1. A method, implemented by one or more devices, comprising: receiving, by the one or more devices and from a plurality of personal emergency response (PER) devices, data relating to acceleration events that occurred at the PER devices; associating, by the one or more devices, the data relating to the acceleration events with indications of whether the data relating to the acceleration events corresponds to fall events of users of the PER devices; training, by the one or more devices, a classification model based on the data relating to the acceleration events and the indications of whether the data relating to the acceleration events corresponds to the fall events; customizing the classification model, based on user-specific data, for a particular user of the PER devices, the customization including removing nodes of the classification model that are determined, based on the user-specific data for the particular user, to not affect an output of the classification model for the particular user; transmitting, by one or more devices, the customized classification model to at least some of the PER devices to update a previous version of the classification model at the at least some of the PER devices; identifying high-risk users, out of a plurality of users associated with the PER devices, based on a number of previous fall events associated with one or more users, of the plurality of users; performing a clustering operation based on the identified high-risk users; and determining additional high-risk users based on a result of the clustering operation.