{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:25:45Z","timestamp":1779877545716,"version":"3.53.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007741","name":"University of Hyderabad","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007741","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s10489-022-04250-4","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T05:07:04Z","timestamp":1666760824000},"page":"14400-14425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["A novel deep learning architecture and MINIROCKET feature extraction method for human activity recognition using ECG, PPG and inertial sensor dataset"],"prefix":"10.1007","volume":"53","author":[{"given":"Rohit Kumar","family":"Bondugula","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8314-2618","authenticated-orcid":false,"given":"Siba K","family":"Udgata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaushik Bhargav","family":"Sivangi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"issue":"9","key":"4250_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-016-0549-7","volume":"40","author":"F Cicirelli","year":"2016","unstructured":"Cicirelli F, Fortino G, Giordano A, Guerrieri A, Spezzano G, Vinci A (2016) On the design of smart homes: a framework for activity recognition in home environment. J Med Syst 40(9):1\u201317","journal-title":"J Med Syst"},{"issue":"5","key":"4250_CR2","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/TSMCA.2009.2025137","volume":"39","author":"P Rashidi","year":"2009","unstructured":"Rashidi P, Cook DJ (2009) Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans Syst Man Cybern-Part Syst Humans 39(5):949\u2013959","journal-title":"IEEE Trans Syst Man Cybern-Part Syst Humans"},{"issue":"1","key":"4250_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1743-0003-9-21","volume":"9","author":"S Patel","year":"2012","unstructured":"Patel S, Park H, Bonato P, Chan L, Rodgers M (2012) A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehab 9(1):1\u201317","journal-title":"J Neuroeng Rehab"},{"key":"4250_CR4","doi-asserted-by":"crossref","unstructured":"Mazilu S, Blanke U, Hardegger M, Tr\u00f6ster G, Gazit E, Hausdorff JM (2014) Gaitassist: a daily-life support and training system for parkinson\u2019s disease patients with freezing of gait. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 2531\u20132540","DOI":"10.1145\/2556288.2557278"},{"issue":"2","key":"4250_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.pmcj.2012.06.002","volume":"9","author":"M Kranz","year":"2013","unstructured":"Kranz M, M\u00f6ller A, Hammerla N, Diewald S, Pl\u00f6tz T, Olivier P, Roalter L (2013) The mobile fitness coach: towards individualized skill assessment using personalized mobile devices. Pervasive Mobile Comput 9(2):203\u2013215","journal-title":"Pervasive Mobile Comput"},{"issue":"2","key":"4250_CR6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MPRV.2008.40","volume":"7","author":"T Stiefmeier","year":"2008","unstructured":"Stiefmeier T, Roggen D, Ogris G, Lukowicz P, Tr\u00f6ster G (2008) Wearable activity tracking in car manufacturing. IEEE Pervasive Comput 7(2):42\u201350","journal-title":"IEEE Pervasive Comput"},{"issue":"12","key":"4250_CR7","doi-asserted-by":"publisher","first-page":"31314","DOI":"10.3390\/s151229858","volume":"15","author":"F Attal","year":"2015","unstructured":"Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y (2015) Physical human activity recognition using wearable sensors. Sensors 15(12):31314\u201331338","journal-title":"Sensors"},{"issue":"8","key":"4250_CR8","doi-asserted-by":"publisher","first-page":"10701","DOI":"10.1007\/s11042-015-3188-y","volume":"76","author":"Y Lu","year":"2017","unstructured":"Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl 76(8):10701\u201310719","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"4250_CR9","first-page":"13","volume":"17","author":"D Keihani","year":"2014","unstructured":"Keihani D, Kargarfard M, Mokhtari M (2014) Cardiac effects of exercise rehabilitation on quality of life, depression and anxiety in patients with heart failure patients. J Fund Mental Health 17(1):13\u201319","journal-title":"J Fund Mental Health"},{"issue":"1","key":"4250_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/data2010001","volume":"2","author":"D Jarchi","year":"2017","unstructured":"Jarchi D, Casson AJ (2017) Description of a database containing wrist ppg signals recorded during physical exercise with both accelerometer and gyroscope measures of motion. Data 2(1):1","journal-title":"Data"},{"key":"4250_CR11","doi-asserted-by":"publisher","first-page":"100082","DOI":"10.1016\/j.smhl.2019.100082","volume":"14","author":"M Boukhechba","year":"2019","unstructured":"Boukhechba M, Cai L, Wu C, Barnes LE (2019) Actippg: using deep neural networks for activity recognition from wrist-worn photoplethysmography (ppg) sensors. Smart Health 14:100082","journal-title":"Smart Health"},{"key":"4250_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.bspc.2015.05.006","volume":"21","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Liu B, Zhang Z (2015) Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography. Biomed Signal Process Contr 21:119\u2013125","journal-title":"Biomed Signal Process Contr"},{"issue":"6","key":"4250_CR13","doi-asserted-by":"publisher","first-page":"4029","DOI":"10.1007\/s10489-020-02005-7","volume":"51","author":"L Chen","year":"2021","unstructured":"Chen L, Liu X, Peng L, Wu M (2021) Deep learning based multimodal complex human activity recognition using wearable devices. Appl Intell 51(6):4029\u20134042","journal-title":"Appl Intell"},{"issue":"1","key":"4250_CR14","first-page":"43","volume":"43","author":"ZB Moghadam","year":"2021","unstructured":"Moghadam ZB, NOGHONDAR MS, Goshvarpour A (2021) Novel delayed poincare\u2019s plot indices of photoplethysmogram for classification of physical activities. Appl Med Inform 43(1):43\u201355","journal-title":"Appl Med Inform"},{"issue":"2","key":"4250_CR15","doi-asserted-by":"publisher","first-page":"613","DOI":"10.3390\/s18020613","volume":"18","author":"S Mehrang","year":"2018","unstructured":"Mehrang S, Pietil\u00e4 J, Korhonen I (2018) An activity recognition framework deploying the random forest classifier and a single optical heart rate monitoring and triaxial accelerometer wrist-band. Sensors 18 (2):613","journal-title":"Sensors"},{"key":"4250_CR16","doi-asserted-by":"crossref","unstructured":"Tang Y, Zhang L, Teng Q, Min F, Song A (2022) Triple cross-domain attention on human activity recognition using wearable sensors. IEEE Trans Emerging Topics Computat Intell","DOI":"10.1109\/TETCI.2021.3136642"},{"key":"4250_CR17","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.bspc.2017.09.018","volume":"40","author":"C Ciucurel","year":"2018","unstructured":"Ciucurel C, Georgescu L, Iconaru EI (2018) Ecg response to submaximal exercise from the perspective of golden ratio harmonic rhythm. Biomed Signal Process Contr 40:156\u2013162","journal-title":"Biomed Signal Process Contr"},{"key":"4250_CR18","doi-asserted-by":"crossref","unstructured":"Tang Y, Zhang L , Min F, He J (2022) Multi-scale deep feature learning for human activity recognition using wearable sensors. IEEE Trans Industr Electr","DOI":"10.1109\/TIE.2022.3161812"},{"key":"4250_CR19","doi-asserted-by":"crossref","unstructured":"Gholamiangonabadi D, Grolinger K (2022) Personalized models for human activity recognition with wearable sensors: deep neural networks and signal processing. Appl Intell:1\u201321","DOI":"10.1007\/s10489-022-03832-6"},{"issue":"14","key":"4250_CR20","doi-asserted-by":"publisher","first-page":"3079","DOI":"10.3390\/s19143079","volume":"19","author":"A Reiss","year":"2019","unstructured":"Reiss A, Indlekofer I, Schmidt P, Van Laerhoven K (2019) Deep ppg: large-scale heart rate estimation with convolutional neural networks. Sensors 19(14):3079","journal-title":"Sensors"},{"issue":"14","key":"4250_CR21","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.3390\/electronics10141715","volume":"10","author":"M Alessandrini","year":"2021","unstructured":"Alessandrini M, Biagetti G, Crippa P, Falaschetti L, Turchetti C (2021) Recurrent neural network for human activity recognition in embedded systems using ppg and accelerometer data. Electronics 10 (14):1715","journal-title":"Electronics"},{"key":"4250_CR22","doi-asserted-by":"crossref","unstructured":"Brophy E, Muehlhausen W, Smeaton AF, Ward TE (2020) Cnns for heart rate estimation and human activity recognition in wrist worn sensing applications. In: 2020 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp 1\u20136","DOI":"10.1109\/PerComWorkshops48775.2020.9156120"},{"issue":"4","key":"4250_CR23","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.3390\/s21041070","volume":"21","author":"M Elshafei","year":"2021","unstructured":"Elshafei M, Costa DE, Shihab E (2021) On the impact of biceps muscle fatigue in human activity recognition. Sensors 21(4):1070","journal-title":"Sensors"},{"key":"4250_CR24","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.inffus.2019.06.013","volume":"53","author":"C Ma","year":"2020","unstructured":"Ma C, Li W, Cao J, Du J, Li Q, Gravina R (2020) Adaptive sliding window based activity recognition for assisted livings. Inform Fusion 53:55\u201365","journal-title":"Inform Fusion"},{"key":"4250_CR25","doi-asserted-by":"crossref","unstructured":"Bondugula RK, Udgata SK, Bommi NS (2021) A novel weighted consensus machine learning model for covid-19 infection classification using ct scan images. Arab J Sci Eng:1\u201312","DOI":"10.1007\/s13369-021-05879-y"},{"key":"4250_CR26","doi-asserted-by":"publisher","first-page":"133982","DOI":"10.1109\/ACCESS.2020.3010715","volume":"8","author":"D Gholamiangonabadi","year":"2020","unstructured":"Gholamiangonabadi D, Kiselov N, Grolinger K (2020) Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection. IEEE Access 8:133982\u2013133994","journal-title":"IEEE Access"},{"issue":"6","key":"4250_CR27","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1007\/s40846-020-00573-0","volume":"40","author":"T Aydemir","year":"2020","unstructured":"Aydemir T, \u015eahin M, Aydemir O (2020) A new method for activity monitoring using photoplethysmography signals recorded by wireless sensor. J Med Bio Eng 40(6):934\u2013942","journal-title":"J Med Bio Eng"},{"key":"4250_CR28","doi-asserted-by":"publisher","first-page":"105044","DOI":"10.1016\/j.dib.2019.105044","volume":"29","author":"G Biagetti","year":"2020","unstructured":"Biagetti G, Crippa P, Falaschetti L, Saraceni L, Tiranti A, Turchetti C (2020) Dataset from ppg wireless sensor for activity monitoring. Data Brief 29:105044","journal-title":"Data Brief"},{"key":"4250_CR29","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.trf.2019.09.015","volume":"66","author":"P van Gent","year":"2019","unstructured":"van Gent P, Farah H, van Nes N, van Arem B (2019) Heartpy: a novel heart rate algorithm for the analysis of noisy signals. Transportat Res Part Traffic Psychol Behav 66:368\u2013378","journal-title":"Transportat Res Part Traffic Psychol Behav"},{"key":"4250_CR30","doi-asserted-by":"crossref","unstructured":"Peng C, Zhang X, Yu G, Luo G, Sun J (2017) Large kernel matters\u2013improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4353\u20134361","DOI":"10.1109\/CVPR.2017.189"},{"issue":"5","key":"4250_CR31","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","volume":"34","author":"A Dempster","year":"2020","unstructured":"Dempster A, Petitjean F, Webb GI (2020) Rocket: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min Knowl Disc 34(5):1454\u20131495","journal-title":"Data Min Knowl Disc"},{"key":"4250_CR32","doi-asserted-by":"crossref","unstructured":"Dempster A, Schmidt DF, Webb GI (2021) Minirocket: a very fast (almost) deterministic transform for time series classification. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 248\u2013257","DOI":"10.1145\/3447548.3467231"},{"issue":"7553","key":"4250_CR33","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"4250_CR34","doi-asserted-by":"crossref","unstructured":"Bondugula RK, Sivangi KB, Udgata SK (2022) Identification of schizophrenic individuals using activity records through visualization of recurrent networks. In: Udgata SK, Sethi S, Gao X-Z (eds) Intelligent Systems. Springer Nature Singapore, pp 653\u2013664","DOI":"10.1007\/978-981-19-0901-6_57"},{"key":"4250_CR35","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2018.03.056","volume":"105","author":"HF Nweke","year":"2018","unstructured":"Nweke HF, Teh YW, Al-Garadi MA, Alo UR (2018) Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Syst Appl 105:233\u2013261","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4250_CR36","doi-asserted-by":"publisher","first-page":"162","DOI":"10.21629\/JSEE.2017.01.18","volume":"28","author":"B Zhao","year":"2017","unstructured":"Zhao B, Lu H, Chen S, Liu J, Wu D (2017) Convolutional neural networks for time series classification. J Syst Eng Electron 28(1):162\u2013169","journal-title":"J Syst Eng Electron"},{"key":"4250_CR37","doi-asserted-by":"crossref","unstructured":"Brophy E, Veiga JJD, Wang Z, Ward TE (2018) A machine vision approach to human activity recognition using photoplethysmograph sensor data. In: 2018 29th Irish signals and systems conference (ISSC). IEEE, pp 1\u20136","DOI":"10.1109\/ISSC.2018.8585372"},{"key":"4250_CR38","doi-asserted-by":"crossref","unstructured":"Mahmud T, Akash SS, Fattah SA, Zhu W-P, Ahmad MO (2020) Human activity recognition from multi-modal wearable sensor data using deep multi-stage lstm architecture based on temporal feature aggregation. In: 2020 IEEE 63rd international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 249\u2013252","DOI":"10.1109\/MWSCAS48704.2020.9184666"},{"key":"4250_CR39","doi-asserted-by":"crossref","unstructured":"Almanifi ORA, Khairuddin IM, Razman MAM, Musa RM, Majeed APA (2022) Human activity recognition based on wrist ppg via the ensemble method. ICT Express","DOI":"10.1016\/j.icte.2022.03.006"},{"key":"4250_CR40","doi-asserted-by":"crossref","unstructured":"Biagetti G, crippa P, Falaschetti L, Orcioni S, Turchetti C (2017) Human activity recognition using accelerometer and photoplethysmographic signals. In: International conference on intelligent decision technologies. Springer, pp 53\u201362","DOI":"10.1007\/978-3-319-59424-8_6"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04250-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-04250-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-04250-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T21:03:59Z","timestamp":1685567039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-04250-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":40,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["4250"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-04250-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"6 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We have used the secondary data available in the public domain and have not conducted any experiments involving human beings in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare that there is no conflict of interest in this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}