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MNLP_M2_rag_documents

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https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research#79
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IPIN. 2015. - ^ Paschke, Fabian, et al. "Sensorlose Zustandsüberwachung an Synchronmotoren."Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5.-6. Dezember 2013. KIT Scientific Publishing, 2013. - ^ Lessmeier, Christian, et al. "Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems." - ^ Ugulino, Wallace, et al. "Wearable computing: Accelerometers’ data classification of body postures and movements Archived 25 Sep
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ion of body postures and movements Archived 25 September 2020 at the Wayback Machine." Advances in Artificial Intelligence-SBIA 2012. Springer Berlin Heidelberg, 2012. 52–61. - ^ Schneider, Jan; et al. (2015). "Augmenting the senses: a review on sensor-based learning support". Sensors. 15 (2): 4097–4133. Bibcode:2015Senso..15.4097S. doi:10.3390/s150204097. PMC 4367401. PMID 25679313. - ^ Madeo, Renata CB, Clodoaldo AM Lima, and Sarajane M. Peres. "Gesture unit segmentation using support vector m
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"Gesture unit segmentation using support vector machines: segmenting gestures from rest positions." Proceedings of the 28th Annual ACM Symposium on Applied Computing. ACM, 2013. - ^ Lun, Roanna; Zhao, Wenbing (2015). "A survey of applications and human motion recognition with Microsoft Kinect". International Journal of Pattern Recognition and Artificial Intelligence. 29 (5): 1555008. doi:10.1142/s0218001415550083. - ^ Theodoridis, Theodoros; Huosheng Hu (2007). "Action classification of 3D huma
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sheng Hu (2007). "Action classification of 3D human models using dynamic ANNs for mobile robot surveillance". 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO). pp. 371–376. doi:10.1109/ROBIO.2007.4522190. ISBN 978-1-4244-1761-2. - ^ Etemad, Seyed Ali; Arya, Ali (2009). "3D human action recognition and style transformation using resilient backpropagation neural networks". 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. pp. 296–301. doi:10
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uting and Intelligent Systems. pp. 296–301. doi:10.1109/ICICISYS.2009.5357690. ISBN 978-1-4244-4754-1. - ^ Altun, Kerem; Barshan, Billur; Tunçel, Orkun (2010). "Comparative study on classifying human activities with miniature inertial and magnetic sensors". Pattern Recognition. 43 (10): 3605–3620. Bibcode:2010PatRe..43.3605A. doi:10.1016/j.patcog.2010.04.019. hdl:11693/11947. - ^ Nathan, Ran; et al. (2012). "Using tri-axial acceleration data to identify behavioral modes of free-ranging animals:
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dentify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures". The Journal of Experimental Biology. 215 (6): 986–996. Bibcode:2012JExpB.215..986N. doi:10.1242/jeb.058602. PMC 3284320. PMID 22357592. - ^ Anguita, Davide, et al. "Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine." Ambient assisted living and home care. Springer Berlin Heidelberg, 2012. 216–223. - ^ Su, Xing; Tong, Hanghang; Ji, Pin
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12. 216–223. - ^ Su, Xing; Tong, Hanghang; Ji, Ping (2014). "Activity recognition with smartphone sensors". Tsinghua Science and Technology. 19 (3): 235–249. doi:10.1109/tst.2014.6838194. S2CID 62751498. - ^ Kadous, Mohammed Waleed. Temporal classification: Extending the classification paradigm to multivariate time series. Diss. The University of New South Wales, 2002. - ^ Graves, Alex, et al. "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural netwo
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egmented sequence data with recurrent neural networks." Proceedings of the 23rd international conference on Machine learning. ACM, 2006. - ^ Velloso, Eduardo, et al. "Qualitative activity recognition of weight lifting exercises."Proceedings of the 4th Augmented Human International Conference. ACM, 2013. - ^ Mortazavi, Bobak Jack, et al. "Determining the single best axis for exercise repetition recognition and counting on smartwatches Archived 4 November 2021 at the Wayback Machine." Wearable and
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ovember 2021 at the Wayback Machine." Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on. IEEE, 2014. - ^ Sapsanis, Christos, et al. "Improving EMG based Classification of basic hand movements using EMD." Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013. - ^ a b Andrianesis, Konstantinos; Tzes, Anthony (2015). "Development and control of a multifunctional prosthetic hand with shape memory
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multifunctional prosthetic hand with shape memory alloy actuators". Journal of Intelligent & Robotic Systems. 78 (2): 257–289. doi:10.1007/s10846-014-0061-6. S2CID 207174078. - ^ Banos, Oresti; et al. (2014). "Dealing with the effects of sensor displacement in wearable activity recognition". Sensors. 14 (6): 9995–10023. Bibcode:2014Senso..14.9995B. doi:10.3390/s140609995. PMC 4118358. PMID 24915181. - ^ Stisen, Allan; Blunck, Henrik; Bhattacharya, Sourav; Prentow, Thor Siiger; Kjærgaard, Mikkel
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a, Sourav; Prentow, Thor Siiger; Kjærgaard, Mikkel Baun; Dey, Anind; Sonne, Tobias; Jensen, Mads Møller (2015). "Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition". Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. pp. 127–140. doi:10.1145/2809695.2809718. ISBN 978-1-4503-3631-4. - ^ Bhattacharya, Sourav; Lane, Nicholas D. (2016). "From smart to deep: Robust activity recognition on smartwatches using deep learn
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ivity recognition on smartwatches using deep learning". 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). pp. 1–6. doi:10.1109/PERCOMW.2016.7457169. ISBN 978-1-5090-1941-0. - ^ Bacciu, Davide; et al. (2014). "An experimental characterization of reservoir computing in ambient assisted living applications". Neural Computing and Applications. 24 (6): 1451–1464. doi:10.1007/s00521-013-1364-4. hdl:11568/237959. S2CID 14124013. - ^ Palumbo, Filip
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l:11568/237959. S2CID 14124013. - ^ Palumbo, Filippo; Barsocchi, Paolo; Gallicchio, Claudio; Chessa, Stefano; Micheli, Alessio (2013). "Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing". Evaluating AAL Systems Through Competitive Benchmarking. Communications in Computer and Information Science. Vol. 386. pp. 24–35. doi:10.1007/978-3-642-41043-7_3. ISBN 978-3-642-41042-0. - ^ Reiss, Attila; Stricker, Didier (2012). "Introducing a New Benchmarked Dataset for Activity M
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troducing a New Benchmarked Dataset for Activity Monitoring". 2012 16th International Symposium on Wearable Computers. pp. 108–109. doi:10.1109/ISWC.2012.13. ISBN 978-0-7695-4697-1. - ^ Roggen, Daniel; Forster, Kilian; Calatroni, Alberto; Holleczek, Thomas; Fang, Yu; Troster, Gerhard; Ferscha, Alois; Holzmann, Clemens; Riener, Andreas; Lukowicz, Paul; Pirkl, Gerald; Bannach, David; Kunze, Kai; Chavarriaga, Ricardo; Millan, Jose del R. (2009). "OPPORTUNITY: Towards opportunistic activity and cont
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PORTUNITY: Towards opportunistic activity and context recognition systems". 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops. pp. 1–6. doi:10.1109/WOWMOM.2009.5282442. ISBN 978-1-4244-4440-3. - ^ Kurz, Marc, et al. "Dynamic quantification of activity recognition capabilities in opportunistic systems." Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 2011. - ^ Sztyler, Timo; Stuckenschmidt, Heiner (2016). "On-body localization
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uckenschmidt, Heiner (2016). "On-body localization of wearable devices: An investigation of position-aware activity recognition". 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom). pp. 1–9. doi:10.1109/PERCOM.2016.7456521. ISBN 978-1-4673-8779-8. - ^ Zhi, Ying Xuan; Lukasik, Michelle; Li, Michael H.; Dolatabadi, Elham; Wang, Rosalie H.; Taati, Babak (2018). "Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy". IEEE Journal of Tran
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roke Rehabilitation Therapy". IEEE Journal of Translational Engineering in Health and Medicine. 6: 1–7. doi:10.1109/JTEHM.2017.2780836. PMC 5788403. PMID 29404226. - ^ Dolatabadi, Elham; Zhi, Ying Xuan; Ye, Bing; Coahran, Marge; Lupinacci, Giorgia; Mihailidis, Alex; Wang, Rosalie; Taati, Babak (2017). "The toronto rehab stroke pose dataset to detect compensation during stroke rehabilitation therapy". Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Hea
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erence on Pervasive Computing Technologies for Healthcare. pp. 375–381. doi:10.1145/3154862.3154925. ISBN 978-1-4503-6363-1. - ^ "Toronto Rehab Stroke Pose Dataset". - ^ Jung, Merel M.; Poel, Mannes; Poppe, Ronald; Heylen, Dirk K. J. (March 2017). "Automatic recognition of touch gestures in the corpus of social touch". Journal on Multimodal User Interfaces. 11 (1): 81–96. doi:10.1007/s12193-016-0232-9. - ^ Jung, M.M. (Merel) (1 June 2016). "Corpus of Social Touch (CoST)". University of Twente. d
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s of Social Touch (CoST)". University of Twente. doi:10.4121/uuid:5ef62345-3b3e-479c-8e1d-c922748c9b29. {{cite journal}} : Cite journal requires|journal= (help) - ^ Aeberhard, S., D. Coomans, and O. De Vel. "Comparison of classifiers in high dimensional settings." Dept. Math. Statist., James Cook Univ., North Queensland, Australia, Tech. Rep 92-02 (1992). - ^ Basu, Sugato. "Semi-supervised clustering with limited background knowledge." AAAI. 2004. - ^ Tüfekci, Pınar (2014). "Prediction of full l
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eptember 2021). "LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe". Earth System Science Data. 13 (9): 4529–4565. Bibcode:2021ESSD...13.4529K. doi:10.5194/essd-13-4529-2021. - ^ Yeh, I–C (1998). "Modeling of strength of high-performance concrete using artificial neural networks". Cement and Concrete Research. 28 (12): 1797–1808. doi:10.1016/s0008-8846(98)00165-3. - ^ Zarandi, MH Fazel; et al. (2008). "Fuzzy polynomial neural networks for approximation of th
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