Deep Learning Based Real-time Daily Human Activity Recognition and Its Implementation in a Smartphone

A novel method for classifying and identifying human activities in real-time is needed in various human-machine interaction fields. In this paper, a multi-channel motion data collected from a smartphone is structured in a new way and converted to a virtual image. An iOS application software was developed to record and stream motion data and to recognize real-time activities. The time series data of an accelerometer and gyroscope motion sensors are structured into 14x60 virtual image. Similarly, their respective amplitudes of 1 dimensional DFT (Discrete Fourier Transformation) are organized into 14x60 image format. The resultant data was given to the designed CNN (Convolutional Neural Network) for classification. Both data structuring methods were analyzed and compared yet the time series data structuring showed a better result and attained an accuracy of 99.5%. Additionally, the model was tested for real-time activity recognition in a computer and smartphone and achieved an excellent result.