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A Survey on Human Activity Recognition Based on Temporal Signals of Portable Inertial Sensors

  • Reda ElbasionyEmail author
  • Walid Gomaa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

In recent years, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware, which resulted in the tremendous explosion of activity data. On the other hand there are urgent growing and pressing demands from many application areas such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper, we introduce a detailed survey on multiple human activity recognition (HAR) systems which use portable inertial sensors (Accelerometer, Magnetometer, and Gyro), where the sensor’s produced temporal signals are used for modeling and recognition of different human activities based on various machine learning techniques.

Keywords

Human activity recognition Machine learning Inertial measurement unit Accelerometer Gyroscope 

References

  1. 1.
    Ericsson Mobility Report on The Pulse of The Networked Society. Technical report, Ericsson, November 2016. https://www.ericsson.com/assets/local/mobility-report/documents/2016/ericsson-mobility-report-november-2016.pdf
  2. 2.
    Alshurafa, N., Xu, W., Liu, J.J., Huang, M.C., Mortazavi, B., Sarrafzadeh, M., Roberts, C.: Robust human intensity-varying activity recognition using stochastic approximation in wearable sensors. In: IEEE International Conference on Body Sensor Networks (BSN), pp. 1–6. IEEE (2013)Google Scholar
  3. 3.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphone-based activity recognition using fixed-point arithmetic. J. UCS 19(9), 1295–1314 (2013)Google Scholar
  4. 4.
    Arif, M., Bilal, M., Kattan, A., Ahamed, S.I.: Better physical activity classification using smartphone acceleration sensor. J. Med. Syst. 38(9), 95 (2014)CrossRefGoogle Scholar
  5. 5.
    Ashry, S., Elbasiony, R., Gomaa, W.: An lstm-based descriptor for human activities recognition using imu sensors. In: Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, ICINCO, Vol. 1, pp. 494–501. INSTICC, SciTePress (2018)Google Scholar
  6. 6.
    Basterretxea, K., Echanobe, J., del Campo, I.: A wearable human activity recognition system on a chip. In: 2014 Conference on Design and Architectures for Signal and Image Processing (DASIP), pp. 1–8. IEEE (2014)Google Scholar
  7. 7.
    Braojos, R., Beretta, I., Constantin, J., Burg, A., Atienza, D.: A wireless body sensor network for activity monitoring with low transmission overhead. In: 12th IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 265–272. IEEE (2014)Google Scholar
  8. 8.
    Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T., Zaccaria, R.: Analysis of human behavior recognition algorithms based on acceleration data. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1602–1607. IEEE (2013)Google Scholar
  9. 9.
    Chernbumroong, S., Atkins, A.S., Yu, H.: Activity classification using a single wrist-worn accelerometer. In: 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA), pp. 1–6. IEEE (2011)Google Scholar
  10. 10.
    Chuang, F.C., Wang, J.S., Yang, Y.T., Kao, T.P.: A wearable activity sensor system and its physical activity classification scheme. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2012)Google Scholar
  11. 11.
    Chung, W.Y., Purwar, A., Sharma, A.: Frequency domain approach for activity classification using accelerometer. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 1120–1123. IEEE (2008)Google Scholar
  12. 12.
    Cornacchia, M., Ozcan, K., Zheng, Y., Velipasalar, S.: A survey on activity detection and classification using wearable sensors. IEEE Sens. J. 17(2), 386–403 (2017)CrossRefGoogle Scholar
  13. 13.
    Ghasemzadeh, H., Jafari, R.: Physical movement monitoring using body sensor networks: a phonological approach to construct spatial decision trees. IEEE Trans. Industr. Inf. 7(1), 66–77 (2011)CrossRefGoogle Scholar
  14. 14.
    Gjoreski, H., Kozina, S., Gams, M., Lustrek, M.: Rarefall–real-time activity recognition and fall detection system. In: IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 145–147. IEEE (2014)Google Scholar
  15. 15.
    Gomaa, W., Elbasiony, R., Ashry, S.: Adl classification based on autocorrelation function of inertial signals. In: 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 833–837. IEEE (2017)Google Scholar
  16. 16.
    He, Z., Jin, L.: Activity recognition from acceleration data based on discrete consine transform and svm. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 5041–5044. IEEE (2009)Google Scholar
  17. 17.
    He, Z., Liu, Z., Jin, L., Zhen, L.X., Huang, J.C.: Weightlessness feature–a novel feature for single tri-axial accelerometer based activity recognition. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)Google Scholar
  18. 18.
    Huynh, T., Schiele, B.: Towards less supervision in activity recognition from wearable sensors. In: 10th IEEE International Symposium on Wearable Computers, pp. 3–10. IEEE (2006)Google Scholar
  19. 19.
    Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10(1), 156–167 (2006)CrossRefGoogle Scholar
  20. 20.
    Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf. Technol. Biomed. 14(5), 1166–1172 (2010)CrossRefGoogle Scholar
  21. 21.
    Kilinc, O., Dalzell, A., Uluturk, I., Uysal, I.: Inertia based recognition of daily activities with anns and spectrotemporal features. In: IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 733–738. IEEE (2015)Google Scholar
  22. 22.
    Koskimaki, H., Huikari, V., Siirtola, P., Laurinen, P., Roning, J.: Activity recognition using a wrist-worn inertial measurement unit: a case study for industrial assembly lines. In: 17th Mediterranean Conference on Control and Automation, MED 2009, pp. 401–405. IEEE (2009)Google Scholar
  23. 23.
    Krishnan, N.C., Panchanathan, S.: Analysis of low resolution accelerometer data for continuous human activity recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 3337–3340. IEEE (2008)Google Scholar
  24. 24.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newsl. 12(2), 74–82 (2011)CrossRefGoogle Scholar
  25. 25.
    Lara, O.D., Labrador, M.A.: A mobile platform for real-time human activity recognition. In: IEEE Consumer Communications and Networking Conference (CCNC), pp. 667–671. IEEE (2012)Google Scholar
  26. 26.
    Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)CrossRefGoogle Scholar
  27. 27.
    Lee, S.W., Mase, K.: Activity and location recognition using wearable sensors. IEEE Pervasive Comput. 1(3), 24–32 (2002)CrossRefGoogle Scholar
  28. 28.
    Mannini, A., Sabatini, A.M.: On-line classification of human activity and estimation of walk-run speed from acceleration data using support vector machines. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 3302–3305. IEEE (2011)Google Scholar
  29. 29.
    Mantyjarvi, J., Himberg, J., Seppanen, T.: Recognizing human motion with multiple acceleration sensors. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 747–752. IEEE (2001)Google Scholar
  30. 30.
    Margarito, J., Helaoui, R., Bianchi, A.M., Sartor, F., Bonomi, A.G.: User-independent recognition of sports activities from a single wrist-worn accelerometer: a template-matching-based approach. IEEE Trans. Biomed. Eng. 63(4), 788–796 (2016)Google Scholar
  31. 31.
    Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2006, pp. 4–pp. IEEE (2006)Google Scholar
  32. 32.
    Mortazavi, B., Nyamathi, S., Lee, S.I., Wilkerson, T., Ghasemzadeh, H., Sarrafzadeh, M.: Near-realistic mobile exergames with wireless wearable sensors. IEEE J. Biomed. Health Inf. 18(2), 449–456 (2014)CrossRefGoogle Scholar
  33. 33.
    Naranjo-Hernández, D., Roa, L.M., Reina-Tosina, J., Estudillo-Valderrama, M.A.: Som: a smart sensor for human activity monitoring and assisted healthy ageing. IEEE Trans. Biomed. Eng. 59(11), 3177–3184 (2012)CrossRefGoogle Scholar
  34. 34.
    Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(1), 21 (2012)CrossRefGoogle Scholar
  35. 35.
    Poushter, J.: Smartphone ownership and internet usage continues to climb in emerging economies (2016). http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies
  36. 36.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. Aaai 5, 1541–1546 (2005)Google Scholar
  37. 37.
    Song, S.k., Jang, J., Park, S.: A phone for human activity recognition using triaxial acceleration sensor. In: International Conference on Consumer Electronics, ICCE 2008. Digest of Technical Papers, pp. 1–2. IEEE (2008)Google Scholar
  38. 38.
    Trabelsi, D., Mohammed, S., Chamroukhi, F., Oukhellou, L., Amirat, Y.: An unsupervised approach for automatic activity recognition based on hidden markov model regression. IEEE Trans. Autom. Sci. Eng. 10(3), 829–835 (2013)CrossRefGoogle Scholar
  39. 39.
    Weng, S., Xiang, L., Tang, W., Yang, H., Zheng, L., Lu, H., Zheng, H.: A low power and high accuracy mems sensor based activity recognition algorithm. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 33–38. IEEE (2014)Google Scholar
  40. 40.
    Wilson, J., Najjar, N., Hare, J., Gupta, S.: Human activity recognition using lzw-coded probabilistic finite state automata. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3018–3023. IEEE (2015)Google Scholar
  41. 41.
    Xu, H., Liu, J., Hu, H., Zhang, Y.: Wearable sensor-based human activity recognition method with multi-features extracted from hilbert-huang transform. Sensors 16(12), 2048 (2016)CrossRefGoogle Scholar
  42. 42.
    Xu, W., Zhang, M., Sawchuk, A.A., Sarrafzadeh, M.: Co-recognition of human activity and sensor location via compressed sensing in wearable body sensor networks. In: Ninth International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 124–129. IEEE (2012)Google Scholar
  43. 43.
    Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., Aberer, K.: Energy-efficient continuous activity recognition on mobile phones: an activity-adaptive approach. In: 16th International Symposium on Wearable Computers (ISWC), pp. 17–24. IEEE (2012)Google Scholar
  44. 44.
    Ye, L., Ferdinando, H., Seppänen, T., Huuki, T., Alasaarela, E.: An instance-based physical violence detection algorithm for school bullying prevention. In: 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1384–1388. IEEE (2015)Google Scholar
  45. 45.
    Zhang, M., Sawchuk, A.A.: Human daily activity recognition with sparse representation using wearable sensors. IEEE J. Biomed. Health Inf. 17(3), 553–560 (2013)CrossRefGoogle Scholar
  46. 46.
    Zhang, S., McCullagh, P., Nugent, C., Zheng, H.: Activity monitoring using a smart phone’s accelerometer with hierarchical classification. In: 2010 Sixth International Conference on Intelligent Environments (IE), pp. 158–163. IEEE (2010)Google Scholar
  47. 47.
    Zhu, C., Sheng, W.: Human daily activity recognition in robot-assisted living using multi-sensor fusion. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 2154–2159. IEEE (2009)Google Scholar
  48. 48.
    Zhu, C., Sheng, W.: Motion-and location-based online human daily activity recognition. Pervasive Mob. Comput. 7(2), 256–269 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of EngineeringTanta UniversityTantaEgypt
  2. 2.Egypt Japan University of Science and TechnologyAlexandriaEgypt
  3. 3.Faculty of EngineeringAlexandria UniversityAlexandriaEgypt

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