A computer-implemented method for training a medication administration monitoring apparatus, comprising the steps of: defining a plurality of particular steps of an administration sequence of one or more predetermined medications; acquiring pixel information from one or more data sources of a user administering medication, wherein acquiring the pixel information comprises acquiring one or more images of the user administering the medication; training a first neural network to recognize a first step of a medication administration sequence, wherein training the first neural network comprises training the first neural network to recognize regions containing a hand of the user as a first target object from the one or more images of the user; training a second neural network to recognize a second step of the medication administration sequence based upon an output from the first neural network, wherein the output from the first neural network comprises regions identified by the first neural network, wherein training the second neural network comprises training the second neural network to recognize regions containing a finger-pill-finger region as a second target object from the regions identified by the first neural network; and training a third neural network to recognize a third step of the medication administration sequence based upon an output from the second neural network, wherein the output from the second neural network comprises regions identified by the second neural network to contain the second target object, wherein training the third neural network comprises training the third neural network to recognize regions containing a medication pill as a third target object from the regions identified by the second neural network; wherein training of each of the first, second, and third neural network comprises employing a plurality of training layers to learn features of a respective target object in the acquired pixel information in accordance with supervised or unsupervised learning techniques, and building a classifier employing the features of the respective target object, wherein employing the classifier allows detection (130, 150, 170, 240) of the respective target obj ect. defining a plurality of particular steps of an administration sequence of one or more predetermined medications; acquiring pixel information from one or more data sources of a user administering medication, wherein acquiring the pixel information comprises acquiring one or more images of the user administering the medication; training a first neural network to recognize a first step of a medication administration sequence, wherein training the first neural network comprises training the first neural network to recognize regions containing a hand of the user as a first target object from the one or more images of the user; training a second neural network to recognize a second step of the medication administration sequence based upon an output from the first neural network, wherein the output from the first neural network comprises regions identified by the first neural network, wherein training the second neural network comprises training the second neural network to recognize regions containing a finger-pill-finger region as a second target object from the regions identified by the first neural network; and training a third neural network to recognize a third step of the medication administration sequence based upon an output from the second neural network, wherein the output from the second neural network comprises regions identified by the second neural network to contain the second target object, wherein training the third neural network comprises training the third neural network to recognize regions containing a medication pill as a third target object from the regions identified by the second neural network; wherein training of each of the first, second, and third neural network comprises employing a plurality of training layers to learn features of a respective target object in the acquired pixel information in accordance with supervised or unsupervised learning techniques, and building a classifier employing the features of the respective target object, wherein employing the classifier allows detection (130, 150, 170, 240) of the respective target obj ect. employing a plurality of training layers to learn features of a respective target object in the acquired pixel information in accordance with supervised or unsupervised learning techniques, and building a classifier employing the features of the respective target object, wherein employing the classifier allows detection (130, 150, 170, 240) of the respective target obj ect.