Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31261–31280 | Cite as

A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer

  • R. JansiEmail author
  • R. Amutha


Human activity recognition using wearable body sensors plays a vital role in the field of pervasive computing. In this paper, we present human activity recognition framework using compressive classification of data collected from a tri-axial accelerometer sensor. Inspired by the theories of random projection, we propose a novel chaotic map for dimensionality reduction of the accelerometer raw data. This framework also involves extraction of time and frequency domain features from the compressed data. These features are used for human activity recognition using a sparse based classifier. Thus, a simultaneous dimension reduction and classification approach is presented in this paper. We experimentally validate the effectiveness of our proposed framework by recognizing 8 common daily human activities performed by 15 subjects of varying age groups. Our proposed framework achieves superior performance in terms of specificity, precision, F-score and overall accuracy.


Activity recognition Accelerometer Chaotic map Classification Compression 



The authors would like to thank all individuals who extended their support during data collection. We are also pleased to express our immense gratitude towards Dr. S. Radha, Professor and Head of the Department, Electronics and Communication Engineering, SSNCE, for the provision of productive research environment.


  1. 1.
    Ayachi FS, Nguyen HP, de Brugiere EG, Boissy P, Duval C (2016) The use of empirical mode decomposition-based algorithm and inertial measurement units to auto-detect daily living activities of healthy adults. IEEE Trans Neural Syst Rehabil Eng 24(10):1060–1070CrossRefGoogle Scholar
  2. 2.
    Bao L, Intille S (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F (eds) Pervasive computing, Lecture notes in computer science, vol 3001. Springer, Berlin, pp 1–17CrossRefGoogle Scholar
  3. 3.
    Bruckstein AM, Donoho DL, Elad M (2007) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chen C, Jafari R, Kehtarnavaz N (2017) A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools and Applications 76(3):4405–4425CrossRefGoogle Scholar
  5. 5.
    Cornacchia M, Ozcan K, Zheng Y, Velipasalar S (2017) A survey on activity detection and classification using wearable sensors. IEEE Sensors J 17(2):386–403CrossRefGoogle Scholar
  6. 6.
    De Pessemier T, Dooms S, Martens L (2014) Context-aware recommendations through context and activity recognition in a mobile environment. Multimed Tools Appl 72(3):2925–2948CrossRefGoogle Scholar
  7. 7.
    Ding J, Liu JT (2016) Three-layered hierarchical scheme with a Kinect sensor microphone array for audio-based human behavior recognition. Comput Electr Eng 49:173–183CrossRefGoogle Scholar
  8. 8.
    Erden F, Çetin AE (2014) Hand gesture based remote control system using infrared sensors and a camera. IEEE Trans Consum Electron 60(4):675–680CrossRefGoogle Scholar
  9. 9.
    Fahad LG, Rajarajan M (2015) Integration of discriminative and generative models for activity recognition in smart homes. Appl Soft Comput 37:992–1001CrossRefGoogle Scholar
  10. 10.
    Gayathri KS, Easwarakumar KS, Elias S (2017) Probabilistic ontology based activity recognition in smart homes using Markov Logic Network. Knowl-Based Syst 121:173–184CrossRefGoogle Scholar
  11. 11.
    Gibson RM, Amira A, Ramzan N, Casaseca-de-la-Higuera P, Pervez Z (2017) Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device. Biomed Signal Proces 33:96–108CrossRefGoogle Scholar
  12. 12.
    Giovanetti V, Decandia M, Molle G, Acciaro M, Mameli M, Cabiddu A, Cossu R, Serra MG, Manca C, Rassu SP, Dimauro C (2017) Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer. Livest Sci 196:42–48CrossRefGoogle Scholar
  13. 13.
    Guan Q, Li C, Guo X, Wang G (2014) Compressive classification of human motion using pyroelectric infrared sensors. Pattern Recogn Lett 49:231–237CrossRefGoogle Scholar
  14. 14.
    Guo P, Miao Z, Shen Y, Xu W, Zhang D (2014) Continuous human action recognition in real time. Multimed Tools Appl 68(3):827–844CrossRefGoogle Scholar
  15. 15.
    Ignatov AD, Strijov VV (2016) Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer. Multimedia tools and applications 75(12):7257–7270CrossRefGoogle Scholar
  16. 16.
    Ijjina EP, Chalavadi KM (2016) Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recogn 59:199–212CrossRefGoogle Scholar
  17. 17.
    Khan AM, Lee YK, Lee SY, Kim TS (2010) A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans Inf Technol Biomed 14(5):1166–1172CrossRefGoogle Scholar
  18. 18.
    Khan A, Hammerla N, Mellor S, Plötz T (2016) Optimising sampling rates for accelerometer-based human activity recognition. Pattern Recogn Lett 73:33–40CrossRefGoogle Scholar
  19. 19.
    Kumari P, Mathew L, Syal P (2017) Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens Bioelectron 90:298–307CrossRefGoogle Scholar
  20. 20.
    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tut 15(3):1192–1209CrossRefGoogle Scholar
  21. 21.
    Lee JS, Choi S, Kwon O (2017) Identifying multiuser activity with overlapping acoustic data for mobile decision making in smart home environments. Expert Syst Appl 81:299–308CrossRefGoogle Scholar
  22. 22.
    Liu X, Mei W, Du H (2016) Simultaneous image compression, fusion and encryption algorithm based on compressive sensing and chaos. Opt Commun 366:22–32CrossRefGoogle Scholar
  23. 23.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  24. 24.
    Machado IP, Gomes AL, Gamboa H, Paixão V, Costa RM (2015) Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization. Inf Process Manag 51(2):204–214CrossRefGoogle Scholar
  25. 25.
    May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261:459–465CrossRefGoogle Scholar
  26. 26.
    Mukhopadhyay SC (2015) Wearable sensors for human activity monitoring: A review. IEEE Sensors J 15(3):1321–1330CrossRefGoogle Scholar
  27. 27.
    Pincus S (1995) Approximate entropy (ApEn) as a complexity measure. Chaos 5(1):110–117MathSciNetCrossRefGoogle Scholar
  28. 28.
    Preece SJ, Goulermas JY, Kenney LP, Howard D (2009) A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng 56(3):871–879CrossRefGoogle Scholar
  29. 29.
    Rashidi P, Mihailidis A (2013) A survey on ambient-assisted living tools for older adults. IEEE journal of biomedical and health informatics 17(3):579–590CrossRefGoogle Scholar
  30. 30.
    Rodgers MM, Pai VM, Conroy RS (2015) Recent advances in wearable sensors for health monitoring. IEEE Sensors J 15(6):3119–3126CrossRefGoogle Scholar
  31. 31.
    Sprott J (2003) Chaos and time series analysis. Oxford University Press, OxfordzbMATHGoogle Scholar
  32. 32.
    Wang Z, Wu D, Chen J, Ghoneim A, Hossain MA (2016) A triaxial accelerometer-based human activity recognition via EEMD-based features and game-theory-based feature selection. IEEE Sensors J 16(9):3198–3207CrossRefGoogle Scholar
  33. 33.
    Xiao Y, Xia L (2016) Human action recognition using modified slow feature analysis and multiple kernel learning. Multimed Tools Appl 75(21):13041–13056CrossRefGoogle Scholar
  34. 34.
    Xiao L, Li R, Luo J, Xiao Z (2016) Energy-efficient recognition of human activity in body sensor networks via compressed classification. Int J Distrib Sens N 12(12):1–8Google Scholar
  35. 35.
    Xu H, Liu J, Hu H, Zhang Y (2016) Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform. Sensors 16(12):2048CrossRefGoogle Scholar
  36. 36.
    Yang CC, Hsu YL (2010) A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8):7772–7788CrossRefGoogle Scholar
  37. 37.
    Zhang M, Sawchuk AA (2011) A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International Conference on Body Area Networks, pp 92-98Google Scholar
  38. 38.
    Zhang M, Sawchuk AA (2013) Human daily activity recognition with sparse representation using wearable sensors. IEEE journal of Biomedical and Health Informatics 17(3):553–560CrossRefGoogle Scholar
  39. 39.
    Zhang K, Zhang L (2017) Extracting hierarchical spatial and temporal features for human action recognition. Multimedia Tools and Applications:1–6Google Scholar
  40. 40.
    Zhou N, Pan S, Cheng S, Zhou Z (2016) Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing. Opt Laser Technol 82:121–133CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSSN College of EngineeringChennaiIndia

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