{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T04:21:10Z","timestamp":1791174070595,"version":"4.1.0"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,1,24]],"date-time":"2013-01-24T00:00:00Z","timestamp":1358985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features\/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.<\/jats:p>","DOI":"10.3390\/s130201402","type":"journal-article","created":{"date-parts":[[2013,1,24]],"date-time":"2013-01-24T11:29:13Z","timestamp":1359026953000},"page":"1402-1424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":121,"title":["Human Behavior Cognition Using Smartphone Sensors"],"prefix":"10.3390","volume":"13","author":[{"given":"Ling","family":"Pei","sequence":"first","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Guinness","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruizhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"},{"name":"Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingbin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heidi","family":"Kuusniemi","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Navigation and Positioning, Finnish Geodetic Institute, FIN-02431 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jyrki","family":"Kaistinen","sequence":"additional","affiliation":[{"name":"Psychology of Evolving Media and Technology Research Group, Institute of Behavioural Sciences, University of Helsinki, 00014 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,1,24]]},"reference":[{"key":"ref_1","unstructured":"Hu, D.H., Zhang, X.X., Yin, J., Zheng, V.W., and Yang, Q. (July, January 11\u2013). Abnormal Activity Recognition Based on Hdp-Hmm Models. California, CA, USA."},{"key":"ref_2","unstructured":"Pei, L., Chen, R., Chen, Y., Lepp\u00e4koski, H., and Perttula, A. (July, January 20\u2013). Indoor\/Outdoor Seamless Positioning Technologies Integrated on Smart Phone. Colmar, France."},{"key":"ref_3","first-page":"52","article-title":"A-GNSS: A different approach","volume":"4","author":"Kraemer","year":"2009","journal-title":"Inside GNSS"},{"key":"ref_4","unstructured":"Syrj\u00e4rinne, J. (2001). Studies on Modern Techniques for Personal Positioning. [Ph.D. Thesis, Tampere University of Technology]."},{"key":"ref_5","unstructured":"Laura, K., Perala, T., and Pich\u00e9, R. (September, January 15\u2013). Indoor Positioning Using Wlan Coverage Area Estimates. Zurich, Switzerland."},{"key":"ref_6","first-page":"122","article-title":"Using inquiry-based Bluetooth RSSI probability distributions for indoor positioning","volume":"9","author":"Pei","year":"2010","journal-title":"J. Glob. Position. Syst."},{"key":"ref_7","unstructured":"Priyantha, N.B., Chakraborty, A., and Balakrishnan, H. (August, January 6\u2013). The Cricket Location-Support System. Boston, MA, USA."},{"key":"ref_8","unstructured":"Bahl, P., and Padmanabhan, V.N. (March, January 26\u2013). Radar: An In-Building RF Based User Location and Tracking System. Tel-Aviv, Israel."},{"key":"ref_9","unstructured":"Pei, L., Chen, R., Liu, J., Tenhunen, T., Kuusniemi, H., and Chen, Y. (June, January 13\u2013). Inquiry-Based Bluetooth Indoor Positioning via RSSI Probability Distributions. Athens, Greece."},{"key":"ref_10","unstructured":"Gomes, G., and Sarmento, H. (June, January 18\u2013). Indoor Location System Using ZigBee Technology. Athens\/Glyfada, Greece."},{"key":"ref_11","first-page":"1","article-title":"Bayesian fusion for indoor positioning using bluetooth fingerprints","volume":"67","author":"Chen","year":"2012","journal-title":"Wirel. Pers. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1023\/B:WINE.0000044029.06344.dd","article-title":"Landmarc: Indoor location sensing using active RFID","volume":"10","author":"Ni","year":"2004","journal-title":"Wirel. Netw."},{"key":"ref_13","unstructured":"Pei, L., Chen, R., Liu, J., Chen, W., Kuusniemi, H., Tenhunen, T., Kr\u00f6ger, T., Chen, Y., Lepp\u00e4koski, H., and Takala, J. (September, January 21\u2013). Motion Recognition Assisted Indoor Wireless Navigation on a Mobile Phone. Portland, OR, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14612","DOI":"10.3390\/s121114612","article-title":"iParking: An intelligent indoor location-based smartphone parking service","volume":"12","author":"Liu","year":"2012","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6155","DOI":"10.3390\/s120506155","article-title":"Using LS-SVM based motion recognition for smartphone indoor wireless positioning","volume":"12","author":"Pei","year":"2012","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5081\/jgps.10.1.11","article-title":"Visual-aided Two-dimensional pedestrian indoor navigation with a smartphone","volume":"10","author":"Ruotsalainen","year":"2011","journal-title":"J. Glob. Position. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MPRV.2009.30","article-title":"Indoor positioning and navigation with camera phones","volume":"8","author":"Mulloni","year":"2009","journal-title":"Pervasive Comput."},{"key":"ref_18","unstructured":"IndoorAtlas Ltd. Oulu, Finland. Available online: http:\/\/www.indooratlas.com (accessed on 22 October 2012)."},{"key":"ref_19","unstructured":"Liu, J., Chen, R., Pei, L., Chen, W., Tenhunen, T., Kuusniem, H., Kr\u00f6ger, T., and Chen, Y. (May, January 3\u2013). Accelerometer Assisted Wireless Signals Robust Positioning Based on Hidden Markov Model. Indian Wells, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17208","DOI":"10.3390\/s121217208","article-title":"Hybrid smartphone indoor positioning solution for mobile LBS","volume":"12","author":"Liu","year":"2012","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1049\/iet-rsn.2011.0247","article-title":"Reliability considerations of multi-sensor multi-network pedestrian navigation","volume":"6","author":"Kuusniemi","year":"2012","journal-title":"Radar Sonar Navig IET"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.cviu.2006.10.016","article-title":"Vision-based human motion analysis: An overview","volume":"108","author":"Poppe","year":"2007","journal-title":"Comput. Vis Image Understand"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5715","DOI":"10.3390\/s90705715","article-title":"Adaptive momentum-based motion detection approach and its application on handoff in wireless networks","volume":"9","author":"Chung","year":"2009","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"11556","DOI":"10.3390\/s101211556","article-title":"The use of wearable inertial motion sensors in human lower limb biomechanics studies: A systematic review","volume":"10","author":"Fong","year":"2010","journal-title":"Sensors"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.3390\/s100807772","article-title":"A Review of accelerometry-based wearable motion detectors for physical activity monitoring","volume":"10","author":"Yang","year":"2010","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"638","DOI":"10.3390\/s110100638","article-title":"Sensing movement: Microsensors for body motion measurement","volume":"11","author":"Zeng","year":"2011","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8028","DOI":"10.3390\/s100908028","article-title":"Identifying and tracking pedestrians based on sensor fusion and motion stability predictions","volume":"10","author":"Musleh","year":"2010","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gaitpost.2007.10.010","article-title":"Accelerometry: A technique for quantifying movement patterns during walking","volume":"28","author":"Kavanagh","year":"2008","journal-title":"Gait Posture"},{"key":"ref_29","first-page":"610","article-title":"Accelerometer signal processing for user activity detection","volume":"3215","author":"Baek","year":"2004","journal-title":"Knowl. Based Intell. Inform. Eng. Syst."},{"key":"ref_30","unstructured":"Chen, W., Fu, Z., Chen, R., Chen, Y., Andrei, O., Kr\u00f6ger, T., and Wang, J. (May, January 20\u2013). An Integrated GPS and Multi-Sensor Pedestrian Positioning System for 3D Urban Navigation. Shanghai, China."},{"key":"ref_31","unstructured":"Yang, J. (October, January 19\u2013). Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones. Beijing, China."},{"key":"ref_32","unstructured":"Frank, K., Nadales, M.J.V., Robertson, P., and Angermann, M. (September, January 21\u2013). Reliable Real-Time Recognition of Motion Related Human Activities Using MEMS Inertial Sensors. Portland, OR, USA."},{"key":"ref_33","unstructured":"Susi, M., Borio, D., and Lachapelle, G. (January, January 24\u2013). Accelerometer Signal Features and Classification Algorithms for Positioning Applications. San Diego, CA, USA."},{"key":"ref_34","unstructured":"Pei, L., Chen, R., Liu, J., Kuusniemi, H., Chen, Y., and Tenhunen, T. (September, January 19\u2013). Using Motion-Awareness for the 3D Indoor Personal Navigation on a Smartphone. Portland, OR, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00779-005-0046-3","article-title":"Reality mining: Sensing complex social systems","volume":"10","author":"Eagle","year":"2006","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1413862.1413864","article-title":"Sensing and using social context","volume":"5","author":"Adams","year":"2008","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11036-007-0011-7","article-title":"Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones","volume":"12","author":"Anderson","year":"2007","journal-title":"Mob. Netw. Appl."},{"key":"ref_38","unstructured":"Choudhury, T., and Pentland, A. (, January October). Sensing and Modeling Human Networks Using the Sociometer. Washington, D.C., WA, US."},{"key":"ref_39","first-page":"49","article-title":"Towards activity databases: using sensors and statistical models to summarize people's lives","volume":"29","author":"Choudhury","year":"2006","journal-title":"Data Eng. Bull."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MCOM.2010.5560598","article-title":"A survey of mobile phone sensing","volume":"48","author":"Lane","year":"2010","journal-title":"Commun. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/MPRV.2012.41","article-title":"From smart to cognitive phones","volume":"11","author":"Campbell","year":"2012","journal-title":"Pervasive Comput."},{"key":"ref_42","unstructured":"Pei, L., Chen, R., Liu, J., Liu, Z., Kuusniemi, H., Chen, Y., and Zhu, L. (June, January 24\u2013). Sensor Assisted 3D Personal Navigation on A Smart Phone in GPS Degraded Environments. Shanghai, China."},{"key":"ref_43","unstructured":"Kaplan, E.D., and Hegarty, C.J. (2006). Understanding GPS: Principles and Applications, Artech House Publishers."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"18","DOI":"10.5081\/jgps.7.1.18","article-title":"On outdoor positioning with Wi-Fi","volume":"7","author":"Li","year":"2008","journal-title":"J. Glob. Position Syst."},{"key":"ref_45","unstructured":"Youssef, M., Agrawala, A., and Shankar, A.U. (March, January 23\u2013). WLAN Location Determination via Clustering and Probability Distributions. Fort-Worth, TX, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1016003126882","article-title":"A probabilistic approach to WLAN user location estimation","volume":"9","author":"Roos","year":"2002","journal-title":"Int. J. Wirel. Inform. Netw."},{"key":"ref_47","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","unstructured":"Hall, M.A. (1999). Correlation-Based Feature Selection for Machine Learning. [Ph.D. Thesis, The University of Waikato]."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/34.574797","article-title":"Feature selection: Evaluation, application, and small sample performance","volume":"19","author":"Jain","year":"1997","journal-title":"Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s13389-011-0023-x","article-title":"Machine learning in side-channel analysis: A first study","volume":"1","author":"Hospodar","year":"2011","journal-title":"J. Cryptogr. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.aca.2010.03.030","article-title":"A tutorial on support vector machine-based methods for classification problems in chemometrics","volume":"665","author":"Luts","year":"2010","journal-title":"Anal. Chim. Acta."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neur. Process. Lett."},{"key":"ref_53","unstructured":"Pei, L., Liu, J., Guinness, R., Chen, Y., Kr\u00f6ger, T., Chen, R., and Chen, L. (October, January 2\u2013). The Evaluation of WiFi Positioning in a Bluetooth and WiFi Coexistence Environment. Helsinki, Finland."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/1402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:44:30Z","timestamp":1760219070000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/2\/1402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,1,24]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2013,2]]}},"alternative-id":["s130201402"],"URL":"https:\/\/doi.org\/10.3390\/s130201402","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,1,24]]}}}