{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:02:46Z","timestamp":1772834566979,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543830","type":"print"},{"value":"9789819543847","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4384-7_33","type":"book-chapter","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T15:42:48Z","timestamp":1762357368000},"page":"474-489","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory Approach for PPG-Based Stress Monitoring from Wrist Worn Wearables"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1831-5843","authenticated-orcid":false,"given":"Md Santo","family":"Ali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1618-3772","authenticated-orcid":false,"given":"Mohammod Abdul","family":"Motin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-9776","authenticated-orcid":false,"given":"El-Sayed M.","family":"El-Alfy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-8348","authenticated-orcid":false,"given":"Mufti","family":"Mahmud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"2","key":"33_CR1","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1049\/htl.2016.0006","volume":"3","author":"A Alqaraawi","year":"2016","unstructured":"Alqaraawi, A., Alwosheel, A., Alasaad, A.: Heart rate variability estimation in photoplethysmography signals using Bayesian learning approach. Healthcare Technol. Let. 3(2), 136\u2013142 (2016)","journal-title":"Healthcare Technol. Let."},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Alshamrani, M.: An advanced stress detection approach based on processing data from wearable wrist devices. Int. J. Adv. Comput. Sci. Appl. 12(7), 399\u2013405 (2021)","DOI":"10.14569\/IJACSA.2021.0120745"},{"issue":"5","key":"33_CR3","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1109\/JBHI.2021.3138639","volume":"27","author":"W-K Beh","year":"2021","unstructured":"Beh, W.-K., Wu, Y.-H., Wu, A.-Y.: Robust PPG-based mental workload assessment system using wearable devices. IEEE J. Biomed. Health Inform. 27(5), 2323\u20132333 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"33_CR4","doi-asserted-by":"publisher","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani, M.M., Ghatee, M.: A systematic review on overfitting control in shallow and deep neural networks. Artif. Intell. Rev. 54(8), 6391\u20136438 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Benchekroun, M., et al.: Comparison of stress detection through ECG and PPG signals using a random forest-based algorithm. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2022)","DOI":"10.1109\/EMBC48229.2022.9870984"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Bobade, P., Vani, M.: Stress detection with machine learning and deep learning using multimodal physiological data. In: Second International Conference on Inventive Research in Computing Applications (ICIRCA). Coimbatore, India (2020)","DOI":"10.1109\/ICIRCA48905.2020.9183244"},{"issue":"18","key":"33_CR8","doi-asserted-by":"publisher","first-page":"5312","DOI":"10.3390\/s20185312","volume":"20","author":"S Elzeiny","year":"2020","unstructured":"Elzeiny, S., Qaraqe, M.: Stress classification using photoplethysmogram-based spatial and frequency domain images. Sensors 20(18), 5312 (2020)","journal-title":"Sensors"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Esler, M.: Mental stress and human cardiovascular disease. Neurosci. Biobehav. Rev. 74(B), 269\u2013276 (2017)","DOI":"10.1016\/j.neubiorev.2016.10.011"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Gasparini, F., Grossi, A., Bandini, S.: A deep learning approach to recognize cognitive load using PPG signals. In: 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA). New York, NY, USA (2021)","DOI":"10.1145\/3453892.3461625"},{"issue":"9","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1933","DOI":"10.1016\/j.immuni.2021.08.023","volume":"54","author":"H Haykin","year":"2021","unstructured":"Haykin, H., Rolls, A.: The neuroimmune response during stress: a physiological perspective. Immunity 54(9), 1933\u20131947 (2021)","journal-title":"Immunity"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Jahanjoo, A., TaheriNejad, N., Aminifar, A.: High-accuracy stress detection using wrist-worn PPG sensors. In: IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, (2024)","DOI":"10.1109\/ISCAS58744.2024.10558012"},{"issue":"2","key":"33_CR13","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1080\/03772063.2020.1844068","volume":"69","author":"P Kalra","year":"2023","unstructured":"Kalra, P., Sharma, V.: Mental stress assessment using PPG signal a deep neural network approach. IETE J. Res. 69(2), 879\u2013885 (2023)","journal-title":"IETE J. Res."},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"1302794","DOI":"10.3389\/fpubh.2023.1302794","volume":"11","author":"H Kim","year":"2023","unstructured":"Kim, H., et al.: Machine learning-based classification analysis of knowledge worker mental stress. Front. Public Health 11, 1302794 (2023)","journal-title":"Front. Public Health"},{"issue":"13","key":"33_CR15","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.3390\/electronics12132923","volume":"12","author":"KB Kim","year":"2023","unstructured":"Kim, K.B., Baek, H.J.: Photoplethysmography in wearable devices: a comprehensive review of technological advances, current challenges, and future directions. Electronics 12(13), 2923 (2023)","journal-title":"Electronics"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Ali, M.S., Motin, M.A., Mahmud, M.: A dual path hybrid convolutional neural network and bidirectional long-short term memory approach for PPG-based stress monitoring. In: 4th Muslims in ML Workshop co-located with ICML. Vancouver, Canada (2025)","DOI":"10.22541\/au.175329643.35949288\/v1"},{"key":"33_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1D convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)","journal-title":"Mech. Syst. Signal Process."},{"issue":"8","key":"33_CR18","doi-asserted-by":"publisher","first-page":"634","DOI":"10.3109\/03091900903150998","volume":"33","author":"G Lu","year":"2019","unstructured":"Lu, G., Yang, F., Taylor, J.A., Stein, J.F.: A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects. J. Med. Eng. Technol. 33(8), 634\u2013641 (2009)","journal-title":"J. Med. Eng. Technol."},{"issue":"1","key":"33_CR19","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1186\/s12911-023-02350-w","volume":"23","author":"M Masoumian Hosseini","year":"2023","unstructured":"Masoumian Hosseini, M., Masoumian Hosseini, S.T., Qayumi, K., Hosseinzadeh, S., Sajadi Tabar, S.S.: Smartwatches in healthcare medicine: assistance and monitoring; a scoping review. BMC Med. Inform. Decis. Mak. 23(1), 248 (2023)","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"02","key":"33_CR20","doi-asserted-by":"publisher","first-page":"1650041","DOI":"10.1142\/S0129065716500416","volume":"27","author":"OM Mozos","year":"2017","unstructured":"Mozos, O.M., et al.: Stress detection using wearable physiological and sociometric sensors. Int. J. Neural Syst. 27(02), 1650041 (2017)","journal-title":"Int. J. Neural Syst."},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Rashid, N., Chen, L., Dautta, M., Jimenez, A., Tseng, P., Al Faruque, M.A.: Feature augmented hybrid cnn for stress recognition using wrist-based photoplethysmography sensor. In: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Mexico (2021)","DOI":"10.1109\/EMBC46164.2021.9630576"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: 20th ACM International Conference on Multimodal Interaction. New York, NY, USA (2018)","DOI":"10.1145\/3242969.3242985"},{"issue":"11","key":"33_CR23","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673\u20132681 (1997)","journal-title":"IEEE Trans. Signal Process."},{"key":"33_CR24","unstructured":"Sharfuddin, A.A., Tihami, M.N., Islam, M.S.: A deep recurrent neural network with BiLSTM model for sentiment classification. In: International Conference on Bangla Speech and Language Processing (ICBSLP). Sylhet, Bangladesh (2018)"},{"issue":"8","key":"33_CR25","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.3390\/ijerph21081077","volume":"21","author":"AY Shchaslyvyi","year":"2024","unstructured":"Shchaslyvyi, A.Y., Antonenko, S.V., Telegeev, G.D.: Comprehensive review of chronic stress pathways and the efficacy of behavioral stress reduction programs (BSRPs) in managing diseases. Int. J. Environ. Res. Public Health 21(8), 1077 (2024)","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"3","key":"33_CR26","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/computation11030052","volume":"11","author":"MM Taye","year":"2023","unstructured":"Taye, M.M.: Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions. Computation 11(3), 52 (2023)","journal-title":"Computation"},{"key":"33_CR27","unstructured":"Wang, Y.: A new concept using LSTM neural networks for dynamic system identification. In: American Control Conference (ACC). Seattle, WA, USA (2017)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4384-7_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:07:46Z","timestamp":1772806066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4384-7_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"ISBN":["9789819543830","9789819543847"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4384-7_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"6 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}