References
- UR Fall Detection Dataset. http://fenix.ur.edu.pl/mkepski/ds/uf.html, 2016.
- G. Beliakov, H. Bustince, and T. Calvo. A practical guide to averaging functions, volume 329 of Studies in Fuzziness and Soft Computing. Springer, 2016.
- A. Bourke and G. Lyons. A threshold-based fall-detection algorithm using a biaxial gyroscope sensor. Medical Engineering and Physics, 30(1):84–90, 2008.
- A. Bourke, P. van de Ven, M. Gamble, R. O’Connor, K. Murphy, E. Bogan, E.and Mc- Quade, P. Finucane, G. Olaighin, and J. Nelson. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. Journal of Biomechanics, 43(15):3051–7, 2010.
- H. Bustince, J. Fernandez, A. Kolesárová, and R. Mesiar. Generation of linear orders for intervals by means of aggregation functions. Fuzzy Sets and Systems, 220:69–77, 2013. Theme: Aggregation functions.
- H. Bustince, M. Galar, B. Bedregal, A. Kolesárová, and R. Mesiar. A new approach to intervalvalued choquet integrals and the problem of ordering in interval-valued fuzzy sets applications. IEEE Transactions on Fuzzy Systems, 21(6):1150–1162, 2013.
- I. Couso and D. Dubois. Statistical reasoning with set-valued information: Ontic vs. epistemic views. International Journal of Approximate Reasoning, 55(7):1502–1518, 2014. Special issue: Harnessing the information contained in low-quality data sources.
- K. Dyczkowski, P. Grochowalski, D. Kosior, D. Gil, W. Kozioł, and B. P˛ekala. IFIS (Interval-Valued Fuzzy Inference System). https://github.com/PGrochowalski/ifis, 2024.
- K. Dyczkowski, P. Grochowalski, D. Kosior, D. Gil, W. Kozioł, B. P˛ekala, U. Kaymak, C. Fuchs, and M. S. Nobile. Python library for interval-valued fuzzy inference. SoftwareX, 26:101730, 2024.
- K. Dyczkowski, B. P˛ekala, J. Szkoła, and A. Wilbik. Federated learning with uncertainty on the example of a medical data. In 2022 IEEE International Conference on Fuzzy Systems (FUZZIEEE), pages 1–8. IEEE, 2022.
- K. Dyczkowski, A. Wójtowicz, P. ˙ Zywica, A. Stachowiak, R. Moszy´nski, and S. Szubert. An Intelligent System for Computer-Aided Ovarian Tumor Diagnosis. In Intelligent Systems’2014, pages 335–343, Cham, 2015. Springer International Publishing.
- M. Gorzałczany. A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems, 21(1):1–17, 1987.
- P. Kairouz, B. McMahan, and et al. Advances and open problems in federated learning. Foundations and Trends ® in Machine Learning, 14:1–210, 2021.
- M. Kepski. Fall Detection and Selected Action Recognition Using Image Sequences. Ph.D. Thesis, AGH University of Science and Technology, Kraków, Poland, 2016.
- M. Komorníková and R. Mesiar. Aggregation functions on bounded partially ordered sets and their classification. Fuzzy Sets and Systems, 175(1):48–56, 2011. Theme: Aggregation Functions, Generalised Measure Theory.
- J. Konečný, H. McMahan, D. Ramage, and P. Richtárik. Federated optimization: Distributed machine learning for on-device intelligence. ArXiv, 1610.02527, 2016.
- J. Konečný, H. McMahan, F. Yu, P. Richtárik, A. Suresh, and D. Bacon. Federated learning: Strategies for improving communication efficiency. ArXiv, 1610.05492, 2017.
- B. Kwolek and M. Kepski. Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer methods and programs in biomedicine, 117(3):489–501, 2014.
- B. Kwolek and M. Kepski. Fuzzy inference-based fall detection using kinect and body-worn accelerometer. Applied Soft Computing, 40:305–318, 2016.
- I. Laktionov, G. Diachenko, D. Rutkowska, and M. Kisiel-Dorohinicki. An explainable ai approach to agrotechnical monitoring and crop diseases prediction in dnipro region of ukraine. Journal of Artificial Intelligence and Soft Computing Research, 13(4):247–272, 2023.
- I. Laktionov, O. Vovna, and M. Kabanets. Information technology for comprehensive monitoring and control of the microclimate in industrial greenhouses based on fuzzy logic. Journal of Artificial Intelligence and Soft Computing Research, 13(1):19–35, 2023.
- T. Li, A. Sahu, A. Talwalkar, and V. Smith. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3):50–60, 2020.
- H. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS 2017, 2017.
- S. Md Salleh, a. h. mohd yusoff, K. ngadimon, and C. Z. Koh. Neural network algorithm-based fall detection modelling. International Journal of Integrated Engineering, 12(3):138–150, Feb. 2020.
- R. Moore. Interval analysis. Prentice Hall, 1966.
- R. Moore. Methods and applications of interval analysis. SIAM, 1979.
- T. Mroczek, D. Gil, and B. Pękala. A hybrid fuzzy-rough approach to handling missing data in a fall detection system. Wojciechowski A.(Ed.), Lipiński P.(Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928., 2023.
- Y. Nizam, M. N. H. Mohd, and M. M. A. Jamil. A study on human fall detection systems: Daily activity classification and sensing techniques. International Journal of Integrated Engineering, 8(1), 2016.
- B. Pe¸kala, T. Mroczek, D. Gil, and M. Kepski. Application of fuzzy and rough logic to posture recognition in fall detection system. Sensors, 22(4):1602, 2022.
- B. Pękala. Uncertainty Data in Interval-Valued Fuzzy Set Theory: Properties, Algorithms and Applications, volume 367 of Studies in Fuzziness and Soft Computing. Springer, 2019.
- A. Piegat and M. Landowski. Multidimensional approach to interval uncertainty calculations. In K. Atanassov and et al., editors, New Trends in Fuzzy Sets, Intuitionistic: Fuzzy Sets, Generalized Nets and Related Topics, Volume II: Applications, page 137–151, Warsaw, 2013. IBS PAN - SRI PAS.
- B. Pękala, A. Wilbik, J. Szkoła, and K. Dyczkowski. Federated learning with uncertainty for unbalanced data using the Choquet integral. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’2024, pages 1–8, 2024.
- R. Sambuc. Fonctions ϕ-floues: Application á l’aide au diagnostic en pathologie thyroidienne. PhD thesis, Faculté de Médecine de Marseille, 1975. (in French).
- E. Stone and M. Skubic. Evaluation of an inexpensive depth camera for passive inhome fall risk assessment. Journal of Ambient Intelligence and Smart Environments, 3(4):349–361, 2011.
- E. Stone and M. Skubic. Unobtrusive, continuous, in-home gait measurement using the microsoft kinect. EEE Transactions on Biomedical Engineering, 60(10):2925–2932, 2013.
- E. Szmidt, J. Kacprzyk, P. Bujnowski, J. T. Star-czewski, and A. Siwocha. Ranking of alternatives described by atanassov’s intuitionistic fuzzy sets – reconciling some misunderstandings. Journal of Artificial Intelligence and Soft Computing Research, 14(3):237–250, 2024.
- T. Theodoridis, V. Solachidis, N. Vretos, and P. Daras. Human fall detection from acceleration measurements using a recurrent neural network. In Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017, Thessaloniki, Greece, 18-21 November 2017, pages 145–149. Springer, 2018.
- I. B. Türksen. Interval valued fuzzy sets based on normal forms. Fuzzy Sets and Systems, 20(2):191–210, 1986.
- A. Wilbik and P. Grefen. Towards a federated fuzzy learning system. pages 1–6. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021.
- A. Wilbik, B. Pękala, K. Dyczkowski, and J. Szkoła. A comparison of client weighting schemes in federated learning. In International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, Springer, pages 116–128, 2022.
- A. Wilbik, B. Pękala, J. Szkoła, and K. Dyczkowski. The Sugeno integral used for federated learning with uncertainty for unbalanced data. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’2023, pages 1–6, 2003.
- Q. Yang, Y. Liu, T. Chen, and Y. Tong. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol., 10(2), 2019.
- S. Yoo and D. Oh. An artificial neural network–based fall detection. International Journal of Engineering Business Management, 10:1847979018787905, 2018.
- L. Zadeh. Fuzzy sets. Information and Control, 8(3):338–353, 1965.
- L. Zadeh. The concept of a linguistic variable and its application to approximate reasoning–i. Information Sciences, 8(3):199–249, 1975.
- H. Zapata, H. Bustince, S. Montes, B. Bedregal, G. Dimuro, Z. Takáč, M. Baczyński, and J. Fernandez. Interval-valued implications and interval-valued strong equality index with admissible orders. International Journal of Approximate Reasoning, 88:91–109, 2017.