Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors

With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.

Automated summary

The use of wearable sensors for activity recognition tasks has become widespread, with deep learning algorithms such as Convolutional Neural Networks proving to be effective. In this study, data from accelerometer sensors was used to feed deep learning algorithms, forming consecutive raw samples of the same activity to capture patterns and preserve the continuous structure of movement.