{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T08:22:38Z","timestamp":1758874958475,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030772109"},{"type":"electronic","value":"9783030772116"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-77211-6_23","type":"book-chapter","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T23:06:27Z","timestamp":1623107187000},"page":"209-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Model Evaluation Approaches for Human Activity Recognition from Time-Series Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7346-6344","authenticated-orcid":false,"given":"Lee B.","family":"Hinkle","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7371-8887","authenticated-orcid":false,"given":"Vangelis","family":"Metsis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"23_CR1","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: Esann, vol. 3, p. 3 (2013)"},{"key":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-319-13105-4_14","volume-title":"Ambient Assisted Living and Daily Activities","author":"O Banos","year":"2014","unstructured":"Banos, O., et al.: mHealthDroid: a novel framework for agile development of mobile health applications. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 91\u201398. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13105-4_14"},{"key":"23_CR3","unstructured":"BenAbdelkader, C., Cutler, R., Davis, L.: Stride and cadence as a biometric in automatic person identification and verification. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 372\u2013377. IEEE (2002)"},{"key":"23_CR4","unstructured":"Brownlee, J.: 1D convolutional neural network models for human activity recognition, July 2020. https:\/\/machinelearningmastery.com\/cnn-models-for-human-activity-recognition-time-series-classification\/"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xue, Y.: A deep learning approach to human activity recognition based on single accelerometer. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1488\u20131492. IEEE (2015)","DOI":"10.1109\/SMC.2015.263"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381\u2013388. IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"23_CR7","series-title":"Springer Series in Statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7. http:\/\/www-stat.stanford.edu\/~tibs\/ElemStatLearn\/"},{"issue":"10","key":"23_CR8","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.3390\/app7101101","volume":"7","author":"D Micucci","year":"2017","unstructured":"Micucci, D., Mobilio, M., Napoletano, P.: UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones. Appl. Sci. 7(10), 1101 (2017)","journal-title":"Appl. Sci."},{"key":"23_CR9","unstructured":"Nils: Introduction to 1d convolutional neural networks in keras for time sequences. https:\/\/blog.goodaudience.com\/introduction-to-1d-convolutional-neural-networks-in-keras-for-time-sequences-3a7ff801a2cf"},{"key":"23_CR10","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"23_CR11","unstructured":"Fran\u00e7ois, C.: Keras. GitHub repository (2015). https:\/\/github.com\/fchollet\/keras"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: The mobiact dataset: recognition of activities of daily living using smartphones. In: ICT4AgeingWell, pp. 143\u2013151 (2016)","DOI":"10.5220\/0005792401430151"},{"key":"23_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","volume":"119","author":"J Wang","year":"2019","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recogn. Lett. 119, 3\u201311 (2019)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77211-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:05:54Z","timestamp":1706695554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77211-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030772109","9783030772116"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77211-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","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":"15 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime21.aimedicine.info\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}