NSF Award
0945307
This Small Business Innovation Research (SBIR)Phase I project will demonstrate the feasibility of a wireless, wearable, noninvasive platform for monitoring fitness and health that is virtually unnoticed by the user for maximum performance and convenience. Generating mobile health assessments, such as cardiovascular status, blood pressure, and metabolic activity, requires advanced algorithms that can extract meaningful correlations from orthogonal, low-acuity sensors within a mobile platform. However, commercially available health and fitness monitors cannot generate these assessments during everyday life activities. This limitation is caused by motion-related noise which convolutes sensor data and destroys sensor accuracy and reliability. This Phase I program will demonstrate the feasibility of a novel method for generating mobile health assessments within a mobile platform using machine learning probabilistic models. These models will then be programmed into algorithms that can be implemented in real-time via a single, wearable device in wireless communication with a smartphone. <br/><br/>The broader impact/commercial potential of this project will address critical problems across multiple industries. In the consumer fitness markets, mobile fitness assessments will promote healthier lifestyles and enhanced athletic performance. In mobile health (mHealth) markets, mobile health assessments will substantially reduce medical costs through prevention. In the military/first responder market, wireless vital status assessments will enable advanced performance training and maximization of triage resources. For clinical research markets, the mobile platform will enable new scientific studies correlating physiological reactions to new therapies in a real-world environment. Additionally, novel algorithms demonstrated in this Phase I program can eventually be applied towards wireless mortality monitoring in hospitals as well has home health monitoring for the outpatients and the elderly.
