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Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data
For a given dataset (D) with n observations and p features (ie, p=168 in our analysis), D={(xi ∈ Rp, yi ∈ R)} ∀ i ∈[1, n], Xgboost ensembles M trees denoted fm to predict the output yi.
The model is trained in a greedy, additive manner starting from m=1 (Figure 2, equation b). Let ŷim−1 be the prediction of yi at the (m−1)th iteration.
JMIR Mhealth Uhealth 2019;7(2):e11201
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