{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T09:02:07Z","timestamp":1761642127425,"version":"build-2065373602"},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In recent days, stress is a major phenomenon that adversely affects both individuals and communities. The research in computing the stress factor has wider advantages as it improves personal learning, learning operations, and high productivity that benefits society. Several computational techniques come into concern to avoid and reduce the stress level using the electrocardiogram (ECG) signals. In this study, the stress level was classified using the feature extraction approach in combination with the classifier. The signal is processed using the variational mode decomposition denoising technique to reconstruct the original signal. The decomposed signal was further extracted using the time\u2013frequency domain technique as characteristics of the ECG signal such as R-wave and T-wave constructed. Further, the support vector machine classifier was used to classify the stress level (low, medium, and high) of the extracted signal. Based on stress classification outcomes, the robot offers a range of personalized interventions to users. These interventions include relaxation exercises, deep breathing techniques, or guided mindfulness sessions. The average accuracy obtained using the proposed technique is 98.98% but without using the feature extraction technique, it is 97.71%. The other performance parameters also get improved and the results are finally compared with the existing techniques.<\/jats:p>","DOI":"10.1515\/pjbr-2024-0003","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:00:46Z","timestamp":1753855246000},"source":"Crossref","is-referenced-by-count":0,"title":["Design of a robot system for improved stress classification using time\u2013frequency domain feature extraction based on electrocardiogram"],"prefix":"10.1515","volume":"15","author":[{"given":"Vikas","family":"Malhotra","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University , Rajpura , Punjab , India"}]},{"given":"Gurpreet Singh","family":"Saini","sequence":"additional","affiliation":[{"name":"School of Electronics and Electrical Engineering, Lovely Professional University , Phagwara , Punjab , India"}]},{"given":"Sumit","family":"Malhotra","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chandigarh University , Mohali , Punjab , India"}]},{"given":"Renu","family":"Popli","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University , Rajpura , Punjab , India"}]}],"member":"374","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"2025102808525704130_j_pjbr-2024-0003_ref_001","doi-asserted-by":"crossref","unstructured":"A. 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Biomed., vol. 202, p. 106005, 2021.","DOI":"10.1016\/j.cmpb.2021.106005"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_004","doi-asserted-by":"crossref","unstructured":"H. Li, H. Liang, C. Miao, L. Cao, X. Feng, C. Tang, et al., \u201cNovel ECG signal classification based on KICA nonlinear feature extraction,\u201d Circuits Syst. Signal. Process, vol. 35, no. 4, pp. 1187\u20131197, 2016.","DOI":"10.1007\/s00034-015-0108-3"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_005","doi-asserted-by":"crossref","unstructured":"R. R. Sharma, M. Kumar, and R. B. Pachori, \u201cAutomated CAD identification system using time-frequency representation based on eigenvalue decomposition of ECG signals,\u201d Machine intelligence and signal analysis, Springer, Singapore, 2018, pp. 597\u2013608.","DOI":"10.1007\/978-981-13-0923-6_51"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_006","doi-asserted-by":"crossref","unstructured":"S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, \u201cReview of noise removal techniques in ECG signals,\u201d IET Signal. Process., vol. 14, no. 9, pp. 569\u2013590, 2020.","DOI":"10.1049\/iet-spr.2020.0104"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_007","doi-asserted-by":"crossref","unstructured":"H. M. Cho, H. Park, S. Y. Dong, and I. Youn, \u201cAmbulatory and laboratory stress detection based on raw electrocardiogram signals using a convolutional neural network,\u201d Sensors, vol. 19, no. 20, p. 4408, 2019.","DOI":"10.3390\/s19204408"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_008","doi-asserted-by":"crossref","unstructured":"R. Ahuja and A. Banga, \u201cMental stress detection in university students using machine learning algorithms,\u201d Procedia Comput. Sci., vol. 15, pp. 349\u2013353, 2019.","DOI":"10.1016\/j.procs.2019.05.007"},{"key":"2025102808525704130_j_pjbr-2024-0003_ref_009","doi-asserted-by":"crossref","unstructured":"S. Pourmohammadi and A. 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