{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:59:15Z","timestamp":1769713155074,"version":"3.49.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation\u2019s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% \u2013 8.5% and CART by 2% \u2013 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% \u2013 20.5% and combined S1\/S2 by 11% \u2013 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness.<\/jats:p>","DOI":"10.3233\/jifs-233791","type":"journal-article","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T11:25:04Z","timestamp":1693567504000},"page":"8203-8216","source":"Crossref","is-referenced-by-count":0,"title":["An intelligent wearable embedded architecture for stress detection and psychological behavior monitoring using heart rate variability"],"prefix":"10.1177","volume":"45","author":[{"given":"Patnala S.R.","family":"Chandra Murty","sequence":"first","affiliation":[{"name":"Department of CSE, Malla Reddy Engineering College (Autonomous), Maisammaguda, Secunderabad, India"}]},{"given":"Chinta","family":"Anuradha","sequence":"additional","affiliation":[{"name":"Department of CSE, V. R. Siddhartha Engineering College, Kanuru, Vijayawada, India"}]},{"given":"P.","family":"Appala Naidu","sequence":"additional","affiliation":[{"name":"Department of CSE, Raghu Engineering College (Autonomous) Visakhapatnam, India"}]},{"given":"C.","family":"Balaswamy","sequence":"additional","affiliation":[{"name":"Department of ECE, Sheshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India"}]},{"given":"Rajeswaran","family":"Nagalingam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Malla Reddy College of Engineering, Maisammaguda, Secunderabad, India"}]},{"given":"Senthil Kumar","family":"Jagatheesaperumal","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India"}]},{"given":"Muruganantham","family":"Ponnusamy","sequence":"additional","affiliation":[{"name":"Deputy Registrar, Indian Institute of Information Technology Kalyani, West Bengal, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-233791_ref1","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1136\/bmj.38693.435301.80","article-title":"Chronic stress at work and the metabolic syndrome: prospective study","volume":"332","author":"Chandola","year":"2006","journal-title":"BMJ"},{"key":"10.3233\/JIFS-233791_ref2","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TFUZZ.2012.2183602","article-title":"Stress monitoring based on stochastic fuzzy analysis of heartbeat intervals","volume":"20","author":"Kumar","year":"2012","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.3233\/JIFS-233791_ref3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.neubiorev.2008.07.006","article-title":"Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior","volume":"33","author":"Montano","year":"2009","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"10.3233\/JIFS-233791_ref4","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.bspc.2017.07.004","article-title":"Heart rate estimation using facial video: A review","volume":"38","author":"Hassan","year":"2017","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.3233\/JIFS-233791_ref5","doi-asserted-by":"crossref","first-page":"8630","DOI":"10.3390\/app10238630","article-title":"Real-time webcam heart-rate and variability estimation with clean ground truth for evaluation","volume":"10","author":"Gudi","year":"2020","journal-title":"Applied Sciences"},{"key":"10.3233\/JIFS-233791_ref6","first-page":"1","article-title":"ECG-based emotion recognition: Overview of methods and applications","volume":"2018","author":"Nikolova","year":"2018","journal-title":"ANNA\u201918; Advances in Neural Networks and Applications"},{"key":"10.3233\/JIFS-233791_ref7","doi-asserted-by":"crossref","first-page":"5015","DOI":"10.3390\/s21155015","article-title":"Electrocardiogram-based emotion recognition systems and their applications in healthcare\u2014a review","volume":"21","author":"Hasnul","year":"2021","journal-title":"Sensors"},{"key":"10.3233\/JIFS-233791_ref8","first-page":"012091","article-title":"Research on emotion recognition based on ECG signal","volume":"1678","author":"Zhang","year":"2020","journal-title":"In Proceedings of the Journal of Physics: Conference Series. 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