Top

Published in:

2020 | OriginalPaper | Chapter

Labeling of Activity Recognition Datasets: Detection of Misbehaving Users

Authors : Alessio Vecchio, Giada Anastasi, Davide Coccomini, Stefano Guazzelli, Sara Lotano, Giuliano Zara

Published in: Wireless Mobile Communication and Healthcare

Publisher: Springer International Publishing

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

search-config

AI-assisted search

Off

Abstract

Automatic recognition of user’s activities by means of wearable devices is a key element of many e-health applications, ranging from rehabilitation to monitoring of elderly citizens. Activity recognition methods generally rely on the availability of annotated training sets, where the traces collected using sensors are labelled with the real activity carried out by the user. We propose a method useful to automatically identify misbehaving users, i.e. the users that introduce inaccuracies during the labeling phase. The method is semi-supervised and detects misbehaving users as anomalies with respect to accurate ones. Experimental results show that misbehaving users can be detected with more than 99% accuracy.

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

previous chapter Intelligent Combination of Food Composition Databases and Food Product Databases for Use in Health Applications

next chapter Mobile App for Optimizing Home Care Nursing

Abbate, S., Avvenuti, M., Bonatesta, F., Cola, G., Corsini, P., Vecchio, A.: A smartphone-based fall detection system. Perv. Mobile Comput. 8(6), 883–899 (2012). https://doi.org/10.1016/j.pmcj.2012.08.003, http://www.sciencedirect.com/science/article/pii/S1574119212000983, special Issue on Pervasive HealthcareCrossRef

Bao, Y., Chen, W.: Automatic model construction for activity recognition using wearable devices. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 806–811, March 2018. https://doi.org/10.1109/PERCOMW.2018.8480411

Burns, A., et al.: Shimmer\(^\text{ TM }\) - a wireless sensor platform for noninvasive biomedical research. IEEE Sens. J. 10(9), 1527–1534 (2010). https://doi.org/10.1109/JSEN.2010.2045498CrossRef

Chambers, R., Gabbett, T.J., Cole, M.H., Beard, A.: The use of wearable microsensors to quantify sport-specific movements. Sports Med. 45(7), 1065–1081 (2015). https://doi.org/10.1007/s40279-015-0332-9CrossRef

Cola, G., Avvenuti, M., Vecchio, A., Yang, G., Lo, B.: An unsupervised approach for gait-based authentication. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–6, June 2015. https://doi.org/10.1109/BSN.2015.7299423

Cola, G., Vecchio, A., Avvenuti, M.: Improving the performance of fall detection systems through walk recognition. J. Ambient Intell. Humanized Comput. 5(6), 843–855 (2014). https://doi.org/10.1007/s12652-014-0235-xCrossRef

Aliperti, A., et al.: Using an indoor localization system for activity recognition. In: Sugimoto, C., Farhadi, H., Hämäläinen, M. (eds.) BODYNETS 2018. EICC, pp. 233–243. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29897-5_19CrossRef

Decawave: www.decawave.com. Accessed 15 July 2019

Diete, A., Sztyler, T., Stuckenschmidt, H.: A smart data annotation tool for multi-sensor activity recognition. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 111–116, March 2017. https://doi.org/10.1109/PERCOMW.2017.7917542

10.

Giunchiglia, F., Zeni, M., Bignotti, E., Zhang, W.: Assessing annotation consistency in the wild. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 561–566, March 2018. https://doi.org/10.1109/PERCOMW.2018.8480236

11.

Khan, S.S., Madden, M.G.: One-class classification: taxonomy of study and review of techniques. Knowl. Eng. Rev. 29(3), 345–374 (2014)CrossRef

12.

Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proceedings of The Twenty-First International Conference on Machine Learning, p. 62. ACM (2004)

13.

Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: Lafferty, J.D., Williams, C.K.I., Shawe-Taylor, J., Zemel, R.S., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 1189–1197. Curran Associates, Inc. (2010). http://papers.nips.cc/paper/3923-self-paced-learning-for-latent-variable-models.pdf

14.

Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013). https://doi.org/10.1109/SURV.2012.110112.00192CrossRef

15.

Liu, C., Freeman, W.T., Adelson, E.H., Weiss, Y.: Human-assisted motion annotation. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, June 2008. https://doi.org/10.1109/CVPR.2008.4587845

16.

Martindale, C.F., Roth, N., Hannink, J., Sprager, S., Eskofier, B.M.: Smart annotation tool for multi-sensor gait-based daily activity data. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 549–554, March 2018. https://doi.org/10.1109/PERCOMW.2018.8480193

17.

Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015). https://doi.org/10.1109/JSEN.2014.2370945CrossRefMATH

18.

Palotai, Z., et al.: LabelMovie: semi-supervised machine annotation tool with quality assurance and crowd-sourcing options for videos. In: 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–4, June 2014. https://doi.org/10.1109/CBMI.2014.6849850

19.

Qi, Y., Soh, C.B., Gunawan, E., Low, K.S., Maskooki, A.: A novel approach to joint flexion/extension angles measurement based on wearable UWB radios. IEEE J. Biomed. Health Inform. 18(1), 300–308 (2013)

20.

Qi, Y., Soh, C.B., Gunawan, E., Low, K.S., Maskooki, A.: Using wearable UWB radios to measure foot clearance during walking. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5199–5202. IEEE (2013)

21.

Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1), 23–69 (2003). https://doi.org/10.1023/A:1025667309714CrossRefMATH

22.

Shaban, H.A., El-Nasr, M.A., Buehrer, R.M.: Toward a highly accurate ambulatory system for clinical gait analysis via UWB radios. IEEE Trans. Inf. Technol. Biomed. 14(2), 284–291 (2010). https://doi.org/10.1109/TITB.2009.2037619CrossRef

23.

Szewcyzk, S., Dwan, K., Minor, B., Swedlove, B., Cook, D.: Annotating smart environment sensor data for activity learning. Technol. Health Care 17(3), 161–169 (2009)CrossRef

24.

Tax, D.: Ddtools, the data description toolbox for Matlab, January 2018. Version 2.1.3

25.

Vecchio, A., Cola, G.: Fall detection using ultra-wideband positioning. In: 2016 IEEE SENSORS, pp. 1–3, October 2016. https://doi.org/10.1109/ICSENS.2016.7808527

26.

Vecchio, A., Mulas, F., Cola, G.: Posture recognition using the interdistances between wearable devices. IEEE Sens. Lett. 1(4), 1–4 (2017). https://doi.org/10.1109/LSENS.2017.2726759CrossRef

27.

Yordanova, K., Krüger, F., Kirste, T.: Providing semantic annotation for the CMU grand challenge dataset. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 579–584, March 2018. https://doi.org/10.1109/PERCOMW.2018.8480380

Title: Labeling of Activity Recognition Datasets: Detection of Misbehaving Users
Authors: Alessio Vecchio
Giada Anastasi
Davide Coccomini
Stefano Guazzelli
Sara Lotano
Giuliano Zara
Publisher: Springer International Publishing
Book: Wireless Mobile Communication and Healthcare
Print ISBN: 978-3-030-49288-5

Electronic ISBN: 978-3-030-49289-2

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-49289-2_25

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