Human activity recognition using smart phone sensors

Ridwan, A. M.; Rahman, Faiyad

dc.contributor.author	Ridwan, A. M.
dc.contributor.author	Rahman, Faiyad
dc.date.accessioned	2021-09-10T03:55:30Z
dc.date.available	2021-09-10T03:55:30Z
dc.date.issued	2013-11-15
dc.identifier.citation	1 Carlos Paniagua*, Huber Flores, Satish Narayana Sriram,"Mobile Sensor Data Classi cation for Human Activity Recognition using MapReduce on Cloud ",The 9th International Conference on Mobile Web Information Systems (MobiWIS 2012) 2 Mitja Lu trek1, Hristijan Gjoreski1, Simon Kozina1, Bo idara Cvetkovi, Violeta Mirchevska, Matja Gams, Jo ef Stefan,"Detecting Falls with Location Sensors and Accelerometers",Proceedings of the Twenty-Third Innovative Applications of Arti cial Intelligence Conference 3 Tanzeem Choudhury,Sunny Consolvo, Beverly Harrison,Je rey Hightower, Anthony LaMarca, Louis LeGrand,Ali Rahimi, and Adam Rea Gaetano Borriello,Bruce Hemingway,Predrag "Pedja" Klasnja, Karl Koscher, James A. Landay,Jonathan Lester, and Danny Wyatt,Dirk Haehnel,"The Mobile Sensing Platform An Embedded Activity Recognition System",Pervasive Computing, IEEE (Volume:7, Issue:2) 4 Robert Bodor, Andrew Drenner, Michael Janssen, Paul Schrater and Nikolaos Papanikolopoulos,"Mobile Camera Positioning to Optimize the Observability of Human Activity Recognition Tasks",Intelligent Robots and Systems,2005. 2005 IEEE/RSJ International conference 5 Eunju Kim, Sumi Helal, and Diane Cook,"Human Activity Recognition and Pattern Discovery",Pervasive Computing, IEEE (Volume:9, Issue:1) 6 Kai-Tai Song and Wei-Jyun Chun, "Human Activity recognition using a mobile camera", The 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 7 O´ scar D. Lara and Miguel A. Labrador,"A Survey on Human Activity Recognition using Wearable Sensors",Communications Surveys & Tutorials, IEEE (Volume:PP, Issue:99) 41 CHAPTER 4. CONCLUSION 42 8 Zhixian Yan, Vigneshwaran Subbaraju, Dipanjan Chakraborty, Archan Misra, Karl Aberer,"Energy-E cient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach",Proceedings of the 16th International Symposium on Wearable Computers (ISWC)	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/936
dc.description	Supervised by Dr. Md. Kamrul Hasan, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT). Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	In this work, algorithms are developed and evaluated to detect physical activities from data acquired using the sensors available in smart phonesaccelerometer, gyroscopes, global positioning system (GPS), carrying it in pockets. Data was collected from 20 subjects without supervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told speci cally where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classi ers using these features were tested. Decision tree classi ers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subjectspeci c training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can e ectively discriminate many activities.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Human activity recognition using smart phone sensors	en_US
dc.type	Thesis	en_US