{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:33:49Z","timestamp":1763458429336,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u00c9cole de technologie sup\u00e9rieure","award":["RGPIN-2022-0327"],"award-info":[{"award-number":["RGPIN-2022-0327"]}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada Discovery Grant Program","doi-asserted-by":"publisher","award":["RGPIN-2022-0327"],"award-info":[{"award-number":["RGPIN-2022-0327"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Impaired balance and mental stress are significant health concerns, particularly among older adults. This study investigated the relationship between the heart rate variability and fall risk during daily activities among individuals over 40 years old. This aimed to explore the potential of the heart rate variability as an indicator of stress and balance loss. Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. This study demonstrates the feasibility of using the heart rate variability and machine learning to monitor physiological responses associated with stress and fall risks. By highlighting this potential biomarker of autonomic health, the findings contribute to developing real-time monitoring systems that could support fall prevention efforts in everyday settings for older adults.<\/jats:p>","DOI":"10.3390\/computers14020045","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T04:01:39Z","timestamp":1738209699000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"given":"Ines Belhaj","family":"Messaoud","sequence":"first","affiliation":[{"name":"Department of Software and Information Technology Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Montreal, QC H3C 1K3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0549-536X","authenticated-orcid":false,"given":"Ornwipa","family":"Thamsuwan","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, \u00c9cole de Technologie Sup\u00e9rieure, Montreal, QC H3C 1K3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"ref_1","unstructured":"GBD 2021 Diseases and Injuries Collaborators (2024). Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u20132021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet, 403, 2133\u20132161."},{"key":"ref_2","unstructured":"Organisation Mondiale de la Sant\u00e9 (2024, November 30). Chutes. Available online: https:\/\/www.who.int\/fr\/news-room\/fact-sheets\/detail\/falls."},{"key":"ref_3","unstructured":"Shumway-Cook, A., and Woollacott, M. (2006). Motor control: Translating research into clinical practice. Osteoporosis International, Lippincott Williams & Wilkins."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1056\/NEJM198812293192604","article-title":"Risk factors for falls among elderly persons living in the community","volume":"319","author":"Tinetti","year":"1988","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S0165-0327(00)00338-4","article-title":"A model of neurovisceral integration in emotion regulation and dysregulation","volume":"61","author":"Thayer","year":"2000","journal-title":"J. Affect. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"i1419","DOI":"10.1136\/bmj.i1419","article-title":"Prevention of falls in older people living in the community","volume":"353","author":"Vieira","year":"2016","journal-title":"BMJ"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ye, P., Liu, Y., Zhang, J., Peng, K., Pan, X., Xiao, S., Armstrong, E., Er, Y., Duan, L., and Ivers, R. (2020). Falls prevention interventions for community-dwelling older people living in mainland China: A narrative systematic review. BMC Health Serv. Res., 20.","DOI":"10.1186\/s12913-020-05645-0"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shaffer, F., McCraty, R., and Zerr, C.L. (2014). A healthy heart is not a metronome: An integrative review of the heart\u2019s anatomy and heart rate variability. Front. Psychol., 5.","DOI":"10.3389\/fpsyg.2014.01040"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1080\/10803548.2008.11076767","article-title":"Perceived mental stress and reactions in heart rate variability\u2014A pilot study among employees of an electronics company","volume":"14","author":"Orsila","year":"2008","journal-title":"Int. J. Occup. Saf. Ergon. (JOSE)"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, D., Seo, Y., and Salahuddin, L. (February, January 30). Decreased long term variations of heart rate variability in subjects with higher self-reporting stress scores. Proceedings of the 2008 Second International Conference on Pervasive Computing Technologies for Healthcare, Tampere, Finland.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2008.2526"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.bspc.2015.02.012","article-title":"Acute mental stress assessment via short-term HRV analysis in healthy adults: A systematic review with meta-analysis","volume":"18","author":"Castaldo","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, L., Hao, J., Zhou, T.H., and Song, F. (2023, January 19\u201321). ECG stress detection model based on heart rate variability feature extraction. Proceedings of the HP3C \u201923: 7th International Conference on High Performance Compilation, Computing and Communications, Nanjing, China.","DOI":"10.1145\/3606043.3606069"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Salahuddin, L., and Kim, D. (2006, January 9\u201311). Detection of acute stress by heart rate variability using a prototype mobile ECG sensor. Proceedings of the 2006 International Conference on Hybrid Information Technology, Cheju Island, Republic of Korea.","DOI":"10.1109\/ICHIT.2006.253646"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dalmeida, K.M., and Masala, G.L. (2021). HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices. Sensors, 21.","DOI":"10.3390\/s21082873"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8203","DOI":"10.3233\/JIFS-233791","article-title":"An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability","volume":"45","author":"Murty","year":"2023","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schaffarczyk, M., Rogers, B., Reer, R., and Gronwald, T. (2022). Validity of the Polar H10 Sensor for Heart Rate Variability Analysis during Resting State and Incremental Exercise in Recreational Men and Women. Sensors, 22.","DOI":"10.3390\/s22176536"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Vicente, A., Hernando, D., Mar\u00edn-Puyalto, J., Vicente-Rodr\u00edguez, G., Garatachea, N., Pueyo, E., and Bail\u00f3n, R. (2021). Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition, and Fitness Level Groups. Sensors, 21.","DOI":"10.3390\/s21030902"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2700607","DOI":"10.1109\/JTEHM.2021.3106803","article-title":"Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring","volume":"9","author":"Attar","year":"2021","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jarchi, D., Andreu-Perez, J., Kiani, M., Vysata, O., Kuchynka, J., Prochazka, A., and Sanei, S. (2020). Recognition of Patient Groups with Sleep-Related Disorders Using Bio-signal Processing and Deep Learning. Sensors, 20.","DOI":"10.3390\/s20092594"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pourmohammadi, S., and Maleki, A. (2021). Continuous mental stress level assessment using electrocardiogram and electromyogram signals. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102694"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11521","DOI":"10.1007\/s11042-017-5069-z","article-title":"PhysioLab\u2014A multivariate physiological computing toolbox for ECG, EMG, and EDA signals: A case study of cardiorespiratory fitness assessment in the elderly population","volume":"77","author":"Gouveia","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12995-021-00313-3","article-title":"Heart rate variability as a strain indicator for psychological stress for emergency physicians during work and alert intervention: A systematic review","volume":"16","author":"Thielmann","year":"2021","journal-title":"J. Occup. Med. Toxicol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rodrigues, S., Dias, D., Paiva, J.S., and Cunha, J.P.S. (2018, January 17\u201321). Psychophysiological Stress Assessment Among On-Duty Firefighters. Proceedings of the 2018 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513250"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Prajod, P., and Andr\u00e9, E. (2022, January 12\u201315). On the generalizability of ECG-based stress detection models. Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas.","DOI":"10.1109\/ICMLA55696.2022.00090"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bustos, D., Cardoso, F., Rios, M., Vaz, M., Guedes, J., Costa, J.T., Baptista, J.S., and Fernandes, R.J. (2022). Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters. Sensors, 23.","DOI":"10.3390\/s23010194"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tervonen, J., Puttonen, S., Sillanp\u00e4\u00e4, M.J., Hopsu, L., Homorodi, Z., Ker\u00e4nen, J., Pajukanta, J., Tolonen, A., L\u00e4ms\u00e4, A., and M\u00e4ntyj\u00e4rvi, J. (2020). Personalized mental stress detection with self-organizing map: From laboratory to the field. Comput. Biol. Med., 124.","DOI":"10.1016\/j.compbiomed.2020.103935"},{"key":"ref_27","first-page":"S7","article-title":"Measuring balance in the elderly: Validation of an instrument","volume":"83","author":"Berg","year":"1992","journal-title":"Can. J. Public Health"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1093\/ptj\/82.2.128","article-title":"Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds","volume":"82","author":"Steffen","year":"2002","journal-title":"Phys. Ther."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/JBHI.2019.2962627","article-title":"Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors","volume":"24","author":"Aygun","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","first-page":"54081","article-title":"A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis","volume":"10","author":"Saleem","year":"2022","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"H1873","DOI":"10.1152\/ajpheart.00405.2000","article-title":"Poincar\u00e9 plot interpretation using a physiological model of HRV based on a network of oscillators","volume":"283","author":"Brennan","year":"2002","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1042\/cs0870649","article-title":"Low-frequency spontaneous fluctuations of R-R interval and blood pressure in conscious humans: A baroreceptor or central phenomenon?","volume":"87","author":"Bernardi","year":"1994","journal-title":"Clin. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1378\/chest.125.2.683","article-title":"Respiratory sinus arrhythmia: Why does the heartbeat synchronize with respiratory rhythm?","volume":"125","author":"Yasuma","year":"2004","journal-title":"Chest"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability: Standards of measurement, physiological interpretation, and clinical use","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Satti, R., Abid, N.U., Bottaro, M., De Rui, M., Garrido, M., Raoufy, M.R., Montagnese, S., and Manj, A.R. (2019). The Application of the Extended Poincar\u00e9 Plot in the Analysis of Physiological Variabilities. Front. Physiol., 10.","DOI":"10.3389\/fphys.2019.00116"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1097\/01.mlr.0000185750.18119.fd","article-title":"Functional impact and health utility of anxiety disorders in primary care outpatients","volume":"43","author":"Stein","year":"2005","journal-title":"Med. Care"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nakao, M. (2019). Heart Rate Variability and Perceived Stress as Measurements of Relaxation Response. J. Clin. Med., 8.","DOI":"10.3390\/jcm8101704"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Taelman, J., Vandeput, S., Spaepen, A., and Huffel, S.V. (2009, January 7\u201312). Influence of Mental Stress on Heart Rate and Heart Rate Variability. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Munich, Germany.","DOI":"10.1007\/978-3-540-89208-3_324"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1111\/j.1469-8986.1994.tb02352.x","article-title":"Autonomic cardiac control. III. Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades","volume":"31","author":"Berntson","year":"1994","journal-title":"Psychophysiology"},{"key":"ref_41","unstructured":"Y\u0131ld\u0131z, A.Y., and Kalayci, A. (2024). Gradient boosting decision trees on medical diagnosis over tabular data. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s41512-020-00075-2","article-title":"Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: Application to the discrimination between type 1 and type 2 diabetes in young adults","volume":"4","author":"Lynam","year":"2020","journal-title":"Diagn. Progn. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gungor, M.A., and Karagoz, I. (2016, January 14\u201317). The effects of the median filter with different window sizes for ultrasound image. Proceedings of the 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/CompComm.2016.7924761"},{"key":"ref_45","unstructured":"Task Force of the European Society of Cardiology, and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043\u20131065."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shaffer, F., and Ginsberg, J.P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Front. Public Health, 5.","DOI":"10.3389\/fpubh.2017.00258"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ga\u00f1\u00e1n-Calvo, A., and Fajardo-L\u00f3pez, J. (2016). Universal structures of normal and pathological heart rate variability. Sci. Rep., 6.","DOI":"10.1038\/srep21749"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ga\u00f1\u00e1n-Calvo, A.M., Hnatkova, K., Romero-Calvo, \u00c1., Fajardo-L\u00f3pez, J., and Malik, M. (2018). Risk stratifiers for arrhythmic and non-arrhythmic mortality after acute myocardial infarction. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-28327-8"},{"key":"ref_49","unstructured":"Gherbi, Y., and Thamsuwan, O. (2024, January 25\u201329). Berg balance test for predicting a fall risk in older adults living at home: A preliminary study on the effect of pre-existing health conditions on postural balance. Proceedings of the 22nd Triennial Congress of the International Ergonomics Association (IEA), Jeju, Republic of Korea."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:38:42Z","timestamp":1759919922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/2\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,30]]},"references-count":49,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["computers14020045"],"URL":"https:\/\/doi.org\/10.3390\/computers14020045","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2025,1,30]]}}}