In this paper, the time series dataset, acquired from Wireless Sensor Data Mining Lab (WISDM) Lab, is used to extract features of common human activities from a raw signal data of smartphone accelerometer. A 2D convolutional neural network is used to visualize the data. Keywords: Human Activity Recognition, Stratified-K-fold cross validation, CNN
Raven1233/Human-Activity-Recognition-on-WISDM-Dataset
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