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PerMML: A Performance Metric for Multi-layer Dataset

Published: 24 September 2021 Publication History
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References

[1]
M.A.R Ahad. 2020. Vision and Sensor Based Human Activity Recognition: Challenges Ahead. In Advancements in Instrumentation and Control in Applied System Applications, Srijan Bhattacharya (Ed.). Hershey, PA: IGI Global, Oxford, Chapter 2, 17–35.
[2]
Sun Aixin and Lim Ee-Peng. 2001. Hierarchical Text Classification and Evaluation. In Proceedings of 2001 IEEE International Conference on Data Mining. IEEE.
[3]
Sayeda Shamma Alia, Paula Lago, Kohei Adachi, Tahera Hossain, Hiroki Goto, Tsuyoshi Okita, and Sozo Inoue. 2020. Summary of the 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (Virtual Event, Mexico) (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 378–383. https://doi.org/10.1145/3410530.3414611
[4]
Sayeda Shamma Alia, Paula Lago, and Sozo Inoue. 2020. MCoMat: A New Performance Metric for Imbalanced Multi-Layer Activity Recognition Dataset. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (Virtual Event, Mexico) (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 232–237. https://doi.org/10.1145/3410530.3414364
[5]
Osmani Aomar, Hamidi Massinissa, and Alizadeh Pegah. 2021. Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition. In arXiv preprint. arXiv:2104.04889
[6]
Silla Jr. Carlos N. and Freitas Alex A.2010. A survey of hierarchical classification across different application domains. (2010).
[7]
Diane Cook, Kyle Feuz, and Narayanan Krishnan. 2013. Transfer Learning for Activity Recognition: A Survey. Knowledge and information systems 36 (09 2013), 537–556. https://doi.org/10.1007/s10115-013-0665-3
[8]
Chunhui Gu, Chen Sun, David A. Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, Cordelia Schmid, and Jitendra Malik. 2018. AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions. In Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 6047–6056 pages.
[9]
Rim Helaoui, Daniele Riboni, Mathias Niepert, Claudio Bettini, and Heiner Stuckenschmidt. 2012. Towards activity recognition using probabilistic description logics. Technical Report.
[10]
Rim Helaoui, Daniele Riboni, and Heiner Stuckenschmidt. 2013. A Probabilistic Ontological Framework for the Recognition of Multilevel Human Activities. In Proceedings of the ACM international joint conference on Pervasive and ubiquitous computings. ACM, Switzerland, 345–354 pages.
[11]
Tahera Hossain, Md Shafiqul Islam, Md Atiqur Rahman Ahad, and Sozo Inoue. 2019. Human Activity Recognition Using Earable Device. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers(London, United Kingdom) (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 81–84. https://doi.org/10.1145/3341162.3343822
[12]
Sozo Inoue, Paula Lago, Tahera Hossain, Tittaya Mairittha, and Nattaya Mairittha. 2019. Integrating Activity Recognition and Nursing Care Records: The System, Deployment, and a Verification Study. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 86 (Sept. 2019), 24 pages. https://doi.org/10.1145/3351244
[13]
Sang-Hack Jung, Yanlin Guo, Harpreet S Sawhney, and Rankesh Kumar. 2008. Action Video Retrieval based on Atomic Action Vocabulary. In Proceedings of the ACM international conference on Multimedia information retrieval (MIR). ACM, 245–-252 pages.
[14]
Paula Lago, Claudia Roncancio, and Claudia Jiménez-Guarín. 2018. Learning and managing context enriched behavior patterns in smart homes. Future Generation Computer Systems 91 (09 2018). https://doi.org/10.1016/j.future.2018.09.004
[15]
Young-Seol Lee and Sung-Bae Cho. 2011. Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer. In Proceedings of the TInternational Conference on Hybrid Artificial Intelligence Systems. Springer, 460–467 pages.
[16]
Li Liu, Li Cheng, Ye Liu, Yongpo Jia, and David S. Rosenblum. 2016. Recognizing Complex Activities by a Probabilistic Interval-Based Model. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI, USA, 1266–-1272 pages.
[17]
Rohrbach Marcus, Rohrbach Anna, Regneri Michaela, Amin Sikandar, Andriluka Mykhaylo, Pinkal Manfred, and Schiele Bernt. 2016. Recognizing Fine-Grained and Composite Activities Using Hand-Centric Features and Script Data. International Journal of Computer Vision(2016), 346––373.
[18]
Fazli Mehrdad, Kowsari Kamran, Gharavi Erfaneh, Barnes Laura, and Doryab Afsaneh. 2020. HHAR-net: Hierarchical Human Activity Recognition using Neural Networks. In arXiv preprint. arXiv:2010.16052
[19]
Cesa-Bianchi Nicolo, Gentile Claudio, and Zaniboni Luca. 2006. Incremental algorithms for hierarchical classification. The Journal of Machine Learning (January 2006), 31–54.
[20]
D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Förster, G. Tröster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha, J. Doppler, C. Holzmann, M. Kurz, G. Holl, R. Chavarriaga, H. Sagha, H. Bayati, M. Creatura, and J. d. R. Millàn. 2010. Collecting complex activity datasets in highly rich networked sensor environments. In 2010 Seventh International Conference on Networked Sensing Systems (INSS). 233–240.
[21]
Saguna Saguna, Arkady Zaslavsky, and Dipanjan Chakraborty. 2013. Complex activity recognition using context-driven activity theory and activity signatures. ACM Transactions on Computer-Human Interaction 20 (2013). Issue 6.
[22]
Chen Sun and Ram Nevatia. 2013. ACTIVE: Activity Concept Transitions in Video Event Classification. In Proceedings of The IEEE International Conference on Computer Vision (ICCV). IEEE, 913–920 pages.
[23]
Zheng Yuhuang. 2015. Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework. Journal of Electrical and Computer Engineering 2015 (July 2015).

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cover image ACM Conferences
UbiComp/ISWC '21 Adjunct: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
September 2021
711 pages
ISBN:9781450384612
DOI:10.1145/3460418
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Published: 24 September 2021

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  1. Hierarchical classification
  2. Multi-layer dataset
  3. Performance metric

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