I'm hoping someone could point me in the direction of some signal processing methods to clean up my data. I'm collecting physiological data from human muscle (lower leg; gastrocnemius) during walking/gait.
I know the sensor is picking up contractions, but the problem is it's so plagued by motion and skin artefacts I can barely do anything with it. Performing an isometric contraction (contracting the muscle without changing its length) shows a good signal (as there are no motion or skin artefacts to contaminate it. However, when collecting real-world data of a subject walking I'm getting huge spikes on the heel strike, amongst other unpleasantries.
The sensor type revolves around a MEMS microphone, so basically it picks up everything. It is un-amplified and only filtered by post processing. The sampling rate is 1kHz and seems to be more than enough. It's a single channel/vector of a continuous signal (i.e. 10 minutes of collection at 1kHz is a 1x600000 vector).
Please help me by suggesting some methods on what I can do. I've been looking at wavelet transforms which look promising, but any help on the matter will be much appreciated. Also look at PCA but having a vector makes it difficult (unless I break my data into windows?). I'm trying to keep it as uncontrolled as possible, so any analysis that I can do on my current collected data would be great!
Can provide some data if it helps in your analysis.