  Proactive monitoring of one's health could avoid serious diseases as well as
better maintain the individual's well-being. In today's IoT world, there has
been numerous wearable technological devices to monitor/measure different
health attributes. However, with that increasing number of attributes and
wearables, it becomes unclear to the individual which ones they should be
using. The aim of this paper is to provide a recommendation engine for
personalized recommended wearables for any given individual. The way the engine
works is through first identifying the diseases that this person is at risk of,
given his/her attributes and medical history. We built a machine learning
classification model for this task. Second, these diseases are mapped to the
attributes that need to be measured in order to monitor such diseases. Third,
we map these measurements to the appropriate wearable technologies. This is
done via a textual analytics model that we developed that uses available
information of different wearables to map the aforementioned measurements to
these wearables. The output can be used to recommend the wearables to
individuals as well as provide a feedback to wearable developers for common
measurements that do not have corresponding wearables today.
