Machine learning algorithm that can use many instances to predict 1 continuous outcome per person I am trying to use movements identified from accelerometers during sleep to predict gait speed (continuous). I am trying to figure out what the best machine learning algorithms/ feature extraction methods can be used to use as much of the information as possible in this prediction.

I am currently identifying every movement that occurs during sleep for each night measured and then extracting features from the accelerations of each movement. I then average all of these features over each movement identified during the night and then each night measured for that person, resulting in 1 set of features per person. This can be easily used in machine learning algorithms to predict gait speed as a continuous variable.

However, I think by averaging over all of the movements per night and then over all of the nights collected, I am losing a lot of information about the individual movements of each person. Is there a way to include all of the movements (~200-1000) from one person as instances that can be used to predict the 1 set of outcomes per person? If not, is there a better way to condense the number of movements in into 1 instance per person without losing a ton of information from taking an average? Maybe using clustering?

Thanks for any input!
I think you would benefit from keeping your data as a time series and use a sequence model. Commonly you would use an RNN, the most popular flavor would be a LSTM.

Here is an example using Keras that I think is relevant for you

Multivariate example from same blog (but forecasting instead of classification)

LSTM with regression