{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/heart-mind-harmony-predicting-stress-from","title":"Heart-Mind Harmony: Predicting Stress from Heart Rate","arxiv_id":null,"date":"2024-07-18","proceeding":"GRENZE International Journal of Engineering and Technology 2024 7","authors":["Aravind M S","Sri Bhavan Prakath","Tarunika R","Dr M.Srividya","Dr M.Marimuthu and  Dr S.GayathriDevi"],"abstract":"stress is a pervasive difficulty with some distance-attaining fitness implications. Early \r\ndetection of pressure is important for well timed intervention and mitigation. in this research, we \r\ndiscover the feasibility of predicting stress degrees utilizing a rich dataset of coronary heart rate \r\nfunctions. This dataset includes a big range of coronary heart charge parameters, along with \r\nmean RR intervals, widespread deviations, spectral additives, and nonlinear dynamics, amongst \r\nothers. each facts entry is associated with a completely unique affected person identifier, taking \r\ninto account personalized analysis. The primary objective of this observe is to broaden a \r\npredictive model able to accurately discerning whether or not an man or woman is experiencing \r\nstress based on their coronary heart charge traits. machine learning strategies will be hired to \r\nextract significant patterns and relationships from the dataset. Key capabilities of interest \r\nencompass metrics like RMSSD (Root imply rectangular of Successive RR c programming \r\nlanguage variations), LF/HF (Ratio of Low Frequency to excessive Frequency), and sample \r\nentropy (Sampen), which capture both linear and nonlinear components of coronary heart \r\ncharge variability. moreover, demographic and contextual records, consisting of affected person \r\ncircumstance and coronary heart charge, could be integrated into the predictive models to \r\nenhance their accuracy and practicality. This examine pursuits to make a contribution to the \r\nimprovement of non-invasive and sensible stress detection methods that may be included into \r\nwearable gadgets or faraway tracking systems. these gear have the potential to assist individuals \r\nin managing pressure and healthcare professionals in delivering timely interventions. The effects \r\nmay additionally pave the way for customized stress management strategies based totally on an \r\nperson's heart rate profile. in the end, the findings of this research may additionally have broader \r\nimplications for improving the satisfactory of existence and basic properly-being of individuals \r\nvia permitting early pressure detection and intervention thru the analysis of heart price statistics.","url_abs":"https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3196&id=8","url_pdf":"https://thegrenze.com/index.php?display=page&view=journalabstract&absid=3196&id=8","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"heart-mind-harmony-predicting-stress-from","repo_url":"https://github.com/avd1729/Heart-Mind-Harmony","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}