{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:11:41Z","timestamp":1777043501054,"version":"3.51.4"},"reference-count":74,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research (BMBF)","award":["01IS15048B"],"award-info":[{"award-number":["01IS15048B"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["01MT20001L"],"award-info":[{"award-number":["01MT20001L"]}]},{"name":"German Federal Ministry for Economic Affairs and Climate Action (BMWK)","award":["01IS15048B"],"award-info":[{"award-number":["01IS15048B"]}]},{"name":"German Federal Ministry for Economic Affairs and Climate Action (BMWK)","award":["01MT20001L"],"award-info":[{"award-number":["01MT20001L"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.<\/jats:p>","DOI":"10.3390\/s22103634","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"3634","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4404-7313","authenticated-orcid":false,"given":"Hawzhin","family":"Hozhabr Pour","sequence":"first","affiliation":[{"name":"Research Group of Operating Systems and Distributed Systems, University of Siegen, H\u00f6lderlinstr. 3, 57076 Siegen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-4207","authenticated-orcid":false,"given":"Fr\u00e9d\u00e9ric","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23538 L\u00fcbeck, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8848-9434","authenticated-orcid":false,"given":"Lukas","family":"Wegmeth","sequence":"additional","affiliation":[{"name":"Intelligent Systems Group (ISG), University of Siegen, H\u00f6lderlinstr. 3, 57076 Siegen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4095-7758","authenticated-orcid":false,"given":"Christian","family":"Trense","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23538 L\u00fcbeck, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2535-3932","authenticated-orcid":false,"given":"Rafa\u0142","family":"Doniec","sequence":"additional","affiliation":[{"name":"Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4877-8287","authenticated-orcid":false,"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, University of L\u00fcbeck, Ratzeburger Allee 160, 23538 L\u00fcbeck, Germany"},{"name":"Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-287 Katowice, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2860-2447","authenticated-orcid":false,"given":"Roland","family":"Wism\u00fcller","sequence":"additional","affiliation":[{"name":"Research Group of Operating Systems and Distributed Systems, University of Siegen, H\u00f6lderlinstr. 3, 57076 Siegen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). Global Status Report on Road Safety 2018: Summary, World Health Organization. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bergasa, L.M., Almer\u00eda, D., Almaz\u00e1n, J., Yebes, J.J., and Arroyo, R. (2014, January 8\u201311). Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA.","DOI":"10.1109\/IVS.2014.6856461"},{"key":"ref_3","unstructured":"Donnelly, B.R., Schabel, D., Blatt, A.J., and Carter, A. (1999, January 3\u20135). The automated collision notification system. Proceedings of the Transportation Recording: 2000 and Beyond. International Symposium on Transportation Recorders, Arlington, VA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"30653","DOI":"10.3390\/s151229822","article-title":"A review of intelligent driving style analysis systems and related artificial intelligence algorithms","volume":"15","author":"Meiring","year":"2015","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zaldivar, J., Calafate, C.T., Cano, J.C., and Manzoni, P. (2011, January 4\u20137). Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones. Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks, Washington, DC, USA.","DOI":"10.1109\/LCN.2011.6115556"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S126","DOI":"10.1080\/15389588.2014.927577","article-title":"Comparison and validation of injury risk classifiers for advanced automated crash notification systems","volume":"15","author":"Kusano","year":"2014","journal-title":"Traffic Inj. Prev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.aap.2016.09.028","article-title":"Serious injury prediction algorithm based on large-scale data and under-triage control","volume":"98","author":"Nishimoto","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105864","DOI":"10.1016\/j.aap.2020.105864","article-title":"Injury risk assessment based on pre-crash variables: The role of closing velocity and impact eccentricity","volume":"150","author":"Gulino","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_9","unstructured":"(2022, February 16). Available online: www.thyssenkrupp-automotive-technology.com\/en\/products-and-services\/carvaloo."},{"key":"ref_10","unstructured":"Transportation Research Board of the National Academy of Sciences (2020, September 10). The 2nd Strategic Highway Research Program Naturalistic Driving Study Dataset. Available online: https:\/\/insight.shrp2nds.us."},{"key":"ref_11","first-page":"1","article-title":"Road traffic data: Collection methods and applications","volume":"1","author":"Leduc","year":"2008","journal-title":"Work. Pap. Energy Transp. Clim. Chang."},{"key":"ref_12","unstructured":"Pour, H.H., Wegmeth, L., Kordes, A., Grzegorzek, M., and Wism\u00fcller, R. (2019). Feature Extraction and Classification of Sensor Signals in Cars Based on a Modified Codebook Approach. Proceedings of the International Conference on Computer Recognition Systems, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"122480","DOI":"10.1109\/ACCESS.2020.3006887","article-title":"A Comprehensive Study on IoT Based Accident Detection Systems for Smart Vehicles","volume":"8","author":"Alvi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., K\u00f6ping, L., and Grzegorzek, M. (2018). Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors, 18.","DOI":"10.3390\/s18020679"},{"key":"ref_15","unstructured":"Ali, H.M., and Alwan, Z.S. (2017). Car Accident Detection and Notification System Using Smartphone, LAP LAMBERT Academic Publishing."},{"key":"ref_16","unstructured":"Amin, M.S., Jalil, J., and Reaz, M.B.I. (2012, January 18\u201319). Accident detection and reporting system using GPS, GPRS and GSM technology. Proceedings of the 2012 International Conference on Informatics, Electronics & Vision (ICIEV), Dhaka, Bangladesh."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jakobsen, K., Mouritsen, S.C., and Torp, K. (2013, January 5\u20138). Evaluating eco-driving advice using GPS\/CANBus data. Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA.","DOI":"10.1145\/2525314.2525358"},{"key":"ref_19","first-page":"30","article-title":"Reducing risk and improving traffic safety: Research on driver behavior and performance","volume":"88","author":"Tefft","year":"2018","journal-title":"Inst. Transp. Eng. ITE J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ferreira, J., Carvalho, E., Ferreira, B.V., de Souza, C., Suhara, Y., Pentland, A., and Pessin, G. (2017). Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174959"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1109\/TVT.2013.2263400","article-title":"Context-aware driver behavior detection system in intelligent transportation systems","volume":"62","author":"Zedan","year":"2013","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","unstructured":"Zinebi, K., Souissi, N., and Tikito, K. (2018, January 4\u20135). Driver Behavior Analysis Methods: Applications oriented study. Proceedings of the 3rd International Conference on Big Data, Cloud and Application (BDCA 2018), Kenitra, Morocco."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khalid, S., Khalil, T., and Nasreen, S. (2014, January 26\u201328). A survey of feature selection and feature extraction techniques in machine learning. Proceedings of the 2014 Science and Information Conference, Shenzhen, China.","DOI":"10.1109\/SAI.2014.6918213"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ang, J.S., Ng, K.W., and Chua, F.F. (2020, January 24\u201325). Modeling Time Series Data with Deep Learning: A Review, Analysis, Evaluation and Future Trend. Proceedings of the 2020 8th International Conference on Information Technology and Multimedia (ICIMU), Selangor, Malaysia.","DOI":"10.1109\/ICIMU49871.2020.9243546"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mori, M., Miyajima, C., Angkititrakul, P., Hirayama, T., Li, Y., Kitaoka, N., and Takeda, K. (2012, January 16\u201319). Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles. Proceedings of the 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, USA.","DOI":"10.1109\/ITSC.2012.6338802"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.aap.2013.02.021","article-title":"The influence of anger, impulsivity, sensation seeking and driver attitudes on risky driving behaviour among post-graduate university students in Durban, South Africa","volume":"55","author":"Bachoo","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jahangiri, A., Rakha, H.A., and Dingus, T.A. (2015, January 15\u201318). Adopting machine learning methods to predict red-light running violations. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain.","DOI":"10.1109\/ITSC.2015.112"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., and Trivedi, M.M. (2014, January 8\u201311). Beyond just keeping hands on the wheel: Towards visual interpretation of driver hand motion patterns. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957858"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3141\/1784-01","article-title":"Analysis of crash precursors on instrumented freeways","volume":"1784","author":"Lee","year":"2002","journal-title":"Transp. Res. Rec."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.trf.2012.08.006","article-title":"Assessing safety critical braking events in naturalistic driving studies","volume":"16","author":"Bagdadi","year":"2013","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.aap.2012.03.032","article-title":"Development of a method for detecting jerks in safety critical events","volume":"50","author":"Bagdadi","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3141\/1746-12","article-title":"Automated accident detection system","volume":"1746","author":"Harlow","year":"2001","journal-title":"Transp. Res. Rec."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/6979.880968","article-title":"Traffic monitoring and accident detection at intersections","volume":"1","author":"Kamijo","year":"2000","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bacon, J., Bejan, A.I., Beresford, A.R., Evans, D., Gibbens, R.J., and Moody, K. (2011). Using real-time road traffic data to evaluate congestion. Dependable and Historic Computing, Springer.","DOI":"10.1007\/978-3-642-24541-1_9"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11036-011-0304-8","article-title":"Wreckwatch: Automatic traffic accident detection and notification with smartphones","volume":"16","author":"White","year":"2011","journal-title":"Mob. Netw. Appl."},{"key":"ref_36","unstructured":"Chuan-zhi, L., Ru-fu, H., and Ye, H.w. (2008, January 1\u20133). Method of freeway incident detection using wireless positioning. Proceedings of the 2008 IEEE International Conference on Automation and Logistics, Qingdao, China."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0377-2217(03)00209-1","article-title":"A sequential detection approach to real-time freeway incident detection and characterization","volume":"157","author":"Sheu","year":"2004","journal-title":"Eur. J. Oper. Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Faiz, A.B., Imteaj, A., and Chowdhury, M. (2015, January 26\u201327). Smart vehicle accident detection and alarming system using a smartphone. Proceedings of the 2015 International Conference on Computer and Information Engineering (ICCIE), Rajshahi, Bangladesh.","DOI":"10.1109\/CCIE.2015.7399319"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ahmed, V., and Jawarkar, N.P. (2013, January 16\u201318). Design of low cost versatile microcontroller based system using cell phone for accident detection and prevention. Proceedings of the 2013 6th International Conference on Emerging Trends in Engineering and Technology, Nagpur, India.","DOI":"10.1109\/ICETET.2013.17"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T.S., Kj\u00e6rgaard, M.B., Dey, A., Sonne, T., and Jensen, M.M. (2015, January 1\u20134). Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Korea.","DOI":"10.1145\/2809695.2809718"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ozbayoglu, M., Kucukayan, G., and Dogdu, E. (2016, January 5\u20138). A real-time autonomous highway accident detection model based on big data processing and computational intelligence. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840798"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pan, B., and Wu, H. (2017, January 19\u201326). Urban traffic incident detection with mobile sensors based on SVM. Proceedings of the 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Montreal, QC, Canada.","DOI":"10.23919\/URSIGASS.2017.8104994"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Sunny, S.J., and Roney, R. (2019, January 1\u20132). Accident detection using convolutional neural networks. Proceedings of the 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India.","DOI":"10.1109\/IconDSC.2019.8816881"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1177\/0361198119862629","article-title":"Prediction of near-crashes from observed vehicle kinematics using machine learning","volume":"2673","author":"Osman","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_45","unstructured":"Hankey, J.M., Perez, M.A., and McClafferty, J.A. (2016). Description of the SHRP2 Naturalistic Database and the Crash, Near-Crash, and Baseline Data Sets, Virginia Tech Transportation Institute. Technical Report."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cook, D.J., and Krishnan, N.C. (2015). Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data, John Wiley & Sons.","DOI":"10.1002\/9781119010258"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_48","unstructured":"Duda, R.O., and Hart, P.E. (2006). Pattern Classification, John Wiley & Sons."},{"key":"ref_49","unstructured":"Tang, J., Alelyani, S., and Liu, H. (2022, February 10). Feature selection for classification: A review. Data Classification: Algorithms and Applications, Available online: www.cvs.edu.in\/upload\/feature_selection_for_classification.pdf."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: Introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_53","unstructured":"Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Sutskever, I., Le, Q.V., Vinyals, O., and Zaremba, W. (2014). Addressing the rare word problem in neural machine translation. arXiv.","DOI":"10.3115\/v1\/P15-1002"},{"key":"ref_55","unstructured":"Gamboa, J.C.B. (2017). Deep learning for time-series analysis. arXiv."},{"key":"ref_56","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_57","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kramer, O. (2013). K-nearest neighbors. Dimensionality Reduction with Unsupervised Nearest Neighbors, Springer.","DOI":"10.1007\/978-3-642-38652-7"},{"key":"ref_62","first-page":"275","article-title":"An introduction to decision tree modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom. A J. Chemom. Soc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random forests for classification in ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gouverneur, P., Li, F., Adamczyk, W.M., Szikszay, T.M., Luedtke, K., and Grzegorzek, M. (2021). Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition. Sensors, 21.","DOI":"10.3390\/s21144838"},{"key":"ref_65","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Savannah, GA, USA."},{"key":"ref_66","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_67","unstructured":"Zeiler, M.D. (2012). Adadelta: An adaptive learning rate method. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1006\/jmps.1999.1279","article-title":"Cross-validation methods","volume":"44","author":"Browne","year":"2000","journal-title":"J. Math. Psychol."},{"key":"ref_69","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X., and Krishnaswamy, S. (2015, January 25\u201331). Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the Ijcai, Buenos Aires, Argentina."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","article-title":"Convolutional neural networks for time series classification","volume":"28","author":"Zhao","year":"2017","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sadouk, L. (2019). CNN approaches for time series classification. Time Series Analysis-Data, Methods, and Applications, IntechOpen.","DOI":"10.5772\/intechopen.81170"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., Huang, X., and Grzegorzek, M. (2020). Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification. Sensors, 20.","DOI":"10.3390\/s20154271"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","article-title":"An embedded feature selection method for imbalanced data classification","volume":"6","author":"Liu","year":"2019","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3634\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:08:48Z","timestamp":1760137728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3634"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,10]]},"references-count":74,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103634"],"URL":"https:\/\/doi.org\/10.3390\/s22103634","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,10]]}}}