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

Integrating Voice-Based Machine Learning Technology into Complex Home Environments