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A novel orientation- and location-independent activity recognition method


The orientation and location of a mobile phone pose fundamental challenges to activity recognition (AR) in a device. Given that AR significantly affects recognition accuracy, in this study, we focus on eliminating the influence of orientation and location changes on AR. First, we propose an activity recognition framework, which is independent of orientation and location changes, to uniformly deal with the problem of orientation and location changes on AR. Second, a dynamic coordinate transformation approach on inertial sensor data is proposed. In this method, the data collected in different orientations are dynamically mapped to the reference coordinate system of a mobile phone. The classification on the mapped data can reach significantly higher accuracy than that on the original data. We design four sets of comparative experiments to verify the validity of the proposed method, and the results demonstrate its effectiveness. Third, the influence of the location changes of mobile phones on AR is eliminated through the location-specific AR method. The effectiveness of the proposed method is verified by two groups of contrast tests. Finally, a real-time AR system is implemented on an Android platform. Results demonstrate that the proposed method obtains valid recognition results despite various orientation and location changes.

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  1. 1.!AvyWWEA0j-fmgS15vwdSN2ikD5c0.


  1. 1.

    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

  2. 2.

    Reddy S, Mun M, Burke J, Estrin D, Hansen M, Srivastava M (2010) Using mobile phones to determine transportation modes. Acm Trans Sens Netw 6(2):662–701

  3. 3.

    Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. Acm Sigkdd Explor Newsl 12(2):74–82

  4. 4.

    Dernbach S, Das B, Krishnan NC, Thomas BL, Cook DJ (2012) Simple and complex activity recognition through smart phones. In: International conference on intelligent environments, pp 214–221

  5. 5.

    Yan Z, Subbaraju V, Chakraborty D, Misra A, Aberer K (2012) Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In: 2012 16th International symposium on wearable computers. IEEE, pp 17–24

  6. 6.

    bin Abdullah M F A, Negara A F P, Sayeed M S, Choi D-J, Muthu K S (2012) Classification algorithms in human activity recognition using smartphones. Int J Comp Inf Eng 6:77–84

  7. 7.

    Miluzzo E, Lane ND, Fodor K, Peterson R, Lu H, Musolesi M, Eisenman SB, Zheng X, Campbell AT (2008) Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: Proceedings of the 6th ACM conference on Embedded network sensor systems. ACM, pp 337–350

  8. 8.

    Dantzig S, Geleijnse G, Halteren AT (2013) Toward a persuasive mobile application to reduce sedentary behavior. Pers Ubiquitous Comp 17(6):1237–1246

  9. 9.

    Hicks J, Ramanathan N, Kim D, Monibi M, Selsky J, Hansen M, Estrin D (2010) Andwellness: an open mobile system for activity and experience sampling. In: Wireless Health 2010. ACM, pp 34–43

  10. 10.

    Lane ND, Mohammod M, Lin M, Yang X, Lu H, Ali S, Doryab A, Berke E, Choudhury T, Campbell A (2011) Bewell: a smartphone application to monitor, model and promote wellbeing. In: 5th International ICST conference on pervasive computing technologies for healthcare, pp 23–26

  11. 11.

    Albert MV, Toledo S, Shapiro M, Kording K (2012) Using mobile phones for activity recognition in Parkinsons patients. Front Neurol 3:158

  12. 12.

    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209

  13. 13.

    Incel OD, Kose M, Ersoy C (2013) A review and taxonomy of activity recognition on mobile phones. BioNanoSci 3(2):145–171

  14. 14.

    Shoaib M, Bosch S, Incel OD, Scholten H, Havinga PJ (2015) A survey of online activity recognition using mobile phones. Sensors 15(1):2059–2085

  15. 15.

    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R et al (2008) Activity sensing in the wild: a field trial of ubifit garden. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1797–1806

  16. 16.

    Liang Y, Zhou X, Yu Z, Guo B, Yang Y (2012) Energy efficient activity recognition based on low resolution accelerometer in smart phones. In: Advances in grid and pervasive computing. Springer, Berlin, pp 122–136

  17. 17.

    Siirtola P, Roning J (2013) Ready-to-use activity recognition for smartphones. In: IEEE symposium on computational intelligence and data mining (CIDM), 2013. IEEE, pp 59–64

  18. 18.

    Das S, Green L, Perez B, Murphy M, Perring A (2010) Detecting user activities using the accelerometer on android smartphones. The team for research in ubiquitous secure technology, TRUSTREU Carnefie Mellon University, pp 1–10

  19. 19.

    Siirtola P, Röning J (2012) Recognizing human activities user-independently on smartphones based on accelerometer data. Int J Interact Multimed Artif Intell 1:38–45

  20. 20.

    Yang J (2009) Toward physical activity diary: motion recognition using simple acceleration features with mobile phones. In: Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics. ACM, pp 1–10

  21. 21.

    Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM conference on embedded networked sensor systems. ACM, pp 71–84

  22. 22.

    Anjum A, Ilyas MU (2013) Activity recognition using smartphone sensors. In: IEEE consumer communications and networking conference (CCNC), 2013. IEEE, pp 914–919

  23. 23.

    Thiemjarus S, Henpraserttae A, Marukatat S (2013) A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone. In: IEEE international conference on body sensor networks (BSN), 2013. IEEE, pp 1–6

  24. 24.

    Guiry JJ, van de Ven P, Nelson J (2012) Orientation independent human mobility monitoring with an android smartphone. In: Proceeedings of the IASTED international conference on assistive technologies, Innsbruck, Austria, pp 15–17

  25. 25.

    Mizell D (2003) Using gravity to estimate accelerometer orientation. In: Proceedings of 7th IEEE international symposium on wearable computers (ISWC 2003). Citeseer, p 252

  26. 26.

    Kai K, Lukowicz P (2014) Sensor placement variations in wearable activity recognition. IEEE Perv Comput 13(4):32–41

  27. 27.

    Incel O D (2015) Analysis of movement, orientation and rotation-based sensing for phone placement recognition. Sensors 15(10):25 474–25 506

  28. 28.

    Fujinami K (2016) On-body smartphone localization with an accelerometer. Information 7(2):21

  29. 29.

    Antos SA, Albert MV, Kording KP (2013) Hand, belt, pocket or bag: practical activity tracking with mobile phones. J Neurosci Methods 231(11):22–30

  30. 30.

    Mehmood K A, Hameed S M, Seok-Won L (2013) Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones. Sensors 13(10):13 099–122

  31. 31.

    Martn H, Bernardos AM, Iglesias J, Casar JR (2013) Activity logging using lightweight classification techniques in mobile devices. Pers Ubiquitous Comput 17(4):675–695

  32. 32.

    Sztyler T, Stuckenschmidt H (2016) On-body localization of wearable devices: An investigation of position-aware activity recognition. In: 2016 IEEE international conference on pervasive computing and communications (PerCom). IEEE, pp 1–9

  33. 33.

    He Z, Liu Z, Jin L, Zhen L-X, Huang J-C (2008) Weightlessness featurea novel feature for single tri-axial accelerometer based activity recognition. In: IEEE 19th international conference on pattern recognition, 2008. ICPR 2008, pp 1–4

  34. 34.

    Nham B, Siangliulue K, Yeung S (2008) Predicting mode of transport from iphone accelerometer data. Machine Learning Final Projects. Stanford University, California

  35. 35.

    Frank A, Asuncion A et al. (2010) Uci machine learning repository

  36. 36.

    Sun L, Zhang D, Li B, Guo B, Li S (2010) Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. J Ubiquitous Comput Intell 6406:548–562

  37. 37.

    Shi Y, Shi Y, Liu J (2011) A rotation based method for detecting on-body positions of mobile devices. In: International conference on ubiquitous computing, pp 559–560

  38. 38.

    Tsai MC, Chou FC, Kao YF, Yang KC, Chen M (2011) Polite ringer ii: a ringtone interaction system using sensor fusion. In: UBICOMP 2011: ubiquitous computing, international conference, UBICOMP 2011, Beijing, China, September 17–21, 2011, Proceedings, pp 567–568

  39. 39.

    Vahdatpour A, Amini N, Sarrafzadeh M (2011) On-body device localization for health and medical monitoring applications. In: IEEE international conference on pervasive computing and communications, pp 37–44

  40. 40.

    Liu X, Wang L, Zhang J, Yin J, Liu H (2013) Global and local structure preservation for feature selection. IEEE Trans Neural Netw Learn Syst 25(6):1083–1095

  41. 41.

    Liu F, Zhou L, Shen C, Yin J (2014) Multiple kernel learning in the primal for multimodal alzheimers disease classification. IEEE J Biomed Health Inform 18(3):984–990

  42. 42.

    Chen L, Nugent CD, Wang H (2012) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 24(99):1–1

  43. 43.

    Chen L, Nugent C, Okeyo G (2014) An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans Human-Mach Syst 44(1):92–105

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This study was supported by the National Natural Science Foundation of China (No. 91118008). We want to thank all the students who participated in our experiments in the National University of Defense Technology. They provided us many valuable comments and suggestions.

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Correspondence to Dianxi Shi.

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Shi, D., Wang, R., Wu, Y. et al. A novel orientation- and location-independent activity recognition method. Pers Ubiquit Comput 21, 427–441 (2017).

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  • Activity recognition
  • Dynamic coordinates
  • Inertial sensor
  • Orientation-independent
  • Location-independent
  • Transformation