Upsampling Inertial Sensor Data from Wearable Smart Devices using Neural Networks

Inertial sensor data collected from wearable smart devices such as smartwatches are expected to be used in various smart applications such as video game controllers, hand drawing, hand writing, gestural input devices, human activity recognition, and remote communication using sign language. However, since the maximum sampling rate of inertial sensors in commercial smartwatches is restricted, capturing fine-grained body movements using the low-sampled signals is difficult for these sensors. Therefore, this study proposes a new method for generating high sampling rate signals from the low-sampled signals by upsampling the low-sampled signals using interpolation with an artificial neural network. Because it is impossible to obtain "non-existent" data from low-sampled signals according to the information theory, we estimate these data from experience, i.e., using high-sampled signals prepared in advance for training. This is possible because trajectories of a sensor are restricted by the skeletal structure of the body part to which the sensor is attached.