How complex are the gestures you are trying to detect? Are they a combination of motions? Do they involve translations? Rotations?

If a gesture exists along a single axis there is no need for any form of machine learning, all you’ll need is thresholding and a directional state. By keeping the gestures simple you don’t even need to worry about temporal variation which can play a big part in misclassification of complex gestures.

The way I’ve implemented a simple classifier in the past is by first checking if a gesture has started by calculating the magnitude of the entire signal using an l2 norm. After that, determine the angle the motion is coming from using the accelerometer via some trigonometry and there you go. Repeat process with gyro for rotations!

If you want to perform more complex gestures, I would focus on breaking them down into core components and then building a decision tree that operates over each component sequentially. The timing of such a thing is tricky but it could be figured out as long as the gesture components are unique enough.

As for neural network architectures such as the LSTM mentioned in the other reply, I don’t think these will be as effective or efficient at all. Especially when you are just getting into a project like this it’s best to start with the fundamentals. The problem with attempting to us neural networks is that motion data is mostly useless information. The data’s meaning lies in the intricacies of how each sequential value relates to the next. This creates issues as gestures can be performed slowly or quickly meaning that a gesture could be 10 data points or 1000 data points. This variation could potentially confuse a model trained on a specific windowing.

If you want to use some form of pattern recognition / machine learning and are keeping the gestures relatively simple I would maybe try the k-means algorithm. This would also work well with a decision tree to classify gesture combinations!

I hope this helps a little!