deliveryPrediction function

Predict Delivery Days in Accelerometry Data

The function predicts the probability of each day in an accelerometry dataset being caused from delivery activity instead of human activity. The prediction model can be selected from one of three models, a Random Forest, a logistic regression, and a convolutional neural network (default: Random Forest).

deliveryPrediction(df, feats, model = c("RF", "GLM", "NN"), ...)

Arguments

• df: A dataframe. The source accelerometry dataset, in dataframe format.
• feats: A dataframe. Features output from the deliveryFeatures function.
• model: A character. Indicates which prediction model to use. RF is a Random Forest. GLM is a logistic regression, and NN is a convolutional neural network.
• ...: not used at this time

Returns

A dataframe is returned with a predicted probability of each day being a delivery activity day.

Details

Function works for data consisting of one or multiple unique trials.

Note

The input dataframe should have the following columns: TimeStamp , axis1 , axis2 , axis3 , vm , where vm is the vector magnitude of axes 1, 2, and 3. Dataframe should also be formatted to 60 second epoch.

Examples

data(deliveryData)

deliveryDataProcessed <- deliveryPreprocess(df = deliveryData)
deliveryDataFeats <- deliveryFeatures(df = deliveryDataProcessed)
deliveryPrediction(deliveryDataProcessed, deliveryDataFeats)

See Also

deliveryFeatures, deliveryPred

Author(s)

Ryan Moore ryan.moore@vumc.org , Cole Beck cole.beck@vumc.org , and Leena Choi leena.choi@Vanderbilt.Edu