Hello,
I'm just getting acquainted with methods of machine learning, and am experimenting with neural networks at the moment. I need some advice how to approach a specific task of classification where serial ordering is important as well.
My current goal is to examine a data timeseries, and classify it into successive phases. I have not seen any examples of classificaiton where there is a serial dependence between classes, and a fixed order that must be satisfied.
To better explain the data set, and the task scenario, the data is acquired from a motion analysis system. There are 7 motion-tracked markers on someone's hand/arm, and the dataseries is captured in 3D, i.e. 21 features total.
There are multiple repetitions of the following behavioural task: 1. trial begins with hand at rest 2. after short delay, a the hand is lifted and moves to a target location to pick up an object 3. the object is lifted (from target-0), and then set down at another location (target-1) 4. the object then picked up from target-1 and set down at target-2
In other words the phases I need to classify would be: phase 1 would be from the beginning of the timeseries until the hand is lifted phase 2 the relatively motionless period during grasping the object at target-0 until lifting the object at target-0 phase 3 the time spent in motion from lifting the object at target-0 to setting down the object at target-1 phase 4 the relatively motionless period during setting the object at target-1, to lifting it from target-1 phase 5 the time spent in motion from lifting the object at target-1 to setting down the object at target-2
My goal is to train a neural network to recognize when the hand is initially lifted from the neutral position, when the hand reaches the target-0 to lift the object, when the hand sets down the object at target-1, and when the hand lifts from target-1 and then sets down object at target-2.
I can manually create the correct classifications for training data very easily, as the data stream is easy to classify through a visual inspection of the plotted timeseries. My use for applying a solution with a neural network is to infer when each movement phase starts/ends for many separate iterations (over a thousand) of this pattern of movement.
Any suggestions how to approach this problem?
If the info provided isn't adequate to understand my data or classification challenge, I'll post a reply with links to some plots.
Thanks, Brian
p.s. I'm not so much worried about data preprocessing as I am about how to construct, train and use an appropriate neural network model.
