Top

Published in:

2021 | OriginalPaper | Chapter

Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark

Authors : Reem Abdel-Salam, Rana Mostafa, Mayada Hadhood

Published in: Deep Learning for Human Activity Recognition

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

next chapter Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks

Recent works are implemented using the same architecture and hyper-parameters as mentioned in their papers and re-evaluated using proposed standardized benchmark.

Abidine, B.M., Fergani, L., Fergani, B., Oussalah, M.: The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition. Pattern Anal. Appl. 21, 119–138 (2016)MathSciNetCrossRef

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

Bevilacqua, A., MacDonald, K., Rangarej, A., Widjaya, V., Caulfield, B., Kechadi, T.: Human activity recognition with convolutional neural networks. In: Brefeld, U., et al. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11053, pp. 541–552. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10997-4_33CrossRef

Bruno, B., Mastrogiovanni, F., Sgorbissa, A.: Wearable inertial sensors: applications, challenges, and public test benches. IEEE Robot. Autom. Mag. 22, 116–124 (2015). https://doi.org/10.1109/MRA.2015.2448279CrossRef

Burns, D.M., Whyne, C.M.: Personalized activity recognition with deep triplet embeddings. arXiv abs/2001.05517 (2020)

Catal, C., Tufekci, S., Pirmit, E., Kocabag, G.: On the use of ensemble of classifiers for accelerometer-based activity recognition. Appl. Soft Comput. 37, 1018–1022 (2015)CrossRef

Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 168–172 (2015)

Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2247–2253 (2007)CrossRef

10.

Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381–388 (2016)

11.

Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 374(2065), 2015020 (2016)

12.

Jordao, A., Nazare, A.C., Sena, J.S.: Human activity recognition based on wearable sensor data: a standardization of the state-of-the-art (2018)

13.

Kasnesis, P., Patrikakis, C.Z., Venieris, I.S.: PerceptionNet: a deep convolutional neural network for late sensor fusion. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 868, pp. 101–119. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01054-6_7CrossRef

14.

Kasteren, T.V., Englebienne, G., Kröse, B.: Human activity recognition from wireless sensor network data: benchmark and software (2011)

15.

Kwapisz, J.R., Weiss, G.M., Moore, S.: Activity recognition using cell phone accelerometers. SIGKDD Explor. 12, 74–82 (2011)CrossRef

16.

Lyu, L., He, X., Law, Y.W., Palaniswami, M.: Privacy-preserving collaborative deep learning with application to human activity recognition. In: CIKM 2017 (2017)

17.

Panwar, M., et al.: CNN based approach for activity recognition using a wrist-worn accelerometer. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2438–2441 (2017)

18.

Saeed, A., Ozcelebi, T., Lukkien, J.: Multi-task self-supervised learning for human activity detection. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 3, 1–30 (2019)CrossRef

19.

Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36 (2004)

20.

Sun, J., Fu, Y., Li, S., He, J., Xu, C., Tan, L.: Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors. J. Sens. 2018, 8580959:1–8580959:10 (2018)

21.

Wang, H., et al.: Wearable sensor-based human activity recognition using hybrid deep learning techniques. Secur. Commun. Netw. 2020, 1–12 (2020). https://doi.org/10.1155/2020/2132138CrossRef

22.

Weiss, G., Lockhart, J.W., Pulickal, T., McHugh, P.T., Ronan, I.H., Timko, J.L.: Actitracker: a smartphone-based activity recognition system for improving health and well-being. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 682–688 (2016)

23.

Xia, K., Huang, J., Wang, H.: LSTM-CNN architecture for human activity recognition. IEEE Access 8, 56855–56866 (2020)CrossRef

24.

Zhang, M., Sawchuk, A.A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: UbiComp 2012 (2012)

Title: Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark
Authors: Reem Abdel-Salam
Rana Mostafa
Mayada Hadhood
Publisher: Springer Singapore
Book: Deep Learning for Human Activity Recognition
Print ISBN: 978-981-16-0574-1

Electronic ISBN: 978-981-16-0575-8

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-16-0575-8_1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner