A New Method of Dimensionality Reduction for Large Time Series Applied to Accelerometer Wristbands’ Signals

Abstract

Feature extraction for high-dimensional time series has become a topic of great importance in recent years. In the medical field, the information needed to predict emotions, stress, epileptic seizures, heart attacks, and other diseases, can be provided by body sensors in the form of time series signals. This work intends to provide a way for these devices to save the relevant information, using little storage memory, by defining a new feature extraction method. The method proposed in this work relies on the relevant data associated with the “changes” in the time series. These changes are identified according to the conditional probabilities of passing from one state to another during the time series, as well as the “relevance” of each state. We show the results of this method with an experiment based on accelerometers data recorded by the ©ActiGraph wGT3X-BT wristband to recognize sedentary behavior. After applying this method, it was achieved to reduce time series frames of dimension 360, to vectors of dimension 12; while the accuracy of their classification was 84%