Skip to main content
Springer Nature Link
Log in

Novel tent pooling based human activity recognition approach

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Nowadays, mobile devices have been widely used worldwide and they have variable sensors. By using these sensors, many applications for instance navigation, healthcare, and human activity recognition (HAR) have been developed. The primary objective of this paper is to present a multi lightweight HAR method with high prediction performance. To implement this objective, novel pooling methodology is presented and it is called as tent pooling since pooling is processed using the variable size of blocks. 2 × 2, 3 × 3, 4 × 4, 5 × 5, 6 × 6, 7 × 7 and 8 × 8 sized non-overlapping blocks are used for pooling and maximum, minimum and average pooling methods. These pooling methods have been utilized as a feature generation model. The generated features are forwarded to the ReliefF feature selection method. ReliefF is a weight-based feature selector and uses Manhattan distance based fitness function. By using relief, the weights of the features are calculated and the negative weighted features are eliminated. This operation is named positive weighted feature selection. The selected positive weighted features are classified using a cubic support vector machine (SVM). To test the proposed method, a publicly and freely published HAR dataset is used. There are 6 activities in the used dataset. Also, the results of the proposed method were compared to other state of art methods. The best accuracy rate of the proposed tent pooling based HAR method was calculated as 99.81% using the average pooling. This results clearly prove that the success of the proposed tent pooling based method. The proposed method is also simple and effective. It has the highest success rates among the selected state-art-of HAR methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Abdelnasser H, Youssef M, Harras KA (2015). WiGest: a ubiquitous WiFi-based gesture recognition system. In: Proceedings - IEEE INFOCOM

  2. Abowd D, Dey AK, Orr R, Brotherton J (1998) Context-awareness in wearable and ubiquitous computing. Virtual Reality 3:200–211. https://doi.org/10.1007/BF01408562

    Article  Google Scholar 

  3. Aggarwal JK, Ryoo MS (2011) Human activity analysis. ACM Comput Surv 43:1–43. https://doi.org/10.1145/1922649.1922653

    Article  Google Scholar 

  4. Alaa M, Zaidan AA, Zaidan BB, Talal M, Kiah MLM (2017) A review of smart home applications based on internet of things. J Netw Comput Appl 97:48–65

    Article  Google Scholar 

  5. Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz,JL (2013) A public domain dataset for human activity recognition using smartphones. In: ESANN 2013, Vol. 3, p. 3

  6. Anguita D, Ghio A, Oneto L, et al (2012). Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Pp 216–223

  7. Anguita D, Ghio A, Oneto L et al (2013) Energy efficient smartphone-based activity recognition using fixed-point arithmetic. Special Session in Ambient Assisted Living: Home Care J Univers Comput Sci 19:1295–1314

    Google Scholar 

  8. Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y (2015) Physical human activity recognition using wearable sensors. Sensors (Switzerland) 15:31314–31338

    Article  Google Scholar 

  9. Banno W, Shinomiya N (2019). Monitoring system for the elderly on staircase using passive RFID sensor tags. In: 2019 IEEE 8th global conference on consumer electronics, GCCE 2019

  10. Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: International conference on pervasive computing (pp. 1-17). Springer, Berlin, Heidelberg.

  11. BENAISSA B, KÖPPEN M, YOSHIDA K (2017) Activity and emotion recognition for elderly health monitoring. Int J Affect Eng 17:81–88. https://doi.org/10.5057/ijae.ijae-d-17-00020

    Article  Google Scholar 

  12. Chen Y, Shen C (2017) Performance analysis of smartphone-sensor behavior for human activity recognition. IEEE Access 5:3095–3110. https://doi.org/10.1109/ACCESS.2017.2676168

    Article  Google Scholar 

  13. Cheng L, You C, Guan Y, Yu Y (2018) Body activity recognition using wearable sensors. Proceedings of Computing Conference 2017:756–765

    Google Scholar 

  14. Cho H, Yoon SM (2018). Divide and conquer-based 1D CNN human activity recognition using test data sharpening. Sensors (Switzerland) 18:. https://doi.org/10.3390/s18041055

  15. Chua SL, Marsland S, Guesgen HW (2009). Behaviour recognition from sensory streams in smart environments. In: lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)

  16. Cornacchia M, Ozcan K, Zheng Y, Velipasalar S (2017) A survey on activity detection and classification using wearable sensors. IEEE Sensors J 17:386–403. https://doi.org/10.1109/JSEN.2016.2628346

    Article  Google Scholar 

  17. Ehatisham-ul-Haq M, Awais Azam M, Naeem U, Amin Y, Loo J (2018) Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing. J Netw Comput Appl 109:24–35. https://doi.org/10.1016/j.jnca.2018.02.020

    Article  Google Scholar 

  18. Fu B, Damer N, Kirchbuchner F, Kuijper A (2020) Sensing technology for human activity recognition: a comprehensive survey. IEEE Access 8:83791–83820. https://doi.org/10.1109/ACCESS.2020.2991891

    Article  Google Scholar 

  19. Giansanti D, Macellari V, Maccioni G (2008). New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device Physiol Meas 29:. https://doi.org/10.1088/0967-3334/29/3/N0129, N19

  20. Hassan MM, Uddin MZ, Mohamed A, Almogren A (2018) A robust human activity recognition system using smartphone sensors and deep learning. Futur Gener Comput Syst 81:307–313. https://doi.org/10.1016/j.future.2017.11.029

    Article  Google Scholar 

  21. Hussain Z, Sheng QZ, Zhang WE (2020). A review and categorization of techniques on device-free human activity recognition. J. Netw. Comput. Appl.

  22. Ignatov A (2018) Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl Soft Comput J 62:915–922. https://doi.org/10.1016/j.asoc.2017.09.027

    Article  Google Scholar 

  23. Jiang W, Yin Z (2015). Human activity recognition using wearable sensors by deep convolutional neural networks. In: proceedings of the 23rd ACM international conference on multimedia - MM ‘15. Pp 1307–1310

  24. Kästner M, Strickert M, Villmann T (2013). A sparse Kernelized matrix learning vector quantization model for human activity recognition. In: ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 24–26

  25. 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 14:1166–1172. https://doi.org/10.1109/TITB.2010.2051955

    Article  Google Scholar 

  26. Kira K, Rendell LA (2014) A practical approach to feature selection. Machine Learning Proceedings 1992:249–256

    Google Scholar 

  27. Lara ÓD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutorials 15:1192–1209. https://doi.org/10.1109/SURV.2012.110112.00192

    Article  Google Scholar 

  28. Li L, Bai R, Xie B, Peng Y, Wang A, Wang W, Jiang B, Liang J, Chen X (2018) R&P: an low-cost device-free activity recognition for E-health. IEEE Access 6:81–90. https://doi.org/10.1109/ACCESS.2017.2749323

    Article  Google Scholar 

  29. Liu J, Chen X, Chen S, et al (2019). TagSheet: sleeping posture recognition with an unobtrusive passive tag matrix. In: Proceedings - IEEE INFOCOM

  30. Lu Y, Zhang C, Zhou BY, Gao XP, Lv Z (2018) A dual model approach to EOG-based human activity recognition. Biomed Signal Process Control 45:50–57. https://doi.org/10.1016/j.bspc.2018.05.011

    Article  Google Scholar 

  31. Mannini A, Sabatini AM (2010) Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10:1154–1175. https://doi.org/10.3390/s100201154

    Article  Google Scholar 

  32. Maurer U, Smailagic A, Siewiorek DP, Deisher M (2006). Activity recognition and monitoring using multiple sensors on different body positions. In: proceedings - BSN 2006: international workshop on wearable and implantable body sensor networks

  33. Mohamed R, Zainudin MNS, Sulaiman MN et al (2018) Multi-label classification for physical activity recognition from various accelerometer sensor positions. J Inf Commun Technol 17:209–231

    Google Scholar 

  34. Narayanan MR, Lord SR, Budge MM, et al (2007). Falls management: detection and prevention, using a waist-mounted triaxial accelerometer. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. pp. 4037–4040

  35. Narayanan MR, Redmond SJ, Scalzi ME, Lord SR, Celler BG, Lovell NH (2010) Longitudinal falls-risk estimation using triaxial accelerometry. IEEE Trans Biomed Eng 57:534–541. https://doi.org/10.1109/TBME.2009.2033038

    Article  Google Scholar 

  36. Narayanan MR, Scalzi ME, Redmond SJ, Lord SR, Celler BG, Lovell NH (2008) A wearable triaxial accelerometry system for longitudinal assessment of falls risk. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2840-2843

  37. Nguyen Gia T, Sarker VK, Tcarenko I, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2018) Energy efficient wearable sensor node for IoT-based fall detection systems. Microprocess Microsyst 56:34–46. https://doi.org/10.1016/j.micpro.2017.10.014

    Article  Google Scholar 

  38. Noury N, Fleury A, Rumeau P, et al (2007). Fall detection - principles and methods. In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

  39. Pu Q, Gupta S, Gollakota S, Patel S (2013). Whole-home gesture recognition using wireless signals. In: Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

  40. Reiss A, Hendeby G, Stricker D (2013). A competitive approach for human activity recognition on smartphones. ESANN 2013

  41. Reyes-Ortiz JL, Ghio A, Anguita D, et al (2013). Human activity and motion disorder recognition: towards smarter interactive cognitive environments. Eur Symp Artif Neural Networks, Comput Intell Mach Learn ESANN 24–26

  42. Romera-Paredes AMS, Bianchi-Berthouze N (2013) A one-vs-one classifier ensemble with majority voting for activity recognition. Esann 2013:24–26

    Google Scholar 

  43. Ronao CA, Cho SB (2016) Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst Appl 59:235–244. https://doi.org/10.1016/j.eswa.2016.04.032

    Article  Google Scholar 

  44. San-Segundo R, Lorenzo-Trueba J, Martínez-González B, Pardo JM (2016) Segmenting human activities based on HMMs using smartphone inertial sensors. Pervasive Mob Comput 30:84–96. https://doi.org/10.1016/j.pmcj.2016.01.004

    Article  Google Scholar 

  45. Shen J, Tao D, Li X (2008) Modality mixture projections for semantic video event detection. IEEE Trans Circuits Syst Video Technol 18:1587–1596. https://doi.org/10.1109/TCSVT.2008.2005607

    Article  Google Scholar 

  46. Shen J, Wang M, Chua TS (2016) Accurate online video tagging via probabilistic hybrid modeling. Multimedia Systems 22:99–113. https://doi.org/10.1007/s00530-014-0399-4

    Article  Google Scholar 

  47. Subasi A, Fllatah A, Alzobidi K, Brahimi T, Sarirete A (2019) Smartphone-based human activity recognition using bagging and boosting. Procedia Comput Sci 163:54–61. https://doi.org/10.1016/j.procs.2019.12.086

    Article  Google Scholar 

  48. Tolstikov A, Hong X, Biswas J, Nugent C, Chen L, Parente G (2011) Comparison of fusion methods based on DST and DBN in human activity recognition. J Control Theory Appl 9:18–27. https://doi.org/10.1007/s11768-011-0260-7

    Article  Google Scholar 

  49. Torres-Huitzil C, Alvarez-Landero A (2015) Accelerometer-based human activity recognition in smartphones for healthcare services. In Mobile Health (pp. 147-169). Springer, Cham.

  50. Tröster G, Amft O (2008) Recognition of dietary activity events using on-body sensors. Artif Intell Med 42:121–136. https://doi.org/10.1016/j.artmed.2007.11.007

    Article  Google Scholar 

  51. Urbanowicz RJ, Meeker M, La Cava W et al (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189–203

    Article  Google Scholar 

  52. Vrigkas M, Nikou C, Kakadiaris IA (2015). A review of human activity recognition methods. Front Robot AI 2:. https://doi.org/10.3389/frobt.2015.00028

  53. Wang Y, Cang S, Yu H (2019) A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst Appl 137:167–190. https://doi.org/10.1016/j.eswa.2019.04.057

    Article  Google Scholar 

  54. Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recogn Lett 119:3–11. https://doi.org/10.1016/j.patrec.2018.02.010

    Article  Google Scholar 

  55. Wang W, Liu AX, Shahzad M (2016). Gait recognition using WiFi signals. In: UbiComp 2016 - proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing

  56. Wang X, Lu Y, Wang D, Liu L, Zhou H (2017) Using jaccard distance measure for unsupervised activity recognition with smartphone accelerometers. In Asia-pacific web (apweb) and web-age information management (waim) joint conference on web and big data (pp. 74-83). Springer, Cham.

  57. Wickramasinghe A, Shinmoto Torres RL, Ranasinghe DC (2017) Recognition of falls using dense sensing in an ambient assisted living environment. Pervasive Mob Comput 34:14–24. https://doi.org/10.1016/j.pmcj.2016.06.004

    Article  Google Scholar 

  58. Xiao F, Miao Q, Xie X, Sun L, Wang R (2018) SHMO: a seniors health monitoring system based on energy-free sensing. Comput Netw 132:108–117. https://doi.org/10.1016/j.comnet.2018.01.003

    Article  Google Scholar 

  59. Yang J, Lee J, Choi J (2011) Activity recognition based on RFID object usage for smart mobile devices. J Comput Sci Technol 26:239–246. https://doi.org/10.1007/s11390-011-9430-9

    Article  Google Scholar 

  60. Yao L, Sheng QZ, Li X, Gu T, Tan M, Wang X, Wang S, Ruan W (2018) Compressive representation for device-free activity recognition with passive RFID signal strength. IEEE Trans Mob Comput 17:293–306. https://doi.org/10.1109/TMC.2017.2706282

    Article  Google Scholar 

  61. Yu Y, Wang D, Zhao R, Zhang Q (2019). RFID based real-time recognition of ongoing gesture with adversarial learning. In: SenSys 2019 - proceedings of the 17th conference on embedded networked sensor systems

  62. Zeng Y, Pathak PH, Mohapatra P (2015). Analyzing shopper’s behavior through WiFi signals. In: WPA 2015 - proceedings of the 2nd workshop on physical analytics

  63. Zhou Z, Shangguan L, Zheng X, Yang L, Liu Y (2017) Design and implementation of an RFID-based customer shopping behavior mining system. IEEE/ACM Trans Netw 25:2405–2418. https://doi.org/10.1109/TNET.2017.2689063

    Article  Google Scholar 

  64. Zolfaghari S, Keyvanpour MR (2016). SARF: smart activity recognition framework in ambient assisted living. In: proceedings of the 2016 federated conference on computer science and information systems. Pp 1435–1443

Download references

Author information

Authors and Affiliations

  1. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

    Türker Tuncer & Fatih Ertam

Authors
  1. Türker Tuncer
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fatih Ertam
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Türker Tuncer.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tuncer, T., Ertam, F. Novel tent pooling based human activity recognition approach. Multimed Tools Appl 80, 4639–4653 (2021). https://doi.org/10.1007/s11042-020-09893-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09893-4

Keywords