Introduction to Clustering and Classification of Linear Sequence I'm a programmer/engineer that is getting into developing prototype wearable technology. I would like to be in a position to interpret raw accelerometer data and categorised the data into activities like running/walking/rest, etc.

The building and coding of the hardware and software is relatively straight forward, but I do not know the terminology and mathematical processes required in order to take the raw data and turn it into a training set that can be used for a real time system.

My question is this: What is a good DSP starting point to take a time based linear series and extract features so that they may be identified at a later stage?

At present, I have an tri-axis accelerometer device which samples at 50Hz with a range of -/+4g. I would like the capability of being able to readily identify activities based upon a training set, or machine learning process, if that's even possible?

Apologies that this isn't necessarily a specific DSP question, thanks for you assistance.
This seems very much like a fit for Hidden Markov Models (HMM). They are pretty powerful in handling sequence based input such as time series.