A Method for Dividing Time-Series Data Periods Using Spectrogram Analysis of Peaks and Calms
Kosuke Shima, Takeo Ueda, Atsuko Mutoh · 2024
Spreading of smartphone, wearable devices, and IoT systems has led to an increasing amount of time-series data. Various types of time-series data are being sampled today, and it needs to be utilized efficiently. However, time-series data has several characteristics that differ according to sampled target, such as periodicity, trends, and complexity. The characteristics make analyzing methods harder to apply versatilely. Therefore, lots of studies focus on one target and propose a corresponding method. In this research, we focus on "momentary large value" and "temporary stopping" in such as martial arts demonstration, CNC control signal, and steep step response signal. In this paper, we propose a method which detects breaks of periods and divides data using spectrogram. The method assumes that the data has one peak and one stasis for each period and detects breaks of periods from multiple criteria by detecting each. In an experiment, we applied the method for 3-axes accelerometer data of Taekwondo demonstration sampled by smartphones. We confirmed the method could detect the breaks correctly.