{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:22:50Z","timestamp":1781763770597,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-026-00837-9","type":"journal-article","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T05:22:39Z","timestamp":1768108959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A deep learning approach with wearable physiological sensors and feature selection methods to detect mental stress in Indian housewives"],"prefix":"10.1007","volume":"6","author":[{"given":"Shruti","family":"Gedam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prashant","family":"Pranav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandip","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritesh","family":"Jha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"837_CR1","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1146\/annurev.clinpsy.1.102803.144141","volume":"1","author":"N Schneiderman","year":"2005","unstructured":"Schneiderman N, Ironson G, Siegel SD. Stress and health: psychological, behavioral, and biological determinants. Ann Rev Clin Psychol. 2005;1:607\u201328. https:\/\/doi.org\/10.1146\/annurev.clinpsy.1.102803.144141.","journal-title":"Ann Rev Clin Psychol"},{"key":"837_CR2","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1007\/s11469-022-00910-1","volume":"21","author":"V Kaplan","year":"2023","unstructured":"Kaplan V. Mental health States of housewives: an evaluation in terms of Self-perception and codependency. Int J Ment Health Addict. 2023;21:666\u201383. https:\/\/doi.org\/10.1007\/s11469-022-00910-1.","journal-title":"Int J Ment Health Addict"},{"key":"837_CR3","first-page":"3","volume":"1","author":"YI Yuehong","year":"2016","unstructured":"Yuehong YI, Zeng Y, Chen X, Fan Y. The internet of things in healthcare: an overview. J Industrial Inform Integr. 2016;1:3\u201313.","journal-title":"J Industrial Inform Integr"},{"key":"837_CR4","doi-asserted-by":"crossref","unstructured":"Malik M, Camm AJ. Heart rate variability. Clin Cardiol. 1990;13(8):570\u2013576.","DOI":"10.1002\/clc.4960130811"},{"key":"837_CR5","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/s42452-021-04427-5","volume":"3","author":"J Zhang","year":"2021","unstructured":"Zhang J, Zeng Y, Starly B. Recurrent neural networks with long term Temporal dependencies in machine tool wear diagnosis and prognosis. SN Appl Sci. 2021;3:442. https:\/\/doi.org\/10.1007\/s42452-021-04427-5.","journal-title":"SN Appl Sci"},{"key":"837_CR6","doi-asserted-by":"publisher","first-page":"14517","DOI":"10.1007\/s12144-021-02636-0","volume":"42","author":"M Durak","year":"2023","unstructured":"Durak M, Senol-Durak E, Karakose S. Psychological distress and anxiety among housewives: the mediational role of perceived Stress, Loneliness, and housewife burnout. Curr Psychol. 2023;42:14517\u201328. https:\/\/doi.org\/10.1007\/s12144-021-02636-0.","journal-title":"Curr Psychol"},{"issue":"11","key":"837_CR7","first-page":"1398","volume":"5","author":"M Maqbool","year":"2014","unstructured":"Maqbool M, Shrivastava N, Pandey M. A comparative study of mental health of working women and housewives. Indian J Health Wellbeing. 2014;5(11):1398\u2013400.","journal-title":"Indian J Health Wellbeing"},{"key":"837_CR8","doi-asserted-by":"publisher","unstructured":"Gedam S, Paul S. A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access. 2021;9:84045\u201384066.https:\/\/doi.org\/10.1109\/ACCESS.2021.3085502","DOI":"10.1109\/ACCESS.2021.3085502"},{"issue":"4","key":"837_CR9","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3390\/s21041030","volume":"21","author":"J Chen","year":"2021","unstructured":"Chen J, Abbod M, Shieh J-S. Pain and stress detection using wearable sensors and Devices\u2014A. Rev Sens. 2021;21(4):1030. https:\/\/doi.org\/10.3390\/s21041030.","journal-title":"Rev Sens"},{"key":"837_CR10","doi-asserted-by":"publisher","unstructured":"Said Y, Arnrich B, Ersoy C. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. J Biomed Inf. 2019;92:103139. https:\/\/doi.org\/10.1016\/j.jbi.2019.103139","DOI":"10.1016\/j.jbi.2019.103139"},{"key":"837_CR11","doi-asserted-by":"publisher","unstructured":"Hasanbasic A, Spahic M, Bosnjic D, adzic HH, Mesic V, Jahic O. Recognition of stress levels among students with wearable sensors. In: 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina, 2019, pp. 1\u20134. https:\/\/doi.org\/10.1109\/INFOTEH.2019.8717754","DOI":"10.1109\/INFOTEH.2019.8717754"},{"key":"837_CR12","doi-asserted-by":"publisher","unstructured":"Uday S, Jyotsna C, Amudha J. Detection of Stress using Wearable Sensors in IoT Platform, 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, 2018, pp. 492\u2013498. https:\/\/doi.org\/10.1109\/ICICCT.2018.8473010","DOI":"10.1109\/ICICCT.2018.8473010"},{"issue":"8","key":"837_CR13","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.3390\/s21082873","volume":"21","author":"KM Dalmeida","year":"2021","unstructured":"Dalmeida KM, Masala GL. HRV features as viable physiological markers for stress detection using wearable devices. Sensors. 2021;21(8):2873. https:\/\/doi.org\/10.3390\/s21082873.","journal-title":"Sensors"},{"key":"837_CR14","doi-asserted-by":"crossref","unstructured":"Ghadi YY, Faisal S, Shah A, Mazhar T, Shahzad T, Ouahada K. Enhancing patient healthcare with mobile edge computing and 5G: challenges and solutions for secure online health tools. J Cloud Comput. 2024;13(1):93.","DOI":"10.1186\/s13677-024-00654-4"},{"key":"837_CR15","doi-asserted-by":"publisher","unstructured":"Ghadi YY, Mazhar T, Shahzad T, Amir M, Alrazaq AA, Ahmed A, et al. The role of blockchain to secure internet of medical things. Sci Rep. 2024;14(1):18422. https:\/\/doi.org\/10.1038\/s41598-024-68529-x","DOI":"10.1038\/s41598-024-68529-x"},{"key":"837_CR16","doi-asserted-by":"publisher","unstructured":"Khan S, Mazhar T, Shahzad T, Bibi A, Ahmad W, Amir M. Discover sustainability antenna systems for IoT applications: a review. Discov Sustain. 2024;5(1):412. https:\/\/doi.org\/10.1007\/s43621-024-00638-z","DOI":"10.1007\/s43621-024-00638-z"},{"key":"837_CR17","doi-asserted-by":"publisher","unstructured":"Mamoon M, Saeed RA, Saeed ES, Ali T, Mazhar ZE, Ahmed T, Shahzad Skhan, Hamam H. A lightweight protocol to enhance privacy in wireless-enabled 5g networks for industrial internet of things (IIoT) communications. Secur Privacy. 2025;8(5):e70083. https:\/\/doi.org\/10.1002\/spy2.70083","DOI":"10.1002\/spy2.70083"},{"key":"837_CR18","doi-asserted-by":"publisher","first-page":"397","DOI":"10.3390\/bios13030397","volume":"13","author":"H Barki","year":"2023","unstructured":"Barki H, Chung W-Y. Mental stress detection using a wearable. In-Ear Plethysmography Biosens. 2023;13:397. https:\/\/doi.org\/10.3390\/bios13030397.","journal-title":"In-Ear Plethysmography Biosens"},{"key":"837_CR19","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3390\/bios12060427","volume":"12","author":"MB Bin Heyat","year":"2022","unstructured":"Bin Heyat MB, Akhtar F, Abbas SJ, Al-Sarem M, Alqarafi A, Stalin A, Abbasi R, Muaad AY, Lai D, Wu KW. Flexible electronics based cardiac electrode for researcher mental stress detection system using machine learning models on single lead electrocardiogram signal. Biosensors. 2022;12:427. https:\/\/doi.org\/10.3390\/bios12060427.","journal-title":"Biosensors"},{"issue":"10","key":"837_CR20","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MAES.2021.3115198","volume":"37","author":"M Gil-martin","year":"2022","unstructured":"Gil-martin M, San-segundo R, Mateos A, Ferreiros-lopez J, Universitaria C. Feature article: human stress detection with wearable sensors using convolutional neural networks. IEEE Aerosp Electron Syst Mag. 2022;37(10):60\u201370.","journal-title":"IEEE Aerosp Electron Syst Mag"},{"issue":"August","key":"837_CR21","doi-asserted-by":"publisher","first-page":"149861","DOI":"10.1109\/ACCESS.2024.3461588","volume":"12","author":"S Kumar","year":"2024","unstructured":"Kumar S, Chauhan ARAJ, Kumar A, Member S, Yang G. Resp-BoostNet: mental stress detection from biomarkers measurable by smartwatches using boosting neural network technique. IEEE Access. 2024;12:149861\u201374.","journal-title":"IEEE Access"},{"key":"837_CR22","doi-asserted-by":"publisher","first-page":"5085","DOI":"10.3390\/s24165085","volume":"24","author":"MK Moser","year":"2024","unstructured":"Moser MK, Ehrhart M, Resch B. An explainable deep learning approach for stress detection in wearable sensor measurements. Sensors. 2024;24:5085. https:\/\/doi.org\/10.3390\/s24165085.","journal-title":"Sensors"},{"key":"837_CR23","doi-asserted-by":"publisher","first-page":"5373","DOI":"10.3390\/s24165373s","volume":"24","author":"L Abdul Kader","year":"2024","unstructured":"Abdul Kader L, Al-Shargie F, Tariq U, Al-Nashash H. One-ChannelWearable mental stress state monitoring system. Sensors. 2024;24:5373. https:\/\/doi.org\/10.3390\/s24165373s.","journal-title":"Sensors"},{"key":"837_CR24","unstructured":"Smith BD, Tola K, Mann M. Caffeine and arousal: A biobehavioral theory of physiological, behavioral, and emotional effects. InCaffeine and Behavior: Current Views & Research Trends. CRC Press; 2020 Mar 5. pp. 87\u2013135."},{"issue":"8","key":"837_CR25","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.3390\/brainsci13081177","volume":"13","author":"S Zahar","year":"2023","unstructured":"Zahar S, De Longis E, Hudry J. Revealing the acute effects of dietary components on mood and cognition: the role of autonomic nervous system responses. Brain Sci. 2023;13(8):1177.","journal-title":"Brain Sci"},{"issue":"3","key":"837_CR26","first-page":"163","volume":"13","author":"A Goshvarpour","year":"2016","unstructured":"Goshvarpour A, et al. Fusion framework for emotional electrocardiogram and galvanic skin response recognition: applying wavelet transform. Iran J Med Phys. 2016;13(3):163\u201373.","journal-title":"Iran J Med Phys"},{"key":"837_CR27","doi-asserted-by":"crossref","unstructured":"Bobade P, Vani M. Stress detection with machine learning and deep learning using multimodal physiological data. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE. pp. 51\u201357. 2020.","DOI":"10.1109\/ICIRCA48905.2020.9183244"},{"key":"837_CR28","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","volume":"5","author":"F Shaffer","year":"2017","unstructured":"Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. https:\/\/doi.org\/10.3389\/fpubh.2017.00258.","journal-title":"Front Public Health"},{"key":"837_CR29","doi-asserted-by":"publisher","first-page":"100085","DOI":"10.1016\/j.smhl.2019.100085","volume":"14","author":"O Dehzangi","year":"2019","unstructured":"Dehzangi O, Sahu V, Rajendra V, Taherisadr M. GSR-based distracted driving identification using discrete & continuous decomposition and wavelet packet transform. Smart Health. 2019;14:100085.","journal-title":"Smart Health"},{"issue":"6","key":"837_CR30","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1097\/00000542-200612000-00011","volume":"105","author":"R Lenhardt","year":"2006","unstructured":"Lenhardt R, Sessler DI. Estimation of mean body temperature from mean skin and core temperature. Anesthesiology. 2006;105(6):1117\u201321. https:\/\/doi.org\/10.1097\/00000542-200612000-00011.","journal-title":"Anesthesiology"},{"key":"837_CR31","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"837_CR32","first-page":"7","volume":"12","author":"SC Nandipati","year":"2020","unstructured":"Nandipati SC, XinYing C. Classification and feature selection approaches for cardiotocography by machine learning techniques. J Telecommun Electron Comput Eng (JTEC). 2020;12(1):7\u201314.","journal-title":"J Telecommunication Electron Comput Eng (JTEC)"},{"key":"837_CR33","doi-asserted-by":"publisher","first-page":"9768","DOI":"10.1007\/s10489-021-02968-1","volume":"52","author":"M Rashid","year":"2022","unstructured":"Rashid M, Kamruzzaman J, Imam T, et al. A tree-based stacking ensemble technique with feature selection for network intrusion detection. Appl Intell. 2022;52:9768\u201381. https:\/\/doi.org\/10.1007\/s10489-021-02968-1.","journal-title":"Appl Intell"},{"key":"837_CR34","doi-asserted-by":"publisher","unstructured":"Al-Adhaileh EMSMH, Alsaade FW, Theyazn HH, Aldhyani AA, Alqarni N, Alsharif M, Irfan Uddin AH, Alahmadi, Mukti E, Jadhav MY, Alzahrani. Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. J Healthcare Eng. 2021;2021:1004767. https:\/\/doi.org\/10.1155\/2021\/1004767","DOI":"10.1155\/2021\/1004767"},{"key":"837_CR35","doi-asserted-by":"crossref","unstructured":"Zhang J, Kim-Fung M. Time series prediction using RNN in multi-dimension embedding phase space. In: SMC\u201998 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218). IEEE. Vol. 2.1998.","DOI":"10.1109\/ICSMC.1998.728147"},{"issue":"8","key":"837_CR36","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-00837-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-00837-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-00837-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T12:52:36Z","timestamp":1770641556000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-00837-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,11]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["837"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-00837-9","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,11]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research techniques used in this study were reviewed and approved by the Institutional Ethics Committee of the Department of Computer Science and Engineering (CSE) at Birla Institute of Technology (BIT), Mesra, Ranchi, India, under Approval No: CSE\/HoD\/Certificate\/2023-24\/164. All methods with human subjects followed the ethical rules established by the institutional research committee, which were consistent with the principles of the 1964 Helsinki Declaration and its revisions, or equivalent ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All individual participants included in the study provided informed consent. Participants were presented with clear and comprehensive details about the study\u2019s purpose, objectives, potential risks, and benefits. They were afforded the opportunity to address any inquiries they had before their data was recorded.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable. This is not a clinical trial, it is an observational experimental study in which wearable sensor data are collected from participants who consent and conducted with institutional ethical approval.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial registration"}},{"value":"The authors declare no competing interests.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"113"}}