Classifying human activities from accelerometer-data with neural network

Question

I've been tasked to carry out a benchmark of an existing classifier for my company. The biggest problem currently is differentiating between different type of transportation's, like recognizing if i'm currently in a train, driving a car or bicycling so this is the main focus.

I've been reading alot about LSTM, http://en.wikipedia.org/wiki/Long_short_term_memory and its recent success in handwriting and speech-recognition, where the time between significant events could be pretty long.

So, my first thought about the problem with train/bus is that there probably isn't such a clear and short cycle as there is when walking/running for instance so long-term memory is probably crucial.

Have anyone tried anything similar with decent results? Or is there other techniques that could potentially solve this problem better?

Answer 1

I've worked on mode of transportation detection using smartphone accelerometers. The main result I've found is that almost any classifier will do; the key problem is then the set of features. (This is no different from many other machine learning problems.) My feature set ended up containing time-domain and frequency-domain values, both taken from time-series sliding-window segmentation.

Another problem is that the accelerometer can be placed anywhere. On the body, it can be anywhere and in any orientation. If the user is driving, is the phone in a pocket, in a bag, on a car seat, attached to a suction-cup window mount, etc.?

If you want to avoid these problems, use GPS instead of the accelerometer. You can make relatively accurate classifications with that sensor, but the cost is the battery usage.