如何将机器学习分类方法应用于1D时间序列数据?

I have IMU data (accelerometer, magnetometer, and gyroscope) during a variety of exercises (squats, push-ups, sit-ups, burpees). These exercises are completed in a single 1D time series signal and I would like to use a machine learning classification method to identify the different exercises within the signal. I do not want to condense the signal into 0D peaks and build my features that way but rather keep the time domain intact. Below is a figure showing example data from the accelerometer that contains the four exercises. My question therefore is which method would be most effective at doing so? K-means clustering would be perfect in the 0D sense so is there a 1D equivalent? Any resources to python (sklearn) would be greatly appreciated!I have IMU data (accelerometer, magnetometer, a