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.

随着可穿戴式传感器的普及，通过使用从传感器获得的数据来解决活动识别任务的解决方案已经普及。近年来，已经研究了由于诸如加速度计，陀螺仪和磁力计等可穿戴传感器的活动识别。尽管在文献中有多种应用，但在本研究中有所不同，通过输入从加速度计获得的数据，深度学习算法（例如由卷积LSTM馈送的卷积神经网络，卷积LSTM和3D卷积神经网络）已用于人类活动识别任务。传感器。为此目的，形成具有相同活性的原始样品的框架，该原始样品连续地从加速计传感器收集。因此，