{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:59:59Z","timestamp":1783612799940,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"non-wearable and non-invasive photonic sleep monitoring system-based optical fiber sensor with machine learning","award":["HKPU 1-WZ01"],"award-info":[{"award-number":["HKPU 1-WZ01"]}]},{"name":"non-wearable and non-invasive photonic sleep monitoring system-based optical fiber sensor with machine learning","award":["HKPU 1-CD8N"],"award-info":[{"award-number":["HKPU 1-CD8N"]}]},{"name":"non-wearable and non-invasive photonic sleep monitoring system-based optical fiber sensor with machine learning","award":["HK RGC GRF 15209321 B-Q85G"],"award-info":[{"award-number":["HK RGC GRF 15209321 B-Q85G"]}]},{"name":"non-wearable and non-invasive photonic smart health monitoring system for atrial fibrillation diagnosis based on optical fiber sensor with machine learning","award":["HKPU 1-WZ01"],"award-info":[{"award-number":["HKPU 1-WZ01"]}]},{"name":"non-wearable and non-invasive photonic smart health monitoring system for atrial fibrillation diagnosis based on optical fiber sensor with machine learning","award":["HKPU 1-CD8N"],"award-info":[{"award-number":["HKPU 1-CD8N"]}]},{"name":"non-wearable and non-invasive photonic smart health monitoring system for atrial fibrillation diagnosis based on optical fiber sensor with machine learning","award":["HK RGC GRF 15209321 B-Q85G"],"award-info":[{"award-number":["HK RGC GRF 15209321 B-Q85G"]}]},{"name":"ultrafast and ultrahigh-resolution optical spectrum response measurement based on linear frequency modulation","award":["HKPU 1-WZ01"],"award-info":[{"award-number":["HKPU 1-WZ01"]}]},{"name":"ultrafast and ultrahigh-resolution optical spectrum response measurement based on linear frequency modulation","award":["HKPU 1-CD8N"],"award-info":[{"award-number":["HKPU 1-CD8N"]}]},{"name":"ultrafast and ultrahigh-resolution optical spectrum response measurement based on linear frequency modulation","award":["HK RGC GRF 15209321 B-Q85G"],"award-info":[{"award-number":["HK RGC GRF 15209321 B-Q85G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach\u2013Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.<\/jats:p>","DOI":"10.3390\/s24092672","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T08:08:27Z","timestamp":1713859707000},"page":"2672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing"],"prefix":"10.3390","volume":"24","author":[{"given":"Qichang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-4805","authenticated-orcid":false,"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-5466","authenticated-orcid":false,"given":"Weimin","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3185-0441","authenticated-orcid":false,"given":"Changyuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong"},{"name":"Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.12968\/bjha.2011.5.1.18","article-title":"Respiratory rate measurement: A comparison of methods","volume":"5","author":"Smith","year":"2011","journal-title":"Br. J. Healthc. Assist."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2398","DOI":"10.1109\/JBHI.2018.2880097","article-title":"Heart rate and heart rate variability from single-channel video and ICA integration of multiple signals","volume":"23","author":"Favilla","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network","volume":"10","author":"Kong","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13685","DOI":"10.1109\/JSEN.2021.3071882","article-title":"Assessment of heart rate and respiratory rate for perioperative infants based on ELC model","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4452","DOI":"10.1109\/JLT.2023.3250670","article-title":"Noninvasive Measurement of Vital Signs with the Optical Fiber Sensor Based on Deep Learning","volume":"41","author":"Wang","year":"2023","journal-title":"J. Light. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shin, Y.-S., and Kim, J. (2023). Sensor Data Reconstruction for Dynamic Responses of Structures Using External Feedback of Recurrent Neural Network. Sensors, 23.","DOI":"10.3390\/s23052737"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, M., Ye, X.-W., Jia, J.-D., Ying, X.-H., Ding, Y., Zhang, D., and Sun, F. (2024). Confining Pressure Forecasting of Shield Tunnel Lining Based on GRU Model and RNN Model. Sensors, 24.","DOI":"10.3390\/s24030866"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xie, H., Huang, Z., Leung, F.H.F., Ju, Y., Zheng, Y.-P., and Ling, S.H. (2023, January 5\u20138). A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkey.","DOI":"10.1109\/BIBM58861.2023.10385419"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wang, Q., Lyu, W., and Yu, C. (2023, January 4\u20137). A Deep Learning-based Model for Human Non-invasive Vital Sign Signal Monitoring with Optical Fiber Sensor. Proceedings of the 2023 Asia Communications and Photonics Conference\/2023 International Photonics and Optoelectronics Meetings (ACP\/POEM), Wuhan, China.","DOI":"10.1109\/ACP\/POEM59049.2023.10369647"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"\u0130\u015fbitirici, A., Giarr\u00e9, L., Xu, W., and Falcone, P. (2024). LSTM-Based Virtual Load Sensor for Heavy-Duty Vehicles. Sensors, 24.","DOI":"10.3390\/s24010226"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2016.12.004","article-title":"A comparative review on sleep stage classification methods in patients and healthy individuals","volume":"140","author":"Boostani","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Toso, F., Milanizadeh, M., Zanetto, F., Grimaldi, V., Melloni, A., Sampietro, M., Morichetti, F., and Ferrari, G. (2021, January 7\u201310). Self-Stabilized Silicon Mach-Zehnder Interferometers by Integrated CMOS Controller. Proceedings of the 2021 IEEE 17th International Conference on Group IV Photonics (GFP), Malaga, Spain.","DOI":"10.1109\/GFP51802.2021.9673835"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dar, M.N., Akram, M.U., Khawaja, S.G., and Pujari, A.N. (2020). CNN and LSTM-Based Emotion Charting Using Physiological Signals. Sensors, 20.","DOI":"10.3390\/s20164551"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/72.97934","article-title":"A general regression neural network","volume":"2","author":"Specht","year":"1991","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_15","first-page":"4452","article-title":"Sleep condition detection and assessment with optical fiber interferometer based on machine learning","volume":"26","author":"Wang","year":"2023","journal-title":"iScience"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vijayasankar, A., and Kumar, P.R. (2018, January 4\u20135). Correction of blink artifacts from single channel EEG by EMD-IMF thresholding. Proceedings of the 2018 Conference on Signal Processing and Communication Engineering Systems (SPACES), Vijayawada, India.","DOI":"10.1109\/SPACES.2018.8316340"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"S4253","DOI":"10.21037\/jtd.2018.10.117","article-title":"Comorbid obstructive sleep apnoea and chronic obstructive pulmonary disease and the risk of cardiovascular disease","volume":"10","author":"McNicholas","year":"2018","journal-title":"J. Thorac. Dis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"68514","DOI":"10.1109\/ACCESS.2020.2985286","article-title":"Remote Measurement of Human Vital Signs Based on Joint-Range Adaptive EEMD","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/0013-4694(69)90021-2","article-title":"A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects","volume":"26","author":"Wolpert","year":"1969","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3389\/fnagi.2014.00325","article-title":"Buying time: A rationale for examining the use of circadian rhythm and sleep interventions to delay progression of mild cognitive impairment to Alzheimer\u2019s disease","volume":"6","author":"Landry","year":"2014","journal-title":"Front. Aging Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s10916-010-9480-5","article-title":"Employment and comparison of different artificial neural networks for epilepsy diagnosis from eeg signals","volume":"36","author":"Sezer","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ulina, M., Purba, R., and Halim, A. (2020, January 3\u20134). Foreign Exchange Prediction using DEMA and Improved FA-LSTM. Proceedings of the 2020 Fifth International Conference on Informatics and Computing (ICIC), Gorontalo, Indonesia.","DOI":"10.1109\/ICIC50835.2020.9288615"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, P., and Wang, M. (2021, January 19\u201321). Variation Characteristics Analysis and Short-Term Forecasting of Load Based on DEMA. Proceedings of the 2021 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE 2021), Seoul, Republic of Korea.","DOI":"10.1145\/3459104.3459185"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"H, S., and Venkataraman, N. (2023). Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications. Sensors, 23.","DOI":"10.3390\/s23062913"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13702","DOI":"10.1109\/JSEN.2023.3272646","article-title":"Non-Invasive Human Ballistocardiography Assessment with the Optical Fiber Sensor Based on Deep Learning","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Juhasz, Z. (2023). GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time\u2013Frequency Analysis. Sensors, 23.","DOI":"10.3390\/s23208654"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/978-3-642-24955-6_87","article-title":"Eeg-based emotion recognition using frequency domain features and support vector machines","volume":"7062","author":"Wang","year":"2011","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Piuzzi, E., Pisa, S., Pittella, E., Podest\u00e0, L., and Sangiovanni, S. (2020). Wearable Belt with Built-In Textile Electrodes for Cardio\u2014Respiratory Monitoring. Sensors, 20.","DOI":"10.3390\/s20164500"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2672\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:32:42Z","timestamp":1760106762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2672"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,23]]},"references-count":28,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092672"],"URL":"https:\/\/doi.org\/10.3390\/s24092672","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,23]]}}}