Integrating Voice-Based Machine Learning Technology into Complex Home Environments
Ye Gao, Jason Jabbour, Eunjung Ko, Lahiru Nuwan Wijayasingha, Sooyoung, Kim, Zetao Wang, Meiyi Ma, Karen Rose, Kristina Gordon, Hongning Wang, John, Stankovic

TL;DR
This paper discusses the challenges and solutions for deploying voice-based machine learning health technologies in real home environments, using a project that detects anger in caregivers of dementia patients as a case study.
Contribution
It presents an approach for developing voice ML solutions that are more likely to succeed in complex real-world home deployments, highlighting necessary development steps and ongoing challenges.
Findings
Deployment in 6 homes over 4 months demonstrated practical challenges.
Development steps can improve deployment success.
Ongoing work needed to address environmental and behavioral complexities.
Abstract
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…
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Taxonomy
TopicsAI in Service Interactions · Digital Mental Health Interventions · Emotion and Mood Recognition
