Personalized Human Activity Recognition Using Convolutional Neural Networks

Because the sensor captures human accelerations continuously Inertial wearable sensors have been vastly utilized for Human while the subject performs different activities in freeliving Activity Recognition (HAR). A major challenge with situations, 'start' and'end' of activities are unknown the trained HAR models is that the performance of the classifier a priori. A typical segmentation with a window of size w is highly sensitive to the context of the sensor and engineered on 3-axis accelerometer data forms 3 channels of input data, features (Rokni and Ghasemzadeh 2017).