Human Activity Recognition Using Smartphones Data Set

doi:doi:10.57702/khlem0qe

Human Activity Recognition Using Smartphones Data Set

Activity recognition has become a popular research branch in the field of pervasive computing in recent years. A large number of experiments can be obtained that activity sensor-based data’s characteristic in activity recognition is variety, volume, and velocity. Deep learning technology, together with its various models, is one of the most effective ways of working on activity data.

BibTex:

@dataset{Li_Xue_and_Si_Xiandong_and_Nie_Lanshun_and_Li_Jiazhen_and_Ding_Renjie_and_Zhan_Dechen_and_Chu_Dianhui_2024,
    abstract = {Activity recognition has become a popular research branch in the field of pervasive computing in recent years. A large number of experiments can be obtained that activity sensor-based data’s characteristic in activity recognition is variety, volume, and velocity. Deep learning technology, together with its various models, is one of the most effective ways of working on activity data.},
    author = {Li Xue and Si Xiandong and Nie Lanshun and Li Jiazhen and Ding Renjie and Zhan Dechen and Chu Dianhui},
    doi = {10.57702/khlem0qe},
    institution = {No Organization},
    keyword = {'Accelerometer', 'Gyroscope', 'Human Activity Recognition', 'Smartphones', 'accelerometer', 'activity recognition', 'gyroscope', 'smartphones'},
    month = {dec},
    publisher = {TIB},
    title = {Human Activity Recognition Using Smartphones Data Set},
    url = {https://service.tib.eu/ldmservice/dataset/human-activity-recognition-using-smartphones-data-set},
    year = {2024}
}