KLASIFIKASI STATIC DAN DYNAMIC ACTIVITY PADA HUMAN ACTIVITY RECOGNITION DATASET MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. For recognizing the activity, the accelerometer was popular sensors. As well as a gyroscope can be embedded in a smartphone. Signal was generated from the accelerometer as a time-series data is an actual approach like a human activity pattern. Motion data have acquired in an of 30 volunteers. Dynamic Actives (Walking, Walking Upstairs, Walking Downstairs) as DA and Static Actives(Laying, Standing, Sitting) as SA were collected from volunteers. Confusion activities were solved with a CNN achieves 96% on DA and 90.6% accuracy on SA meanwhile hyperparameter tuning generate accuracy 97% on all of datasets