brain_den asked . 2021-07-26

serial classification of timeseries "phases" with neural network

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.

neural networks , machine learning , classification

Expert Answer

John Williams answered . 2024-11-11 13:25:52

Inputs to both both static and dynamic nets have a fixed number of inputs. There may be problems if the significant temporal length of feature vectors is variable.

If you are considering a stream of multivariable data with variable length feature regions separated by variable length non-feature regions, it will be very, very difficult.

The success of a this type of classifier depends on preprocessing. Therefore you have to have a clear idea of

 1. How to recognize the presence of target data

 2. How to extract the data from the steam

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