IUCRC Phase I University of Missouri: Center to Stream Healthcare In Place (C2SHIP)

Healthcare In Place (C2SHIP) unites the best minds in academic medical and engineering disciplines with leaders in biomedical industry to research, develop and promote in-place care technologies for managing chronic health conditions in the home. The Center will accelerate innovation through partnerships, multi-specialty collaborations, and resource sharing. C2SHIP will prepare an educated workforce to promote wellness through self-care technologies.

Predicting ALS Outcomes Based on Networked Passive Sensors

The research team proposes to expand and adapt this existing sensor platform to work with people living with ALS. Researchers will add wrist-based wearable sensors (like a smart watch or fitness tracker) to the system, adding the ability to track indicators like blood oxygen saturation and activity outside of the home. Researchers will first test […]

Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke

Specific Aims. Development and Acceptability of an Ambient In-Home Activity Assessment Tool for Stroke Stroke is the leading cause of serious, long-term disability in the United States [1]. Every year, approximately 800,000 people experience a new or recurrent stroke [1]. Due to advances in acute neurological care, nearly 85% survive and many live with the […]

Customized Health Alerts and Consumer-Centered Interfaces for ADRD Patients and Family

Sensing technologies hold enormous potential for detecting and tracking health changes that can dramatically affect the aging experience. Embedded health assessment can improve management of chronic or acute conditions, and thus, improve quality of life. Problems in chronic disease management are often the cause of losing independence for aging Americans.

Linguistic Summarization of Sensor Data for Early Illness Recognition in Eldercare

Extracting information from the sensors installed in the homes of elderly pose a unique set of challenges. Add to it the short amount of time the clinicians and nurses have to analyze this data, and the problem becomes more complicated. The ongoing work in this project focuses on development of algorithms to glean information from in-home sensor data and then presenting it in the form of textual summaries using Natural Language Generation techniques.