Compared to Imagimob AI, the deep learning model achieved a similar level of accuracy, but used 33 times more memory and required 800 times more instructions in order to make a single classification.
The aim of the study was to benchmark Imagimob’s machine learning software, Imagimob AI, against one leading deep learning model for time series classification. In order to find the optimal deep learning model for the study, a wide range of architectures and hyper-parameters were tested. The case chosen for the study was sourced from a real customer project, where the aim was to detect two specific activities hidden inside a long sequence of motion data. The dataset consisted of accelerometer and gyro data from a microelectromechanical (MEMS) inertial measurement unit (IMU).
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