DOI: https://doi.org/10.20535/SRIT.2308-8893.2020.2.03

Розпізнавання людської діяльності за допомогою портативних натільних датчиків

Roman V. Kyslyi, Anatolii I. Petrenko

Анотація


Досліджено системи розпізнавання людської діяльності (HAR) за допомогою систематизації типів натільних датчиків для HAR та розглянуто методи збору даних з цих датчиків. Описано модель процесу реалізації HAR та ретельно проаналізовано кожний компонент процесу розпізнавання. Запропоновано методи ідентифікації людської діяльності для різних видів діяльності і визначено їх сильні і слабкі сторони. Виконано порівняльний аналіз цих методів. Процес пошуку тимчасових збігів подано у вигляді діаграми з детальним поясненням кожного переходу. На основі виконаного аналізу запропоновано поєднання як алгоритмів, так і методів, що сприятимуть підвищенню ефективності системи розпізнавання людської діяльності в цілому.

Ключові слова


розпізнавання людської діяльності; класифікація; відстеження; ідентифікація

Повний текст:

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Посилання


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Пристатейна бібліографія ГОСТ


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21. J. Cheng, O. Amft, and P. Lukowicz, “Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition”, in Pervasive Computing, Springer Berlin / Heidelberg, 2010, vol. 6030 of Lecture Notes in Computer Science, pp. 319–336,

22. E.M. Tapia et al., “Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor”, in International Symposium on Wearable Computers, 2007.

23. H. Zhang, “The Optimality of Naive Bayes”, in FLAIRS Conference, AAAI Press, 2004.

24. C. Cortes and V. Vapnik, “Support-vector networks”, Machine Learning, vol. 20, pp. 273–297, 1995.

25. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016. Available: http://www.deeplearningbook.org.

26. Antonio Artés Rodríguez, Human Activity Recognition using Inertial Sensors with Invariance to Sensor Orientation. Available: https://www.researchgate.net/ publication/229597612_Human_Activity_Recognition_using_Inertial_Sensors_with_Invariance_to_Sensor_Orientation.

27. F. Luo, S. Poslad, and E. Bodanese, Temporal convolutional networks for multi-person activity recognition using a 2D LIDAR. Available: https://ieeexplore. ieee.org/document/9051989.