":"10.1109\/JSEN.2020.2980207","article-title":"A smart chair sitting posture recognition system using flex sensors and FPGA implemented artificial neural network","volume":"20","author":"Hu","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Engelbart, D.C. (1962). Augmenting Human Intellect: A Conceptual Framework, Summary Report AFOSR-3223 under Contract AF 49(638)-1024, SRI Project 3578 for Air Force Office of Scientific Research, Stanford Research Institute.","DOI":"10.21236\/AD0289565"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Krinkin, K., Shichkina, Y., and Ignatyev, A. Co-evolutionary hybrid intelligence. Proceedings of the 2021 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), Kaliningrad, Russia.","DOI":"10.1109\/DCNA53427.2021.9587002"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Krinkin, K., Shichkina, Y., and Ignatyev, A. (2022). Co-Evolutionary Hybrid Intelligence Is a Key Concept for the World Intellectualization. Kybernetes, ahead-of-print.","DOI":"10.1108\/K-03-2022-0472"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Schmorrow, D., and Fidopiastis, C. (2018). Lecture Notes in Computer Science, Proceedings of the Augmented Cognition: Intelligent Technologies, 12th International Conference AC 2018, Las Vegas, NV, USA, 15\u201320 July 2018, Springer.","DOI":"10.1007\/978-3-319-91470-1"},{"key":"ref_46","unstructured":"Parrott, W.G. (2000). Emotions in Social Psychology, Psychology Press. [1st ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.entcs.2019.04.009","article-title":"Emotion Recognition from Physiological Signal Analysis: A Review","volume":"343","author":"Egger","year":"2019","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1511\/2001.28.344","article-title":"The Nature of Emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice","volume":"89","author":"Plutchik","year":"2001","journal-title":"Am. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"113223","DOI":"10.1016\/j.psychres.2020.113223","article-title":"Development of the short version of the spielberger state\u2014Trait anxiety inventory","volume":"291","author":"Zsido","year":"2020","journal-title":"Psychiatry Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/0022-3999(94)00125-O","article-title":"The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue","volume":"39","author":"Smets","year":"1995","journal-title":"J. Psychosom. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/0022-3999(67)90010-4","article-title":"The Social Readjustment Rating Scale","volume":"11","author":"Holmes","year":"1967","journal-title":"J. Psychosom. Research"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1097\/00005650-199206000-00002","article-title":"The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection","volume":"30","author":"Ware","year":"1992","journal-title":"Med. Care"},{"key":"ref_53","unstructured":"Karle, H.W.A., and Boy, J.H. (2010). Hypnotherapy: A Practical Handbook, Free Association Books. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Gilligan, S. (2018). Therapeutic Trances: The Cooperation Principle in Ericksonian Hypnotherapy, Routledge. [1st ed.].","DOI":"10.4324\/9780429506079"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1080\/00223980.1960.9916432","article-title":"The multifactor-analytic theory of emotion","volume":"50","author":"Plutchik","year":"1960","journal-title":"J. Psychol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1111\/j.1745-6916.2007.00044.x","article-title":"Basic emotions, natural kinds, emotion schemas, and a new paradigm","volume":"2","author":"Izard","year":"2007","journal-title":"Perspect. Psychol. Sci."},{"key":"ref_57","unstructured":"Erickson, M.H., and Rossi, E.L. (1979). Hypnotherapy: An Exploratory Casebook, Irvington Pulishers Inc.. [1st ed.]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Kumar, A., Mase, J.M., Rengasamy, D., Rothwell, B., Torres, M.T., Winkler, D.A., and Figueredo, G.P. (2022, January 18\u201322). EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python. Proceedings of the Machine Learning, Optimization, and Data Science: 8th International Conference, Certosa di Pontignano, Italy.","DOI":"10.1007\/978-3-031-25891-6_19"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"710","DOI":"10.3389\/fpsyg.2017.00710","article-title":"Postural Communication of Emotion: Perception of Distinct Poses of Five Discrete Emotions","volume":"8","author":"Lopez","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"111555","DOI":"10.1016\/j.jbiomech.2023.111555","article-title":"Influence of the Hawthorne effect on spatiotemporal parameters, kinematics, ground reaction force, and the symmetry of the dominant and nondominant lower limbs during gait","volume":"152","author":"Jeon","year":"2023","journal-title":"J. Biomech."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5591\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:14Z","timestamp":1760126114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,15]]},"references-count":60,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125591"],"URL":"https:\/\/doi.org\/10.3390\/s23125591","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,15]]}}}