{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T09:01:58Z","timestamp":1785402118912,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Technology and Business University (BTBU) Undergraduate Innovation and Entrepreneurship Training Program","award":["X202610011003"],"award-info":[{"award-number":["X202610011003"]}]},{"award":["X202610011003"],"award-info":[{"award-number":["X202610011003"]}],"id":[{"id":"https:\/\/ror.org\/013e0zm98","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO2 and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN\u2013LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland\u2013Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop\u2014fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals\u2014supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios.<\/jats:p>","DOI":"10.3390\/s26134003","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T03:20:21Z","timestamp":1782357621000},"page":"4003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multimodal Closed-Loop Framework for Vital Sign Monitoring and Intelligent Diagnosis of Amusement Ride Passengers Under High-Dynamic Motion"],"prefix":"10.3390","volume":"26","author":[{"given":"Yikun","family":"Wu","sequence":"first","affiliation":[{"name":"School of Design and Art, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4716-6325","authenticated-orcid":false,"given":"Yulong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0426-6584","authenticated-orcid":false,"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Design and Art, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14419\/ijet.v7i4.11.20677","article-title":"Wearable sensor and internet of things technology for better medical science: A review","volume":"7","author":"Putri","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107757","DOI":"10.1016\/j.measurement.2020.107757","article-title":"Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction","volume":"159","author":"Fouad","year":"2020","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gupta, V., Ingle, A., Gaikwad, D., and Vibhute, M. (2021). Patient Health Monitoring and Diagnosis Using IoT and Machine Learning. Inventive Communication and Computational Technologies, Springer.","DOI":"10.1007\/978-981-15-7345-3_24"},{"key":"ref_4","first-page":"165","article-title":"AI, IoT and Robotics in the Medical and Healthcare Field","volume":"15","author":"Kavidha","year":"2021","journal-title":"AI IoT-Based Intell. Autom. Robot."},{"key":"ref_5","first-page":"7941","article-title":"Artificial Intelligence (AI) and Internet of Things (IoT) in Healthcare: Opportunities and Challenges","volume":"107","author":"Thakare","year":"2021","journal-title":"SPAST Abstr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Prithi, S., Poongodi, T., Sumathi, D., and Suresh, P. (2021). Real-time remote health monitoring using IoT sensors. Electronic Devices, Circuits, and Systems for Biomedical Applications, Elsevier.","DOI":"10.1016\/B978-0-323-85172-5.00006-X"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dang, L.M., Piran, M., Han, D., Min, K., and Moon, H. (2019). A survey on internet of things and cloud computing for healthcare. Electronics, 8.","DOI":"10.3390\/electronics8070768"},{"key":"ref_8","unstructured":"Isravel, D.P., and Silas, S. (2020). A comprehensive review on the emerging IoT-cloud based technologies for smart healthcare. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6\u20137 March 2020, IEEE."},{"key":"ref_9","first-page":"447","article-title":"A review on internet of things and its applications in healthcare","volume":"10","author":"Singh","year":"2017","journal-title":"J. Chem. Pharm. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kristoffersson, A., and Lind\u00e9n, M. (2020). A systematic review on the use of wearable body sensors for health monitoring: A qualitative synthesis. Sensors, 20.","DOI":"10.3390\/s20051502"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10877-021-00666-4","article-title":"Adaptive threshold-based alarm strategies for continuous vital signs monitoring","volume":"36","author":"Vlaskamp","year":"2022","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_12","first-page":"4071474","article-title":"IoT-based ambulatory vital signs data transfer system","volume":"2018","author":"Misbahuddin","year":"2018","journal-title":"J. Comput. Netw. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41928-019-0258-6","article-title":"Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor","volume":"2","author":"Mercuri","year":"2019","journal-title":"Nat. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"54819","DOI":"10.1109\/ACCESS.2019.2913189","article-title":"Vital sign measurement in telemedicine rehabilitation based on intelligent wearable medical devices","volume":"7","author":"Fan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6027","DOI":"10.1007\/s12652-020-02656-x","article-title":"The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: A systematic literature review","volume":"14","author":"Mohammadzadeh","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/ACCESS.2020.3045115","article-title":"Edge intelligence and Internet of Things in healthcare: A survey","volume":"9","author":"Amin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, J., Ling, Y., and Wang, J. (2017). A novel denoising algorithm for acceleration signal based on compressed sensing. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China, 29\u201331 July 2017, IEEE.","DOI":"10.1109\/FSKD.2017.8393396"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108061","DOI":"10.1016\/j.soildyn.2023.108061","article-title":"Structural floor acceleration denoising method using generative adversarial network","volume":"173","author":"Shen","year":"2023","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hon, T., Subramaniam, S., and Georgakis, A. (2010). STFT-based denoising of biomechanical impact signals. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, 31 August\u20134 September 2010, IEEE.","DOI":"10.1109\/IEMBS.2010.5628013"},{"key":"ref_20","first-page":"14","article-title":"Wall acceleration signal-denoising via dual-adaptive lifting scheme","volume":"28","author":"Sun","year":"2009","journal-title":"J. Vib. Shock."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Z., Cao, Z., Yang, M., and Chen, C. (2024). The Denoising Method for MEMS Acceleration Signals Based on CEEMDAN-ASA. Proceedings of the 2024 3rd International Conference on Electronics and Information Technology (EIT), Chengdu, China, 20\u201322 September 2024, IEEE.","DOI":"10.1109\/EIT63098.2024.10762615"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, G., Zhang, H., You, L., Yang, Y., Zhou, H., Zhang, B., Chen, W., and Liu, L. (2023). Joint denoising method of seismic velocity signal and acceleration signals based on independent component analysis. Front. Earth Sci., 11.","DOI":"10.3389\/feart.2023.1178861"},{"key":"ref_23","unstructured":"Wu, J., and Xu, H. (2015). New Method for Compression and Reconstruction of Acceleration Signal Using EMD and Wavelet Denoising. Comput. Syst. Appl., 24."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, W., Guo, Y., Huang, B., Zhao, G.R., Liu, B.Q., and Wang, L. (2011). Analysis of filtering methods for 3D acceleration signals in body sensor network. International Symposium on Bioelectronics and Bioinformations, IEEE.","DOI":"10.1109\/ISBB.2011.6107697"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qu, G., Zhao, F., Liu, G., and Liu, H. (2009). Adaptive MEMS gyroscope denoising method based on the \u00e0 trous wavelet transform. Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments, Beijing, China, 16\u201319 August 2009, IEEE.","DOI":"10.1109\/ICEMI.2009.5274468"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Su, Y., CHan Han, J., Wang, X., Wang, Y., and Ji, Z. (2023). Research on Magnetohydrodynamic Angular Rate Sensor Denoising for a Space Laser Stabilization Control System. Appl. Sci., 13.","DOI":"10.3390\/app13105895"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, B., Li, R., and Cui, Y. (2023). Angular Velocity Detection of Spinning Object Based on Orbital Angular Momentum. Proceedings of the 2023 16th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT), Guangzhou, China, 31 August\u20133 September 2023, IEEE.","DOI":"10.1109\/UCMMT58116.2023.10310569"},{"key":"ref_28","unstructured":"Zou, X.-G. (2015). Study on denoising method of gear angular domain signal based on improved EMD. Min. Process. Equip."},{"key":"ref_29","first-page":"1224425","article-title":"Denoising acceleration by jerk constraint in IMU sensor","volume":"12244","author":"Huang","year":"2022","journal-title":"Proc. SPIE"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, Z., Wang, X., and Li, Z. (2016). Simulation on Denoising Method for Fiber Optic Gyroscope Signal. Proceedings of the 2016 International Conference on Wireless Communication and Sensor Network, Atlantis Press.","DOI":"10.2991\/icwcsn-16.2017.10"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bodile, R., and Rao, T. (2020). ECG Denoising Using Cubature Quadrature Kalman Filter Approach. Proceedings of the 2020 IEEE India Council International Subsections Conference (INDISCON), Jaipur, India, 3\u20134 October 2020, IEEE.","DOI":"10.1109\/INDISCON50162.2020.00052"},{"key":"ref_32","first-page":"71","article-title":"Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition","volume":"37","author":"Geng","year":"2020","journal-title":"J. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012126","DOI":"10.1088\/1742-6596\/2224\/1\/012126","article-title":"Heart Rate Estimation Algorithm Based on Normalized Least Mean Square combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise","volume":"2224","author":"Cheng","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"128001","DOI":"10.1088\/1361-6579\/ac9bc8","article-title":"An empirical study of modified beat SQI based majority voting fusion method for heart-rate estimation in noisy multimodal cardiovascular signals","volume":"43","author":"Rankawat","year":"2022","journal-title":"Physiol. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pandey, R., and Chao, P. (2021). An Adaptive Analog Front End for a Flexible PPG Sensor Patch with Self-Determined Motion Related DC Drift Removal. Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Republic of Korea, 23\u201326 May 2021, IEEE.","DOI":"10.1109\/ISCAS51556.2021.9401265"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Xu, J. (2021). Denoising of ECG Signals Based on CEEMDAN. Proceedings of the 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China, 9\u201311 April 2021, IEEE.","DOI":"10.1109\/ICSP51882.2021.9408721"},{"key":"ref_37","first-page":"5145","article-title":"Noise Removal in ECG Signal Using Digital Filters","volume":"7","author":"Sasirekha","year":"2020","journal-title":"Eur. J. Mol. Clin. Med."},{"key":"ref_38","first-page":"324","article-title":"Simulink implementation of RLS algorithm for resilient artefacts removal in ECG signal","volume":"16","author":"Tejaswi","year":"2020","journal-title":"Int. J. Adv. Intell. Paradig."},{"key":"ref_39","first-page":"23","article-title":"Removal of Ecg signals artifacts using multistage adaptive giltering technique","volume":"15","author":"Durrani","year":"2020","journal-title":"J. Mech. Contin. Math. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhan, L., Yang, X., Sun, Y., Bai, Q., Xu, Y., and Zhou, X. (2023). Variational Modal Decomposition Joint Independent Component Analysis of PPG Noise Reduction Algorithm for Blood Oxygen Calculation. Proceedings of the 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Taizhou, China, 28\u201330 October 2023, IEEE.","DOI":"10.1109\/CISP-BMEI60920.2023.10373362"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"251","DOI":"10.2298\/SJEE2402251R","article-title":"Wavelet transform generated inherent noise reference for adaptive filtering to de-noise pulse oximeter signals","volume":"21","author":"Rao","year":"2024","journal-title":"Serbian J. Electr. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"012086","DOI":"10.1088\/1742-6596\/2246\/1\/012086","article-title":"Continuous blood oxygen estimation using PPG based on VMD","volume":"2246","author":"Tang","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3732","DOI":"10.1109\/JSEN.2019.2960370","article-title":"Photoplethysmography Motion Artifacts Removal Based on Signal-Noise Interaction Modeling Utilizing Envelope Filtering and Time-Delay Neural Network","volume":"20","author":"Xu","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Iino, K., and Nakao, M. (2013, January 4\u20137). A fatal accident case and lessons for entertainment engineering. Proceedings of the ASME 2013 International Design Engineering Technical Conferences, Portland, Oregon.","DOI":"10.1115\/DETC2013-12133"},{"key":"ref_45","unstructured":"ASM International (2019). Fatigue Fracture of a Shaft for an Amusement Ride Because of Undercuts in Welds; ASM Failure Analysis Case Histories, ASM International."},{"key":"ref_46","unstructured":"Zampella, A. (2018). Passenger Restraint Device for Amusement Rides. (US20150232059A1), U.S. Patent."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shad, M., and Hasan, F. (2018, January 9\u201310). Failure of giant wheel ride at an amusement park. Proceedings of the 7th International Conference on Fracture Fatigue and Wear, Ghent, Belgium.","DOI":"10.1007\/978-981-13-0411-8_33"},{"key":"ref_48","unstructured":"State Administration for Market Regulation and Standardization Administration of China (2018). GB 8408\u20142018 Safety Specification for Large-Scale Amusement Rides."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"650","DOI":"10.14429\/dsj.59.1571","article-title":"Microelectromechnical systems inertial measurement unit error modelling and error analysis for low-cost strapdown inertial navigation system","volume":"59","author":"Ramalingam","year":"2009","journal-title":"Def. Sci. J."},{"key":"ref_50","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shashikumar, S.P., Shah, A.J., Li, Q., Clifford, G.D., and Nemati, S. (2017). A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. Proceedings of the 4th IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Orlando, FL, USA, 16\u201319 February 2017, IEEE.","DOI":"10.1109\/BHI.2017.7897225"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"172692","DOI":"10.1109\/ACCESS.2020.3025374","article-title":"Atrial fibrillation identification with PPG signals using a combination of time-frequency analysis and deep learning","volume":"8","author":"Cheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Aliamiri, A., and Shen, Y. (2018). Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor. Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA, 4\u20137 March 2018, IEEE.","DOI":"10.1109\/BHI.2018.8333463"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1007\/s11760-022-02152-z","article-title":"Dynamic time warping based arrhythmia detection using photoplethysmography signals","volume":"16","author":"Dogra","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e023555","DOI":"10.1161\/JAHA.121.023555","article-title":"Multiclass arrhythmia detection and classification from photoplethysmography signals using a deep convolutional neural network","volume":"11","author":"Liu","year":"2022","journal-title":"J. Am. Heart Assoc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T03:30:26Z","timestamp":1782358226000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/13\/4003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":55,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["s26134003"],"URL":"https:\/\/doi.org\/10.3390\/s26134003","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]}}}