Year: 2017
Type: article
Abstract: With the multitude of non-communicating wearable sensors, there is an urgent need to better combine wearable data streams in order to improve human health and well-being. A five-step process is proposed. The first step is to specify the human behavior that the data set will address. The second step is to critically assess primary measurement that allows the behavioral goal to be addressed. After this, other streams can be integrated in a hierarchical fashion based on their accuracy, precision and relevance. The third step is to perform a hierarchical synthesis of the multiple data streams. In the fourth step, the multiple data streams are integrated for practical use; we propose achieving this with wearable computers. The final step is that system retraining occurs, via Artificial Intelligence, so that an integrated wearable system can be individualized. A case study of Type 1 diabetes is used: this analysis and the proposed solutions illustrate the need for an urgent interdisciplinary debate to advance useful methods for combining data from divergent wearable sensors. Wearable fully integrated systems, programmed with Artificial Intelligence, will enable data from multiple wearable sensors to be optimized to improve individual well-being.
Source: Technology and Investment
Author: James A. Levine
Institutions: Mayo Clinic in Arizona, Arizona State University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2584011886