{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:04:06Z","timestamp":1777381446710,"version":"3.51.4"},"reference-count":84,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2016,3,15]]},"DOI":"10.3233\/ais-160372","type":"journal-article","created":{"date-parts":[[2016,3,16]],"date-time":"2016-03-16T13:31:45Z","timestamp":1458135105000},"page":"87-107","source":"Crossref","is-referenced-by-count":125,"title":["Human activity recognition using multisensor data fusion based on Reservoir Computing"],"prefix":"10.1177","volume":"8","author":[{"given":"Filippo","family":"Palumbo","sequence":"first","affiliation":[{"name":"Institute of Information Science and Technologies \u201cAlessandro Faedo\u201d, National Research Council, Pisa, Italy"},{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claudio","family":"Gallicchio","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rita","family":"Pucci","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alessio","family":"Micheli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/AIS-160372_ref1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3233\/AIS-120192","article-title":"Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 evaal competition","volume":"5","author":"\u00c1lvarez-Garc\u00eda","year":"2013","journal-title":"Journal of Ambient Intelligence and Smart Environments"},{"key":"10.3233\/AIS-160372_ref3","doi-asserted-by":"crossref","unstructured":"[3]G.\u00a0Amato, M.\u00a0Broxvall, S.\u00a0Chessa, M.\u00a0Dragone, C.\u00a0Gennaro, R.\u00a0Lopez, L.\u00a0Maguire, T.\u00a0McGinnity, A.\u00a0Micheli, R.\u00a0Renteria, G.O.\u00a0Hare and F.\u00a0Pecora, Robotic ubiquitous cognitive network, in: Ambient Intelligence \u2013 Software and Applications, P.\u00a0Novais, K.\u00a0Hallenborg, D.I.\u00a0Tapia and J.M.C.\u00a0Rodriguez, eds, Advances in Intelligent and Soft Computing, Vol.\u00a0153, Springer, Berlin, Heidelberg, 2012, pp.\u00a0191\u2013195.","DOI":"10.1007\/978-3-642-28783-1_23"},{"key":"10.3233\/AIS-160372_ref4","doi-asserted-by":"crossref","unstructured":"[4]D.\u00a0Anguita, A.\u00a0Ghio, L.\u00a0Oneto, X.\u00a0Parra and J.L.\u00a0Reyes-Ortiz, Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, in: Ambient Assisted Living and Home Care, Springer, 2012, pp.\u00a0216\u2013223.","DOI":"10.1007\/978-3-642-35395-6_30"},{"issue":"6","key":"10.3233\/AIS-160372_ref5","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s00521-013-1364-4","article-title":"An experimental characterization of reservoir computing in ambient assisted living applications","volume":"24","author":"Bacciu","year":"2014","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/AIS-160372_ref6","doi-asserted-by":"crossref","unstructured":"[6]D.\u00a0Bacciu, S.\u00a0Chessa, C.\u00a0Gallicchio, A.\u00a0Lenzi, A.\u00a0Micheli and S.\u00a0Pelagatti, A general purpose distributed learning model for robotic ecologies, in: Proc. of the 10th International IFAC Symposium on Robot Control (SYROCO), Robot Control, Vol.\u00a010, no.\u00a01, 2012, pp.\u00a0435\u2013440.","DOI":"10.3182\/20120905-3-HR-2030.00178"},{"key":"10.3233\/AIS-160372_ref7","doi-asserted-by":"crossref","unstructured":"[7]D.\u00a0Bacciu, S.\u00a0Chessa, C.\u00a0Gallicchio, A.\u00a0Micheli and P.\u00a0Barsocchi, An experimental evaluation of reservoir computation for ambient assisted living, in: Neural Nets and Surroundings, B.\u00a0Apolloni, S.\u00a0Bassis, A.\u00a0Esposito and F.C.\u00a0Morabito, eds, Smart Innovation, Systems and Technologies, Vol.\u00a019, Springer, Berlin, Heidelberg, 2013, pp.\u00a041\u201350.","DOI":"10.1007\/978-3-642-35467-0_5"},{"key":"10.3233\/AIS-160372_ref8","unstructured":"[8]D.\u00a0Bacciu, C.\u00a0Gallicchio, A.\u00a0Micheli, S.\u00a0Chessa and P.\u00a0Barsocchi, Predicting user movements in heterogeneous indoor environments by reservoir computing, in: Proc. of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI) 2011, M.\u00a0Bhatt, H.W.\u00a0Guesgen and J.C.\u00a0Augusto, eds, 2011, pp.\u00a01\u20136."},{"key":"10.3233\/AIS-160372_ref9","doi-asserted-by":"crossref","unstructured":"[9]D.\u00a0Bacciu, C.\u00a0Gallicchio, A.\u00a0Micheli, M.\u00a0Di Rocco and A.\u00a0Saffiotti, Learning context-aware mobile robot navigation in home environments, in: Proc. of the 5th International Conference on Information, Intelligence, Systems and Applications, IISA 2014, IEEE, 2014, pp.\u00a057\u201362.","DOI":"10.1109\/IISA.2014.6878733"},{"key":"10.3233\/AIS-160372_ref10","doi-asserted-by":"crossref","unstructured":"[10]L.\u00a0Bao and S.S.\u00a0Intille, Activity recognition from user-annotated acceleration data, in: Pervasive Computing, Springer, 2004, pp.\u00a01\u201317.","DOI":"10.1007\/978-3-540-24646-6_1"},{"issue":"4","key":"10.3233\/AIS-160372_ref12","doi-asserted-by":"crossref","first-page":"978","DOI":"10.3390\/ijgi2040978","article-title":"Forecast-driven enhancement of received signal strength (RSS)-based localization systems","volume":"2","author":"Barsocchi","year":"2013","journal-title":"ISPRS International Journal of Geo-Information"},{"key":"10.3233\/AIS-160372_ref13","doi-asserted-by":"crossref","unstructured":"[13]P.\u00a0Barsocchi, F.\u00a0Potort\u00ec and P.\u00a0Nepa, Device-free indoor localization for AAL applications, in: Wireless Mobile Communication and Healthcare, Springer, 2013, pp.\u00a0361\u2013368.","DOI":"10.1007\/978-3-642-37893-5_40"},{"key":"10.3233\/AIS-160372_ref14","doi-asserted-by":"crossref","unstructured":"[14]M.\u00a0Bocca, O.\u00a0Kaltiokallio and N.\u00a0Patwari, Radio tomographic imaging for ambient assisted living, in: Evaluating AAL Systems Through Competitive Benchmarking, Springer, 2013, pp.\u00a0108\u2013130.","DOI":"10.1007\/978-3-642-37419-7_9"},{"key":"10.3233\/AIS-160372_ref15","doi-asserted-by":"crossref","unstructured":"[15]J.A.\u00a0Bot\u00eda, J.A.\u00c1.\u00a0Garc\u00eda, K.\u00a0Fujinami, P.\u00a0Barsocchi and T.\u00a0Riedel, Evaluating AAL Systems Through Competitive Benchmarking, Springer, 2013.","DOI":"10.1007\/978-3-642-41043-7"},{"issue":"2","key":"10.3233\/AIS-160372_ref16","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait & Posture"},{"key":"10.3233\/AIS-160372_ref17","doi-asserted-by":"crossref","unstructured":"[17]T.\u00a0Brezmes, J.-L.\u00a0Gorricho and J.\u00a0Cotrina, Activity recognition from accelerometer data on a mobile phone, in: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, Springer, 2009, pp.\u00a0796\u2013799.","DOI":"10.1007\/978-3-642-02481-8_120"},{"key":"10.3233\/AIS-160372_ref18","doi-asserted-by":"crossref","unstructured":"[18]C.-Y.\u00a0Chang, B.\u00a0Lange, M.\u00a0Zhang, S.\u00a0Koenig, P.\u00a0Requejo, N.\u00a0Somboon, A.A.\u00a0Sawchuk and A.A.\u00a0Rizzo, Towards pervasive physical rehabilitation using Microsoft kinect, in: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), IEEE, 2012, pp.\u00a0159\u2013162.","DOI":"10.4108\/icst.pervasivehealth.2012.248714"},{"key":"10.3233\/AIS-160372_ref19","doi-asserted-by":"crossref","unstructured":"[19]S.\u00a0Chessa, C.\u00a0Gallicchio, R.\u00a0Guzman and A.\u00a0Micheli, Robot localization by echo state networks using RSS, in: Recent Advances of Neural Network Models and Applications, S.\u00a0Bassis, A.\u00a0Esposito and F.C.\u00a0Morabito, eds, Smart Innovation, Systems and Technologies, Vol.\u00a026, Springer International Publishing, 2014, pp.\u00a0147\u2013154.","DOI":"10.1007\/978-3-319-04129-2_15"},{"issue":"2","key":"10.3233\/AIS-160372_ref20","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MPRV.2008.39","article-title":"The mobile sensing platform: An embedded activity recognition system","volume":"7","author":"Choudhury","year":"2008","journal-title":"IEEE Pervasive Computing"},{"key":"10.3233\/AIS-160372_ref21","doi-asserted-by":"crossref","unstructured":"[21]S.\u00a0Coradeschi, A.\u00a0Cesta, G.\u00a0Cortellessa, L.\u00a0Coraci, J.\u00a0Gonzalez, L.\u00a0Karlsson, F.\u00a0Furfari, A.\u00a0Loutfi, A.\u00a0Orlandini, F.\u00a0Palumbo et al., Giraffplus: Combining social interaction and long term monitoring for promoting independent living, in: 2013 The 6th International Conference on Human System Interaction (HSI), IEEE, 2013, pp.\u00a0578\u2013585.","DOI":"10.1109\/HSI.2013.6577883"},{"key":"10.3233\/AIS-160372_ref22","doi-asserted-by":"crossref","unstructured":"[22]M.\u00c1.\u00c1.\u00a0de la Concepci\u00f3n, L.M.S.\u00a0Morillo, L.G.\u00a0Abril and J.A.O.\u00a0Ram\u00edrez, Activity recognition system using non-intrusive devices through a complementary technique based on discrete methods, in: Evaluating AAL Systems Through Competitive Benchmarking, Springer, 2013, pp.\u00a036\u201347.","DOI":"10.1007\/978-3-642-41043-7_4"},{"key":"10.3233\/AIS-160372_ref23","unstructured":"[23]K.\u00a0Ellis, S.\u00a0Godbole, J.\u00a0Kerr and G.\u00a0Lanckriet, Multi-sensor physical activity recognition in free-living, in: Proc. of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, ACM, 2014, pp.\u00a0431\u2013440."},{"issue":"1","key":"10.3233\/AIS-160372_ref24","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TITB.2007.899496","article-title":"Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions","volume":"12","author":"Ermes","year":"2008","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"5","key":"10.3233\/AIS-160372_ref25","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.neunet.2011.02.002","article-title":"Architectural and Markovian factors of echo state networks","volume":"24","author":"Gallicchio","year":"2011","journal-title":"Neural Networks"},{"key":"10.3233\/AIS-160372_ref26","doi-asserted-by":"crossref","unstructured":"[26]C.\u00a0Gallicchio, A.\u00a0Micheli, P.\u00a0Barsocchi and S.\u00a0Chessa, User movements forecasting by reservoir computing using signal streams produced by mote-class sensors, in: Mobile Lightweight Wireless Systems (Mobilight 2011), Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol.\u00a081, Springer, Berlin, Heidelberg, 2012, pp.\u00a0151\u2013168.","DOI":"10.1007\/978-3-642-29479-2_12"},{"key":"10.3233\/AIS-160372_ref27","unstructured":"[27]S.\u00a0Haykin, Neural Networks: A Comprehensive Foundation, 2nd edn, Prentice Hall, 1999."},{"key":"10.3233\/AIS-160372_ref28","doi-asserted-by":"crossref","unstructured":"[28]Z.\u00a0He and L.\u00a0Jin, Activity recognition from acceleration data using AR model representation and SVM, in: Proc. of the International Conference on Machine Learning and Cybernetics, Vol.\u00a04, IEEE, 2008, pp.\u00a02245\u20132250.","DOI":"10.1109\/ICMLC.2008.4620779"},{"key":"10.3233\/AIS-160372_ref29","doi-asserted-by":"crossref","unstructured":"[29]Z.\u00a0He and L.\u00a0Jin, Activity recognition from acceleration data based on discrete consine transform and SVM, in: IEEE International Conference on Systems, Man and Cybernetics, 2009, SMC 2009, IEEE, 2009, pp.\u00a05041\u20135044.","DOI":"10.1109\/ICSMC.2009.5346042"},{"key":"10.3233\/AIS-160372_ref30","doi-asserted-by":"crossref","unstructured":"[30]U.\u00a0Hunkeler, H.L.\u00a0Truong and A.\u00a0Stanford-Clark, MQTT-S a publish\/subscribe protocol for wireless sensor networks, in: 3rd International Conference on Communication Systems Software and Middleware and Workshops, 2008, COMSWARE 2008, IEEE, 2008, pp.\u00a0791\u2013798.","DOI":"10.1109\/COMSWA.2008.4554519"},{"key":"10.3233\/AIS-160372_ref31","unstructured":"[31]D.P.\u00a0Hunt, D.\u00a0Parry and S.\u00a0Schliebs, Exploring the applicability of reservoir methods for classifying punctual sports activities using on-body sensors, in: Proc. of the Thirty-Seventh Australasian Computer Science Conference, Vol.\u00a0147, Australian Computer Society, Inc., 2014, pp.\u00a067\u201373."},{"key":"10.3233\/AIS-160372_ref32","doi-asserted-by":"crossref","unstructured":"[32]N.\u00a0Imamoglu, Z.\u00a0Wei, H.\u00a0Shi, Y.\u00a0Yoshida, M.\u00a0Nergui, J.\u00a0Gonzalez, D.\u00a0Gu, W.\u00a0Chen, K.\u00a0Nonami and W.\u00a0Yu, An improved saliency for RGB-D visual tracking and control strategies for a bio-monitoring mobile robot, in: Evaluating AAL Systems Through Competitive Benchmarking, Springer, 2013, pp.\u00a01\u201312.","DOI":"10.1007\/978-3-642-41043-7_1"},{"issue":"5667","key":"10.3233\/AIS-160372_ref34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.1091277","article-title":"Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication","volume":"304","author":"Jaeger","year":"2004","journal-title":"Science"},{"issue":"3","key":"10.3233\/AIS-160372_ref35","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neunet.2007.04.016","article-title":"Optimization and applications of echo state networks with leaky-integrator neurons","volume":"20","author":"Jaeger","year":"2007","journal-title":"Neural Networks"},{"issue":"1","key":"10.3233\/AIS-160372_ref36","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"5","key":"10.3233\/AIS-160372_ref37","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/TITB.2010.2051955","article-title":"A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer","volume":"14","author":"Khan","year":"2010","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"key":"10.3233\/AIS-160372_ref39","unstructured":"[39]J.\u00a0Kolen and S.\u00a0Kremer\u00a0(eds), A Field Guide to Dynamical Recurrent Networks, IEEE Press, 2001."},{"key":"10.3233\/AIS-160372_ref40","doi-asserted-by":"crossref","unstructured":"[40]S.\u00a0Kozina, H.\u00a0Gjoreski, M.\u00a0Gams and M.\u00a0Lu\u0161trek, Efficient activity recognition and fall detection using accelerometers, in: Evaluating AAL Systems Through Competitive Benchmarking, Springer, 2013, pp.\u00a013\u201323.","DOI":"10.1007\/978-3-642-41043-7_2"},{"issue":"1","key":"10.3233\/AIS-160372_ref41","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1024494031121","article-title":"Rehabilitation robotics: Performance-based progressive robot-assisted therapy","volume":"15","author":"Krebs","year":"2003","journal-title":"Autonomous Robots"},{"key":"10.3233\/AIS-160372_ref42","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.pmcj.2012.07.003","article-title":"Activity recognition on streaming sensor data","volume":"10","author":"Krishnan","year":"2014","journal-title":"Pervasive and Mobile Computing"},{"issue":"2","key":"10.3233\/AIS-160372_ref43","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explorations Newsletter"},{"key":"10.3233\/AIS-160372_ref44","doi-asserted-by":"crossref","unstructured":"[44]B.\u00a0Lange, C.-Y.\u00a0Chang, E.\u00a0Suma, B.\u00a0Newman, A.S.\u00a0Rizzo and M.\u00a0Bolas, Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft kinect sensor, in: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, IEEE, 2011, pp.\u00a01831\u20131834.","DOI":"10.1109\/IEMBS.2011.6090521"},{"key":"10.3233\/AIS-160372_ref45","doi-asserted-by":"crossref","unstructured":"[45]O.D.\u00a0Lara and M.A.\u00a0Labrador, A mobile platform for real-time human activity recognition, in: Proc. of the Consumer Communications and Networking Conference (CCNC), IEEE, 2012, pp.\u00a0667\u2013671.","DOI":"10.1109\/CCNC.2012.6181018"},{"issue":"3","key":"10.3233\/AIS-160372_ref46","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"10.3233\/AIS-160372_ref47","doi-asserted-by":"crossref","unstructured":"[47]H.\u00a0Leutheuser, D.\u00a0Schuldhaus and B.M.\u00a0Eskofier, Hierarchical, multi-sensor based classification of daily life activities: Comparison with state-of-the-art algorithms using a benchmark dataset, PloS One 8(10) (2013), e75196.","DOI":"10.1371\/journal.pone.0075196"},{"key":"10.3233\/AIS-160372_ref48","doi-asserted-by":"crossref","unstructured":"[48]Q.\u00a0Li, J.A.\u00a0Stankovic, M.A.\u00a0Hanson, A.T.\u00a0Barth, J.\u00a0Lach and G.\u00a0Zhou, Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information, in: Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009, BSN 2009, IEEE, 2009, pp.\u00a0138\u2013143.","DOI":"10.1109\/BSN.2009.46"},{"key":"10.3233\/AIS-160372_ref49","unstructured":"[49]L.\u00a0Liao, D.\u00a0Fox and H.\u00a0Kautz, Location-based activity recognition using relational Markov networks, in: Proc. of the 19th International Joint Conference on Artificial Intelligence, IJCAI\u201905, Morgan Kaufmann Publishers Inc., 2005, pp.\u00a0773\u2013778."},{"issue":"5","key":"10.3233\/AIS-160372_ref50","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1061\/(ASCE)CP.1943-5487.0000097","article-title":"Accelerometer-based activity recognition in construction","volume":"25","author":"Liju","year":"2011","journal-title":"Journal of Computing in Civil Engineering"},{"key":"10.3233\/AIS-160372_ref51","doi-asserted-by":"crossref","unstructured":"[51]X.\u00a0Long, B.\u00a0Yin and R.M.\u00a0Aarts, Single-accelerometer-based daily physical activity classification, in: Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBC 2009, IEEE, 2009, pp.\u00a06107\u20136110.","DOI":"10.1109\/IEMBS.2009.5334925"},{"issue":"3","key":"10.3233\/AIS-160372_ref52","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","article-title":"Reservoir computing approaches to recurrent neural network training","volume":"3","author":"Luko\u0161evi\u010dius","year":"2009","journal-title":"Computer Science Review"},{"issue":"4","key":"10.3233\/AIS-160372_ref53","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s13218-012-0204-5","article-title":"Reservoir computing trends","volume":"26","author":"Luko\u0161evi\u010dius","year":"2012","journal-title":"KI-K\u00fcnstliche Intelligenz"},{"issue":"2","key":"10.3233\/AIS-160372_ref54","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"10.3233\/AIS-160372_ref55","doi-asserted-by":"crossref","unstructured":"[55]U.\u00a0Maurer, A.\u00a0Smailagic, D.\u00a0Siewiorek and M.\u00a0Deisher, Activity recognition and monitoring using multiple sensors on different body positions, in: Proc. of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN), IEEE, 2006, pp.\u00a0116\u2013119.","DOI":"10.21236\/ADA534437"},{"key":"10.3233\/AIS-160372_ref56","doi-asserted-by":"crossref","unstructured":"[56]F.\u00a0Moiz, P.\u00a0Natoo, R.\u00a0Derakhshani and W.D.\u00a0Leon-Salas, A comparative study of classification methods for gesture recognition using a 3-axis accelerometer, in: 2011 International Joint Conference on Neural Networks, (IJCNN), IEEE, 2011, pp.\u00a02479\u20132486.","DOI":"10.1109\/IJCNN.2011.6033541"},{"issue":"4","key":"10.3233\/AIS-160372_ref57","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TASE.2010.2049840","article-title":"Behavior analysis for assisted living","volume":"7","author":"Monekosso","year":"2010","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"6","key":"10.3233\/AIS-160372_ref58","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1242\/jeb.058602","article-title":"Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: General concepts and tools illustrated for griffon vultures","volume":"215","author":"Nathan","year":"2012","journal-title":"The Journal of Experimental Biology"},{"key":"10.3233\/AIS-160372_ref59","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.biosystemseng.2014.01.005","article-title":"Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network","volume":"119","author":"Oczak","year":"2014","journal-title":"Biosystems Engineering"},{"key":"10.3233\/AIS-160372_ref61","doi-asserted-by":"crossref","unstructured":"[61]F.\u00a0Palumbo, P.\u00a0Barsocchi, C.\u00a0Gallicchio, S.\u00a0Chessa and A.\u00a0Micheli, Multisensor data fusion for activity recognition based on reservoir computing, in: Evaluating AAL Systems Through Competitive Benchmarking, J.\u00a0Bot\u00eda, J.\u00a0Alvarez-Garcia, K.\u00a0Fujinami, P.\u00a0Barsocchi and T.\u00a0Riedel, eds, Communications in Computer and Information Science, Vol.\u00a0386, Springer, Berlin, Heidelberg, 2013, pp.\u00a024\u201335.","DOI":"10.1007\/978-3-642-41043-7_3"},{"key":"10.3233\/AIS-160372_ref62","doi-asserted-by":"crossref","unstructured":"[62]F.\u00a0Palumbo, D.\u00a0La Rosa and S.\u00a0Chessa, GP-m: Mobile middleware infrastructure for ambient assisted living, in: 2014 IEEE Symposium on Computers and Communication (ISCC), IEEE, 2014, pp.\u00a01\u20136.","DOI":"10.1109\/ISCC.2014.6912623"},{"issue":"3","key":"10.3233\/AIS-160372_ref63","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.3390\/s140303833","article-title":"Sensor network infrastructure for a home care monitoring system","volume":"14","author":"Palumbo","year":"2014","journal-title":"Sensors"},{"issue":"5","key":"10.3233\/AIS-160372_ref64","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TITB.2010.2055060","article-title":"Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree","volume":"14","author":"Parkka","year":"2010","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"1","key":"10.3233\/AIS-160372_ref65","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TITB.2005.856863","article-title":"Activity classification using realistic data from wearable sensors","volume":"10","author":"Parkka","year":"2006","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"key":"10.3233\/AIS-160372_ref66","doi-asserted-by":"crossref","unstructured":"[66]N.\u00a0Patwari and A.O.\u00a0Hero III, Using proximity and quantized RSS for sensor localization in wireless networks, in: Proc. of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, ACM, 2003, pp.\u00a020\u201329.","DOI":"10.1145\/941350.941354"},{"key":"10.3233\/AIS-160372_ref67","doi-asserted-by":"crossref","unstructured":"[67]T.\u00a0Peterek, M.\u00a0Penhaker, P.\u00a0Gajdo\u0161 and P.\u00a0Dohn\u00e1lek, Comparison of classification algorithms for physical activity recognition, in: Innovations in Bio-Inspired Computing and Applications, Springer, 2014, pp.\u00a0123\u2013131.","DOI":"10.1007\/978-3-319-01781-5_12"},{"issue":"4","key":"10.3233\/AIS-160372_ref68","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MPRV.2004.7","article-title":"Inferring activities from interactions with objects","volume":"3","author":"Philipose","year":"2004","journal-title":"IEEE Pervasive Computing"},{"key":"10.3233\/AIS-160372_ref69","doi-asserted-by":"crossref","unstructured":"[69]A.\u00a0Purwar, D.\u00a0un\u00a0Jeong and W.Y.\u00a0Chung, Activity monitoring from real-time triaxial accelerometer data using sensor network, in: International Conference on Control, Automation and Systems, 2007, ICCAS\u201907, IEEE, 2007, pp.\u00a02402\u20132406.","DOI":"10.1109\/ICCAS.2007.4406764"},{"issue":"1","key":"10.3233\/AIS-160372_ref70","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MASSP.1986.1165342","article-title":"An introduction to hidden Markov models","volume":"3","author":"Rabiner","year":"1986","journal-title":"IEEE ASSP Magazine"},{"key":"10.3233\/AIS-160372_ref71","unstructured":"[71]C.\u00a0Randell and H.\u00a0Muller, Context awareness by analysing accelerometer data, in: The Fourth International Symposium on Wearable Computers, IEEE, 2000, pp.\u00a0175\u2013176."},{"issue":"3","key":"10.3233\/AIS-160372_ref72","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/JBHI.2012.2234129","article-title":"A survey on ambient-assisted living tools for older adults","volume":"17","author":"Rashidi","year":"2013","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/AIS-160372_ref73","unstructured":"[73]N.\u00a0Ravi, N.\u00a0Dandekar, P.\u00a0Mysore and M.L.\u00a0Littman, Activity recognition from accelerometer data, in: Proc. of the Seventeenth Conference on Innovative Applications of Artificial Intelligence (IAAI), AAAI Press, 2005, pp.\u00a01541\u20131546."},{"key":"10.3233\/AIS-160372_ref74","doi-asserted-by":"crossref","unstructured":"[74]V.N.T.\u00a0Sang, N.D.\u00a0Thang, V.\u00a0Van Toi, N.D.\u00a0Hoang and T.Q.D.\u00a0Khoa, Human activity recognition and monitoring using smartphones, in: 5th International Conference on Biomedical Engineering in Vietnam, Springer, 2015, pp.\u00a0481\u2013485.","DOI":"10.1007\/978-3-319-11776-8_119"},{"key":"10.3233\/AIS-160372_ref75","unstructured":"[75]M.D.\u00a0Skowronski and J.G.\u00a0Harris, Minimum mean squared error time series classification using an echo state network prediction model, in: Proc. of the 2006 IEEE International Symposium on Circuits and Systems, ISCAS, IEEE, 2006, p.\u00a04."},{"issue":"4","key":"10.3233\/AIS-160372_ref76","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Information Processing & Management"},{"key":"10.3233\/AIS-160372_ref77","doi-asserted-by":"crossref","unstructured":"[77]P.\u00a0Ti\u0148o, B.\u00a0Hammer and M.\u00a0Bod\u00e9n, Markovian bias of neural-based architectures with feedback connections, in: Perspectives of Neural-Symbolic Integration, Springer, 2007, pp.\u00a095\u2013133.","DOI":"10.1007\/978-3-540-73954-8_5"},{"issue":"3\u20134","key":"10.3233\/AIS-160372_ref78","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/S0925-2312(97)00161-6","article-title":"Discrete time recurrent neural network architectures: A unifying review","volume":"15","author":"Tsoi","year":"1997","journal-title":"Neurocomputing"},{"key":"10.3233\/AIS-160372_ref79","unstructured":"[79]T.\u00a0van Kasteren, H.\u00a0Alemdar and C.\u00a0Ersoy, Effective performance metrics for evaluating activity recognition methods, in: Proc. of the 24th International Conference on Architecture of Computing Systems (ARCS), 2011, pp.\u00a0301\u2013310."},{"key":"10.3233\/AIS-160372_ref80","doi-asserted-by":"crossref","unstructured":"[80]K.\u00a0Van Laerhoven, A.\u00a0Schmidt and H.-W.\u00a0Gellersen, Multi-sensor context aware clothing, in: Proc. of the Sixth International Symposium on Wearable Computers, 2002, (ISWC 2002), IEEE, 2002, pp.\u00a049\u201356.","DOI":"10.1109\/ISWC.2002.1167218"},{"issue":"5","key":"10.3233\/AIS-160372_ref81","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"3","key":"10.3233\/AIS-160372_ref82","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.neunet.2007.04.003","article-title":"An experimental unification of reservoir computing methods","volume":"20","author":"Verstraeten","year":"2007","journal-title":"Neural Networks"},{"issue":"1","key":"10.3233\/AIS-160372_ref83","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1162\/neco.1989.1.1.39","article-title":"Modular construction of time-delay neural networks for speech recognition","volume":"1","author":"Waibel","year":"1989","journal-title":"Neural Computation"},{"issue":"3","key":"10.3233\/AIS-160372_ref84","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"Acoustics, Speech and Signal Processing"},{"issue":"3","key":"10.3233\/AIS-160372_ref85","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.pmcj.2010.11.008","article-title":"Recognizing multi-user activities using wearable sensors in a smart home","volume":"7","author":"Wang","year":"2011","journal-title":"Pervasive and Mobile Computing"},{"issue":"1","key":"10.3233\/AIS-160372_ref86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1889681.1889687","article-title":"Performance metrics for activity recognition","volume":"2","author":"Ward","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.3233\/AIS-160372_ref87","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/JBHI.2013.2287400","article-title":"Highly accurate recognition of human postures and activities through classification with rejection","volume":"18","author":"Wenlong","year":"2014","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/AIS-160372_ref88","doi-asserted-by":"crossref","unstructured":"[88]D.H.\u00a0Wilson and C.\u00a0Atkeson, Simultaneous tracking and activity recognition (star) using many anonymous, binary sensors, in: Pervasive Computing, Springer, 2005, pp.\u00a062\u201379.","DOI":"10.1007\/11428572_5"},{"key":"10.3233\/AIS-160372_ref90","doi-asserted-by":"crossref","unstructured":"[90]C.\u00a0Zhu and W.\u00a0Sheng, Human daily activity recognition in robot-assisted living using multi-sensor fusion, in: Proc. of the IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2009, pp.\u00a02154\u20132159.","DOI":"10.1109\/ROBOT.2009.5152756"}],"container-title":["Journal of Ambient Intelligence and Smart Environments"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIS-160372","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:20:45Z","timestamp":1777368045000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIS-160372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,15]]},"references-count":84,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/ais-160372","relation":{},"ISSN":["1876-1372","1876-1364"],"issn-type":[{"value":"1876-1372","type":"electronic"},{"value":"1876-1364","type":"print"}],"subject":[],"published":{"date-parts":[[2016,3,15]]}}}