Transition-Aware Human Activity Recognition Using Smartphones

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Prototypal Analysis and Prototypal Regression (2017, methodology): “cal and distributional predictors. 5.3 Applications 5.3.1 Smartphone-basedHumanActivities RecognitionData Set The smartphone-based human activities recognition data set from Anguita et al. (2013) and Reyes-Ortiz et al. (2016) contains activity data collected by smartphone’s inertial sensors. In thei”
UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies (2017, background): “A number for human activity recognition datasets are available publicly [76]–[83].”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, neutral): “was the Support Vector Machine (SVM) model [4], [6], [58], [60], [69], [78], [79], [82], [85], [92], [95], [109], [118], [127], [131], [132], [136], [138], [142], [145], [155], [168], [169], [171], [173], [182], [184], [186], [187], [189], [190], [209]–[212] which was used in 35 papers, achieving an”
LSTM-CNN Architecture for Human Activity Recognition (2020, methodology): “The UCI-HAR dataset [30] was built from the recordings of 30 subjects aged 19-48 years.”
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones (2016, neutral): “Thetotalnumber of datasets decreases to 11 because Mobi-Act and UCI HAPT are updated versions of MobiFall and UCI HAR respectively.”