  To demonstrate the value of machine learning based smart health technologies,
researchers have to deploy their solutions into complex real-world environments
with real participants. This gives rise to many, oftentimes unexpected,
challenges for creating technology in a lab environment that will work when
deployed in real home environments. In other words, like more mature
disciplines, we need solutions for what can be done at development time to
increase success at deployment time. To illustrate an approach and solutions,
we use an example of an ongoing project that is a pipeline of voice based
machine learning solutions that detects the anger and verbal conflicts of the
participants. For anonymity, we call it the XYZ system. XYZ is a smart health
technology because by notifying the participants of their anger, it encourages
the participants to better manage their emotions. This is important because
being able to recognize one's emotions is the first step to better managing
one's anger. XYZ was deployed in 6 homes for 4 months each and monitors the
emotion of the caregiver of a dementia patient. In this paper we demonstrate
some of the necessary steps to be accomplished during the development stage to
increase deployment time success, and show where continued work is still
necessary. Note that the complex environments arise both from the physical
world and from complex human behavior.
