Human activity recognition based on smartphone sensor data using CNN
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
International Society for Photogrammetry and Remote Sensing
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Human activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Açıklama
5th International Conference on Smart City Applications, SCA 2020 -- 7 October 2020 through 8 October 2020 -- Safranbolu -- 167323
Anahtar Kelimeler
CNN, Human activity recognition, Sensing data
Kaynak
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
44
Sayı
4/W3
