Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

Abstract

Human activity recognition has been applied in various areas of life by utilizing the gyroscope and accelerometer sensors embedded in smartphones. One of the functions of recognizing human activities is by understanding the pattern of human activity, thereby minimizing the possibility of unexpected incidents. This study classified of human activity recognition through CNN-LSTM on the UCI HAR dataset by applying the divide and conquer algorithm. This study additionally employs tuning hyperparameter to obtain the best accuracy value from the parameters and the proposed architecture. From the test results with the CNN-LSTM method, the accuracy rate for dynamic activity is 99.35%, for static activity is 96.08%, and the combination of the two models is 97.62%.