{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:55:54Z","timestamp":1779101754081,"version":"3.51.4"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2011,3,31]],"date-time":"2011-03-31T00:00:00Z","timestamp":1301529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2011,3,31]]},"abstract":"<jats:p>Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily\/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise.<\/jats:p>","DOI":"10.1145\/1964897.1964918","type":"journal-article","created":{"date-parts":[[2011,4,1]],"date-time":"2011-04-01T15:54:25Z","timestamp":1301673265000},"page":"74-82","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2181,"title":["Activity recognition using cell phone accelerometers"],"prefix":"10.1145","volume":"12","author":[{"given":"Jennifer R.","family":"Kwapisz","sequence":"first","affiliation":[{"name":"Fordham University, Bronx, NY"}]},{"given":"Gary M.","family":"Weiss","sequence":"additional","affiliation":[{"name":"Fordham University, Bronx, NY"}]},{"given":"Samuel A.","family":"Moore","sequence":"additional","affiliation":[{"name":"Fordham University, Bronx, NY"}]}],"member":"320","published-online":{"date-parts":[[2011,3,31]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-007-0011-7"},{"key":"e_1_2_1_2_1","unstructured":"Apple iPhone and Apple iPod Touch. 2009. Apple Inc. www.apple.com.  Apple iPhone and Apple iPod Touch. 2009. Apple Inc. www.apple.com."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-02481-8_120"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1515747.1515757"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2008.39"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-008-0112-y"},{"key":"e_1_2_1_8_1","volume-title":"International Joint Conference, 5878--5881","author":"Inooka H."},{"key":"e_1_2_1_9_1","volume-title":"Signals and Information Processing Workshop.","author":"Krishnan N."},{"key":"e_1_2_1_10_1","first-page":"3337","volume-title":"Analysis of Low Resolution Accelerometer Data for Continuous Human Activity Recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP","author":"Krishnan N.","year":"2008"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/11748625_1"},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Mathie M. Celler B. Lovell N. and Coster A. 2004. Classification of basic daily movements using a triaxial accelerometer. In Medical & Biological Engineering and Computing 42.  Mathie M. Celler B. Lovell N. and Coster A. 2004. Classification of basic daily movements using a triaxial accelerometer. In Medical & Biological Engineering and Computing 42.","DOI":"10.1007\/BF02347551"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/BSN.2006.6"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1460412.1460445"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence.","author":"Ravi N.","year":"2005"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2007.4373774"},{"key":"e_1_2_1_17_1","unstructured":"Unwired View.com. 2009. Google wants to make your Android phone much smarter with accelerometer and other sensors. Stasys Bielinis.http:\/\/www.unwiredview.com\/2009\/05\/21\/google-wants-to-make-your-android-phone-muchsmarter-with-accelerometer-and-other-sensors\/  Unwired View.com. 2009. Google wants to make your Android phone much smarter with accelerometer and other sensors. Stasys Bielinis.http:\/\/www.unwiredview.com\/2009\/05\/21\/google-wants-to-make-your-android-phone-muchsmarter-with-accelerometer-and-other-sensors\/"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press","author":"Weiss G. M."},{"key":"e_1_2_1_19_1","unstructured":"WISDM (Wireless Sensor Data Mining) Project. Fordham University Department of Computer and Information Science http:\/\/storm.cis.fordham.edu\/~gweiss\/wisdm\/  WISDM (Wireless Sensor Data Mining) Project. Fordham University Department of Computer and Information Science http:\/\/storm.cis.fordham.edu\/~gweiss\/wisdm\/"},{"key":"e_1_2_1_20_1","volume-title":"Morgan Kaufmann","author":"Witten I. 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