Skip to main content

The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 419)

Abstract

While the sensor-based recognition of Activities of Daily Living (ADLs) is a well-established research area, few high-quality labeled datasets are available to compare the results of different approaches. This is especially true for multi-inhabitant settings, where multiple residents live in the same home performing both individual and collaborative ADLs. The reference multi-inhabitant datasets consider only environmental sensors data and two residents in the same home. In this paper, we present MARBLE: a novel multi-inhabitant ADLs dataset that combines both smart-watch and environmental sensors data. MARBLE includes sixteen hours of ADLs considering scripted but realistic scenarios where up to four subjects live in the same home environment. Twelve volunteers participated in data collection. We describe MARBLE also providing details on the design of data collection and tools. We also present initial benchmarks of ADLs recognition on MARBLE, obtained by applying state-of-the-art deep learning methods. Our goal is to share the result of a complex and time consuming data acquisition and annotation task, hoping that the challenge of improving the current baselines on MARBLE will contribute to the progress of the research in multi-inhabitant ADLs recognition.

Keywords

  • Activity recognition
  • Smart-home
  • Multi-inhabitant

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-94822-1_25
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-94822-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    The dataset can be downloaded here: tinyurl.com/marbledataset.

  2. 2.

    We used Huawei Sport 2 and other brands with similar features.

  3. 3.

    In our experimental setup, we used machine learning methods to analyse RSSI signal of BLE beacons and WiFi APs in order to classify the semantic location of each subject in real-time.

  4. 4.

    We proposed in [3] a data association method evaluated on MARBLE. However, the dataset was not public yet and it was not described in detail.

  5. 5.

    Since the number of measurements in a window may slightly differ from \(L_w\), we interpolate missing values or downsample measurements when needed.

References

  1. Alemdar, H., Ersoy, C.: Multi-resident activity tracking and recognition in smart environments. J. Ambient. Intell. Humaniz. Comput. 8(4), 513–529 (2017). https://doi.org/10.1007/s12652-016-0440-x

    CrossRef  Google Scholar 

  2. Alemdar, H., Ertan, H., Incel, O.D., Ersoy, C.: ARAS human activity datasets in multiple homes with multiple residents. In: 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, pp. 232–235. IEEE (2013)

    Google Scholar 

  3. Arrotta, L., Bettini, C., Civitarese, G., Presotto, R.: Context-aware data association for multi-inhabitant sensor-based activity recognition. In: 2020 21st IEEE International Conference on Mobile Data Management (MDM), pp. 125–130. IEEE (2020)

    Google Scholar 

  4. Benmansour, A., Bouchachia, A., Feham, M.: Multioccupant activity recognition in pervasive smart home environments. ACM Comput. Surv. (CSUR) 48(3), 34 (2016)

    CrossRef  Google Scholar 

  5. Blunsden, S., Fisher, R.: The behave video dataset: ground truthed video for multi-person behavior classification. Ann. BMVA 4(1–12), 4 (2010)

    Google Scholar 

  6. Calatroni, A., Roggen, D., Tröster, G.: Collection and curation of a large reference dataset for activity recognition. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 30–35. IEEE (2011)

    Google Scholar 

  7. Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012)

    Google Scholar 

  8. Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C.: CASAS: a smart home in a box. Computer 46(7), 62–69 (2012)

    CrossRef  Google Scholar 

  9. Das, B., Cook, D.J., Schmitter-Edgecombe, M., Seelye, A.M.: Puck: an automated prompting system for smart environments: toward achieving automated prompting-challenges involved. Pers. Ubiquit. Comput. 16(7), 859–873 (2012)

    CrossRef  Google Scholar 

  10. Das, S., et al.: Toyota smarthome: real-world activities of daily living. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 833–842 (2019)

    Google Scholar 

  11. van Kasteren, T.L., Englebienne, G., Kröse, B.J.: Human activity recognition from wireless sensor network data: Benchmark and software. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments. Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 165–186. Springer, Heidelberg (2011). https://doi.org/10.2991/978-94-91216-05-3_8

  12. Kong, Q., Wu, Z., Deng, Z., Klinkigt, M., Tong, B., Murakami, T.: MMAct: a large-scale dataset for cross modal human action understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8658–8667 (2019)

    Google Scholar 

  13. Li, Q., Gravina, R., Li, Y., Alsamhi, S.H., Sun, F., Fortino, G.: Multi-user activity recognition: challenges and opportunities. Information Fusion 63, 121–135 (2020)

    CrossRef  Google Scholar 

  14. Lukowicz, P., et al.: Recording a complex, multi modal activity data set for context recognition. In: Proceedings of ARCS 2010 - 23th International Conference on Architecture of Computing Systems, pp. 161–166. VDE Verlag (2010)

    Google Scholar 

  15. Münzner, S., Schmidt, P., Reiss, A., Hanselmann, M., Stiefelhagen, R., Dürichen, R.: CNN-based sensor fusion techniques for multimodal human activity recognition. In: Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 158–165 (2017)

    Google Scholar 

  16. Ordóñez, F., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)

    CrossRef  Google Scholar 

  17. Rai, N., et al.: Home action genome: cooperative compositional action understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11184–11193 (2021)

    Google Scholar 

  18. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Bulgari, V.: From lab to life: fine-grained behavior monitoring in the elderly’s home. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 342–347. IEEE Computer Society, Washington, D.C. (2015)

    Google Scholar 

  19. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Helaoui, R.: SmartFABER: recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment. Artif. Intell. Med. 67, 57–74 (2016)

    CrossRef  Google Scholar 

  20. Riboni, D., Murru, F.: Unsupervised recognition of multi-resident activities in smart-homes. IEEE Access 8, 201985–201994 (2020)

    CrossRef  Google Scholar 

  21. Rossi, S., Capasso, R., Acampora, G., Staffa, M.: A multimodal deep learning network for group activity recognition. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2018)

    Google Scholar 

  22. Roy, N., Misra, A., Cook, D.: Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments. J. Ambient. Intell. Humaniz. Comput. 7(1), 1–19 (2016)

    CrossRef  Google Scholar 

  23. Singla, G., Cook, D.J., Schmitter-Edgecombe, M.: Recognizing independent and joint activities among multiple residents in smart environments. J. Ambient. Intell. Humaniz. Comput. 1(1), 57–63 (2010)

    CrossRef  Google Scholar 

  24. Tran, S.N., et al.: On multi-resident activity recognition in ambient smart-homes. Artif. Intell. Rev. 53(6), 3929–3945 (2019). https://doi.org/10.1007/s10462-019-09783-8

    CrossRef  Google Scholar 

  25. Wang, T., Cook, D.J.: Toward unsupervised multiresident tracking in ambient assisted living: methods and performance metrics. In: Assistive Technology for the Elderly, pp. 249–280. Elsevier (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gabriele Civitarese .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Arrotta, L., Bettini, C., Civitarese, G. (2022). The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94822-1_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94821-4

  • Online ISBN: 978-3-030-94822-1

  • eBook Packages: Computer ScienceComputer Science (R0)