{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:29:09Z","timestamp":1760243349697,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2014,11,3]],"date-time":"2014-11-03T00:00:00Z","timestamp":1414972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, an adaptive activity and environment recognition algorithm running on a mobile phone is presented. The algorithm makes inferences based on sensor and radio receiver data provided by the phone. A wide set of features that can be extracted from these data sources were investigated, and a Bayesian maximum a posteriori classifier was used for classifying between several user activities and environments. The accuracy of the method was evaluated on a dataset collected in a real-life trial. In addition, comparison to other state-of-the-art classifiers, namely support vector machines and decision trees, was performed. To make the system adaptive for individual user characteristics, an adaptation algorithm for context model parameters was designed. Moreover, a confidence measure for the classification correctness was designed. The proposed adaptation algorithm and confidence measure were evaluated on a second dataset obtained from another real-life trial, where the users were requested to provide binary feedback on the classification correctness. The results show that the proposed adaptation algorithm is effective at improving the classification accuracy.<\/jats:p>","DOI":"10.3390\/s141120753","type":"journal-article","created":{"date-parts":[[2014,11,3]],"date-time":"2014-11-03T08:52:52Z","timestamp":1415004772000},"page":"20753-20778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Adaptive Activity and Environment Recognition for Mobile Phones"],"prefix":"10.3390","volume":"14","author":[{"given":"Jussi","family":"Parviainen","sequence":"first","affiliation":[{"name":"Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland"}]},{"given":"Jayaprasad","family":"Bojja","sequence":"additional","affiliation":[{"name":"Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland"}]},{"given":"Jussi","family":"Collin","sequence":"additional","affiliation":[{"name":"Department of Pervasive Computing, Tampere University of Technology, FI-33101 Tampere, Finland"}]},{"given":"Jussi","family":"Lepp\u00e4nen","sequence":"additional","affiliation":[{"name":"Nokia Technologies, FI-33721 Tampere, Finland"}]},{"given":"Antti","family":"Eronen","sequence":"additional","affiliation":[{"name":"Nokia Technologies, FI-33721 Tampere, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1016\/j.patcog.2010.04.019","article-title":"Comparative study on classifying human activities with miniature inertial and magnetic sensors","volume":"43","author":"Altun","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/TMC.2011.43","article-title":"Recognizing Multiuser Activities Using Wireless Body Sensor Networks","volume":"10","author":"Gu","year":"2011","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.pmcj.2009.07.001","article-title":"Automatic feature selection for context recognition in mobile devices","volume":"6","author":"Ermes","year":"2010","journal-title":"Pervasive Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., and Srivastava, M. (2010). Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw., 6.","DOI":"10.1145\/1689239.1689243"},{"key":"ref_5","unstructured":"Santos, A.C., Tarrataca, L., Cardoso, J.M.P., Ferreira, D.R., Diniz, P.C., and Chainho, P. (2009). MobileWireless Middleware, Operating Systems, and Applications, Springer Berlin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.pmcj.2010.01.001","article-title":"Providing user context for mobile and social networking applications","volume":"6","author":"Santos","year":"2010","journal-title":"Pervasive Mob. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., and Campbell, A.T. (2008, January 4\u20137). Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. Raleigh, NC, USA.","DOI":"10.1145\/1460412.1460445"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1109\/TMC.2009.139","article-title":"Activity-Based Proactive Data Management in Mobile Environments","volume":"9","author":"Wu","year":"2010","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TMC.2011.113","article-title":"Scalable Activity-Travel Pattern Monitoring Framework for Large-scale City Environment","volume":"11","author":"Lee","year":"2011","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bancroft, J., Garrett, D., and Lachapelle, G. (2012, January 13\u201315). Activity and Environment Classification Using Foot Mounted Navigation Sensors. Sydney, Australia.","DOI":"10.1109\/IPIN.2012.6418902"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.3390\/s130201402","article-title":"Human Behavior Cognition Using Smartphone Sensors","volume":"13","author":"Pei","year":"2013","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.3390\/s130201539","article-title":"Motion mode recognition and step detection algorithms for mobile phone users","volume":"13","author":"Susi","year":"2013","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16181","DOI":"10.3390\/s140916181","article-title":"A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors","volume":"14","author":"Han","year":"2014","journal-title":"Sensors"},{"key":"ref_14","unstructured":"Lin, T., ODriscoll, C., and Lachapelle, G. (2011, January 24\u201326). Development of a Context-Aware Vector-Based High-Sensitivity GNSS Software Receiver. San Diego, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","article-title":"Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences","volume":"28","author":"Davis","year":"1980","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Webb, A. (2002). Statistical Pattern Recognition, Wiley. [2nd ed.].","DOI":"10.1002\/0470854774"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0031-3203(73)90025-3","article-title":"A new approach to feature selection based on the Karhunen-Loeve expansion","volume":"5","author":"Kittler","year":"1973","journal-title":"Pattern Recognit."},{"key":"ref_18","unstructured":"Rao, C.R., and Toutenburg, H. (1999). Linear Models, Least Squares and Alternatives, Springer."},{"key":"ref_19","first-page":"92","article-title":"Algorithm AS 106: The Distribution of Non-Negative Quadratic Forms in Normal Variables","volume":"26","author":"Sheil","year":"1977","journal-title":"J. R. Statist. Soc. Ser. C"},{"key":"ref_20","unstructured":"Dietterich, T.G. (2000). Multiple Classifier Systems, Springer."},{"key":"ref_21","first-page":"289","article-title":"On the problem of the most efficient tests of statistical hypotheses","volume":"231","author":"Neyman","year":"1933","journal-title":"R. Soc."},{"key":"ref_22","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley. [2nd ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1137\/S1052623496303470","article-title":"Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions","volume":"9","author":"Lagarias","year":"1998","journal-title":"SIAM J. Optim."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TAES.2007.4441741","article-title":"User-level reliability monitoring in urban personal satellite-navigation","volume":"43","author":"Kuusniemi","year":"2007","journal-title":"IEEE TranS. Aerosp. Electron. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5009","DOI":"10.1109\/T-WC.2008.070830","article-title":"Quadratic forms in normal RVs: Theory and applications to OSTBC over hoyt fading channels","volume":"7","author":"Ropokis","year":"2008","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10291-010-0188-2","article-title":"Statistical characterization of composite protection levels for GPS","volume":"15","author":"Bruckner","year":"2011","journal-title":"GPS Solut."},{"key":"ref_27","unstructured":"Multivariate Normal Distribution Value for an ellipsoid Matlab algorithm. Available online: http:\/\/www.math.wsu.edu\/faculty\/genz\/software\/matlab\/mvnlps.m."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.specom.2004.12.004","article-title":"Confidence measures for speech recognition: A survey","volume":"45","author":"Jiang","year":"2005","journal-title":"Speech Commun."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"(2002). The class imbalance problem: A systematic study. Intell. Data Anal., 6, 429\u2013449.","DOI":"10.3233\/IDA-2002-6504"},{"key":"ref_30","unstructured":"Kantola, J., Perttunen, M., Leppanen, T., Collin, J., and Riekki, J. (2010, January 25\u201327). Context Awareness for GPS-Enabled Phones. San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., and Campbell, A.T. (2010, January 3\u20135). The Jigsaw continuous sensing engine for mobile phone applications. Zurich, Switzerland.","DOI":"10.1145\/1869983.1869992"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/20753\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:17:44Z","timestamp":1760217464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/11\/20753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,11,3]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2014,11]]}},"alternative-id":["s141120753"],"URL":"https:\/\/doi.org\/10.3390\/s141120753","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2014,11,3]]}}}