{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:19:26Z","timestamp":1743023966339,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030963071"},{"type":"electronic","value":"9783030963088"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96308-8_113","type":"book-chapter","created":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T13:15:41Z","timestamp":1648300541000},"page":"1216-1226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using Machine Learning Approaches to\u00a0Identify Exercise Activities from\u00a0a\u00a0Triple-Synchronous Biomedical Sensor"],"prefix":"10.1007","author":[{"given":"Yohan","family":"Mahajan","sequence":"first","affiliation":[]},{"given":"Jahnavi","family":"Pinnamraju","sequence":"additional","affiliation":[]},{"given":"John L.","family":"Burns","sequence":"additional","affiliation":[]},{"given":"Judy W.","family":"Gichoya","sequence":"additional","affiliation":[]},{"given":"Saptarshi","family":"Purkayastha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"issue":"6","key":"113_CR1","doi-asserted-by":"publisher","first-page":"e0178664","DOI":"10.1371\/journal.pone.0178664","volume":"12","author":"J Carriot","year":"2017","unstructured":"Carriot, J., Jamali, M., Cullen, K.E., Chacron, M.J.: Envelope statistics of self-motion signals experienced by human subjects during everyday activities: implications for vestibular processing. PLoS One. 12(6), e0178664 (2017)","journal-title":"PLoS One."},{"key":"113_CR2","doi-asserted-by":"publisher","first-page":"32066","DOI":"10.1109\/ACCESS.2020.2973425","volume":"8","author":"A Ferrari","year":"2020","unstructured":"Ferrari, A., Micucci, D., Mobilio, M., Napoletano, P.: On the personalization of classification models for human activity recognition. IEEE Access 8, 32066\u201332079 (2020)","journal-title":"IEEE Access"},{"key":"113_CR3","doi-asserted-by":"publisher","first-page":"107287","DOI":"10.1016\/j.dib.2021.107287","volume":"38","author":"Y Mahajan","year":"2021","unstructured":"Mahajan, Y., Bhimireddy, A., Abid, A., Gichoya, J.W., Purkayastha, S.: PLHI-MC10: a dataset of exercise activities captured through a triple synchronous medically-approved sensor. Data Brief 38, 107287 (2021)","journal-title":"Data Brief"},{"issue":"5","key":"113_CR4","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.3390\/s17051100","volume":"17","author":"A Manzi","year":"2017","unstructured":"Manzi, A., Dario, P., Cavallo, F.: A human activity recognition system based on dynamic clustering of skeleton data. Sensors 17(5), 1100 (2017)","journal-title":"Sensors"},{"key":"113_CR5","doi-asserted-by":"crossref","unstructured":"Dobbins, C., Rawassizadeh, R.: Towards clustering of mobile and smartwatch accelerometer data for physical activity recognition. Informatics 5(29) (2018)","DOI":"10.3390\/informatics5020029"},{"key":"113_CR6","doi-asserted-by":"crossref","unstructured":"Bulbul, E., Cetin, A., Dogru, I.A.: Human activity recognition using smartphones. In: 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/ISMSIT.2018.8567275"},{"key":"113_CR7","doi-asserted-by":"crossref","unstructured":"Oluwalade, B., Neela, S., Wawira, J., Adejumo, T., Purkayastha, S.: Human activity recognition using deep learning models on smartphones and smartwatches sensor data. In: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: HEALTHINF, INSTICC, pp. 645\u2013650. SciTePress (2021)","DOI":"10.5220\/0010325906450650"},{"key":"113_CR8","doi-asserted-by":"crossref","unstructured":"He, Z.Y., Jin, L.W.: Activity recognition from acceleration data using AR model representation and SVM. In: International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2245\u20132250. IEEE (2008)","DOI":"10.1109\/ICMLC.2008.4620779"},{"issue":"12","key":"113_CR9","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.: Physical human activity recognition using wearable sensors. Sensors 15(12), 31314\u201331338 (2015)","journal-title":"Sensors"},{"issue":"2","key":"113_CR10","first-page":"227","volume":"17","author":"M Elshourbagy","year":"2016","unstructured":"Elshourbagy, M., Hemayed, E., Fayek, M.: Enhanced bag of words using multilevel k-means for human activity recognition. Egypt. Inf. J. 17(2), 227\u2013237 (2016)","journal-title":"Egypt. Inf. J."},{"issue":"9","key":"113_CR11","doi-asserted-by":"publisher","first-page":"2702","DOI":"10.3390\/s20092702","volume":"20","author":"P Ariza Colpas","year":"2020","unstructured":"Ariza Colpas, P., Vicario, E., De-La-Hoz-Franco, E., Pineres-Melo, M., Oviedo-Carrascal, A., Patara, F.: Unsupervised human activity recognition using the clustering approach: a review. Sensors 20(9), 2702 (2020)","journal-title":"Sensors"},{"issue":"9","key":"113_CR12","doi-asserted-by":"publisher","first-page":"2972","DOI":"10.1093\/ndt\/gfn187","volume":"23","author":"N Tangri","year":"2008","unstructured":"Tangri, N., Ansell, D., Naimark, D.: Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression. Nephrol. Dial. Transplant. 23(9), 2972\u20132981 (2008)","journal-title":"Nephrol. Dial. Transplant."},{"key":"113_CR13","series-title":"Springer Texts in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An Introduction to Statistical Learning","author":"G James","year":"2013","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. STS, vol. 103. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4614-7138-7"},{"key":"113_CR14","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3389\/fspor.2020.630576","volume":"01","author":"D Seshadri","year":"2021","unstructured":"Seshadri, D., Thom, M., Harlow, E., Gabbett, T., Geletka, B., Hsu, J., et al.: Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Front. Sports Active Living 01, 2 (2021). https:\/\/doi.org\/10.3389\/fspor.2020.630576","journal-title":"Front. Sports Active Living"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96308-8_113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T01:48:30Z","timestamp":1726883310000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96308-8_113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030963071","9783030963088"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96308-8_113","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The data is collected from seven anonymous healthy adult subjects after valid consent is received from all of them. IU IRB-2010321996.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}