Personal and Ubiquitous Computing

, Volume 15, Issue 8, pp 887–898 | Cite as

A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation

  • Myong-Woo Lee
  • Adil Mehmood Khan
  • Tae-Seong KimEmail author
Original Article


Recording a personal life log (PLL) of daily activities in a ubiquitous environment is an emerging application of information technology. In this work, we present a single tri-axial accelerometer-based PLL system capable of human activity recognition and exercise information generation. Our PLL system exhibits two main functions: activity recognition and exercise information generation. For activity recognition, the system first recognizes a state of daily activities based on the statistical and spectral features of the accelerometer signals. An activity within the recognized state is then recognized using a set of augmented features, including autoregressive coefficients, signal magnitude area, and tilt angle, via linear discriminant analysis and hierarchical artificial neural networks. Upon the recognition of each activity, the system further estimates exercise information that includes energy expenditure based on metabolic equivalents, stride length, step count, walking distance, and walking speed. Our PLL system operates in real-time, and the life log information it generates is archived in a daily log database. We have validated our PLL system for six daily activities (i.e., lying, standing, walking, going-upstairs, going-downstairs, and driving) via subject-independent and subject-dependent recognition on a total of twenty subjects, achieving an average recognition accuracy of 94.43 and 96.61%, respectively. Our results demonstrate the feasibility of a portable real-time PLL system that could be used for u-lifecare and u-healthcare services in the near future.


Personal life log Activity recognition Exercise information Accelerometer 



This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2011-(C1090-1121-0003).

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0001031).


  1. 1.
    Takata K, Ma J, Apduhan BO, Huang R, Jin Q (2008) Modeling and analyzing individual’s daily activities using lifelog. In: Proceedings of international conference on embedded software and systems, pp 503–510Google Scholar
  2. 2.
    Sellen AJ, Fogg A, Aitken M, Hodges S, Rother C, Wood K (2007) Do life-logging technologies support memory for the past? An experimental study using SenseCam. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2007), ACM press, pp 81–90Google Scholar
  3. 3.
    Mann S (1998) ‘WearCam’ (the wearable camera): personal imaging system for long-term use in wearable tetherless computer-mediated reality and personal photo/video-graphic memory prosthesis. In: Proceedings of international symposium on wearable computers, IEEE, pp 124–131Google Scholar
  4. 4.
    Aizawa K, Ishijima K, Shiina M (2001) Summarizing wearable video. In: Proceedings of international conference on image processing, pp 398–401Google Scholar
  5. 5.
    Krishnan NC, Colbry D, Juillard C, Panchanathan S (2008) Real time human activity recognition using tri-axial accelerometers. In: Sensors, signals and information processing workshopGoogle Scholar
  6. 6.
    Khan AM, Truc PTH, Lee YK, Kim TS (2008) A tri-axial accelerometer sensor-based human activity recognition via augmented signal features and hierarchical recognizer. In: Proceedings of 5th international conference on ubiquitous healthcare, pp 5172–5175Google Scholar
  7. 7.
    DeVaul RW, Dunn S (2001) Real time motion classification for wearable computing applications. Technical report, MIT, media laboratoryGoogle Scholar
  8. 8.
    Veltink PH, Bussmann HBJ, Vries WD et al (1996) Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans Rehabil Eng 4(4):375–385CrossRefGoogle Scholar
  9. 9.
    Montoye H, Washburn R, Servais S, Ertl A, Webster JG, Nagle FJ (1983) Estimation of energy expenditure by a portable accelerometer. Med Sci Sports Exerc 15:403–407Google Scholar
  10. 10.
    Ryu N, Kawahara Y, Asami T (2008) A calorie count application for a mobile phone based on METS value. In: Proceedings of 5th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks, pp 583–584Google Scholar
  11. 11.
    Bouten CV, Westerterp KR, Verduin M, Janssen JD (1994) Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med Sci Sports Exerc 26:1516–1523Google Scholar
  12. 12.
    Lee MW, Khan AM, Kim HJ, Cho YS, Kim TS (2010) A single tri-axial accelerometer-based real-time personal life log system capable of activity classification and exercise information generation. In: Proceedings of 32nd international conference on engineering in medicine and biology society, pp 1390–1393Google Scholar
  13. 13.
    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of 2nd international conference on pervasive computing, pp 1–17Google Scholar
  14. 14.
    Roth K, Kauppinen I, Esquef P, Valimaki V (2003) Frequency warped burg’s method. In: Proceedings of IEEE workshop on applications of signal processing to audio and acoustics, pp 5–8Google Scholar
  15. 15.
    Sung BJ, Yoon JW (2008) Analysis of stride length and the ratio between height and stride length in 10–60 aged men and women. Korean J Walk Sci 8:63–70Google Scholar
  16. 16.
    Kawahara Y, Ryu N, Asami T (2009) Monitoring daily energy expenditure using a 3-axis accelerometer with a low-power microprocessor. Int J Hum-Comput interact 1(5):145–154Google Scholar
  17. 17.
    Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ et al (2000) Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 32(9):S498–S516Google Scholar
  18. 18.
    Laerhoven KV, Cakmakci O (2000) What shall we teach our pants? In: The fourth international symposium on wearable computers, pp 49–56Google Scholar
  19. 19.
    Scott CB, Littlefield ND, Chason JD, Bunker MP, Asselin EM (2006) Differences in oxygen uptake but equivalent energy expenditure between a brief bout of cycling and running. Nutr Metab 3(1):1743–1753CrossRefGoogle Scholar
  20. 20.
    Rose J, Gamble JG (1994) Human walking. Williams and Wilkins, PhiladelphiaGoogle Scholar
  21. 21.
    Hardt M, Kreutz-Delgado K, Helton JW, Stryk OV (1999) Obtaining minimum energy biped walking gaits with symbolic models and numerical optimal control. In: Proceedings of the workshop- biomechanics meets robotics, modeling and simulation of motion, pp 1–19Google Scholar
  22. 22.
    Zarrugh MY, Todd FN, Ralston HJ (1974) Optimization of energy expenditure during level walking. Eur J Appl Physiol 33:293–306CrossRefGoogle Scholar
  23. 23.
    Kim JH, Thang ND, Suh HS, Rasheed T, Kim TS (2009) Forearm motion tracking with estimating joint angles from inertial sensor signals. In: Proceedings of the 2nd international conference on biomedical engineering and informatics, pp 1–4Google Scholar
  24. 24.
    Hori T, Aizawa K (2003) Context-based video retrieval system for the life-log applications. In: Proceedings of ACM workshop on multimedia information retrieval, pp 31–38Google Scholar
  25. 25.
    Abe M, Morinishi Y, Maeda A, Aoki M, Inagaki H (2009) A life log collector integrated with a remote-controller for enabling user centric services. IEEE Trans Consumer Electron 55(1):295–302CrossRefGoogle Scholar
  26. 26.
    Zheng Y, Wang L, Zhang R, Xie X, Andma WY (2008) Geolife: managing and understanding your past life over maps. In: Proceedings of the 9th international conference on mobile data management, IEEE Press, pp 211–212Google Scholar
  27. 27.
    Ushiama T, Watanabe T (2004) A life-log search model based on Bayesian network. In: Proceedings of IEEE 6th international symposium on multimedia software engineering, pp 337–343Google Scholar
  28. 28.
    Lee YS, Cho SB (2009) Exploiting mobile contexts for Petri-net to generate a story in cartoons. Appl Intell 34:1–18MathSciNetCrossRefGoogle Scholar
  29. 29.
    Khan AM, Lee YK, Lee SY, Kim TS (2010) Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: Proceedings of the 5th international conference on future information technology, pp 1–6Google Scholar
  30. 30.
    Kukkonen J, Lagerspetz E, Nurmi P, Andersson M (2009) BeTelGeuse: a platform for gathering and processing situational data. IEEE Pervasive Comput 8(2):49–56CrossRefGoogle Scholar
  31. 31.
    Bouchard K, Ajroud A, Bouchard B, Bouzouane A (2010) SIMACT: a 3D open source smart home simulator for activity recognition. In: Proceedings of the international conference on advances in computer science and information technology, pp 524–533Google Scholar
  32. 32.
    Tapia EM, Intille SS (2006) MITes: MIT environmental sensors hardware and software specifications. Accessed 25 Oct 2010

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Myong-Woo Lee
    • 1
  • Adil Mehmood Khan
    • 2
  • Tae-Seong Kim
    • 1
    Email author
  1. 1.Department of Biomedical EngineeringKyung Hee UniversityYongin, GyeonggiRepublic of Korea
  2. 2.Division of Information and Computer EngineeringAjou UniversitySuwon, GyeonggiRepublic of Korea

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