The goal of this project is to develop and evaluate a position tracking and mobility assessment system that will enable the elderly to live longer and more safely at home or in an assisted living facility. The ability to move is a critical function tat underlies the quality of life for the elderly. Changes in mobility such as speed of walking have been shown to correlate with changes in physical and cognitive function, and can predict future cognitive and physical decline. Positional tracking and mobility information can be further used to estimate clinically relevant activities of daily living, which are useful in assessing patient health. EmbedRF's objective in phase 2 is to continue research and development towards the commercialization of a new position tracking and mobility assessment system called MobileRF. This system will be low-cost, accurate, unobtrusive, and easy-to-install. In phase 1, preliminary prototype hardware was designed and fabricated, which enabled us to meet and exceed our specific aims by (1) demonstrating feasibility of the MobileRF system to effectively track one or more people's movement within a living space at sub-meter accuracy using a body-worn tag, and (2) demonstrating feasibility of tag-free tracking at 2-3 meter accuracy without requiring the individual to wear a tag. The system consists of multiple wireless access points (APs) positioned throughout the home, an (optional) small wireless tag worn by the individual, a hub that aggregates the information and performs the position tracking and mobility assessment, and an information system used to notify friends, family, and health care providers when a change in health status has occurred. The novel tag-free approach uses the APs to evaluate disruptions in radio frequency (RF) signals as a person walks freely throughout their home. During passive localization, the system will also determine walking speed, activity level, and assess social interaction by detecting if multiple people are present. For even higher levels of accuracy, a tag may be worn that uses time-of-flight wireless radio transceivers, inertial sensors (3-axis accelerometers and gyroscopes), and proprietary Bayesian tracking algorithms, to provide accurate sub-meter localization and fine level 3D movement and mobility assessment. In phase 2, we plan to build on the success achieved in phase 1 by adding several new sensors and features to the tag-free and tag-based tracking systems that will significantly improve our monitoring accuracy, completing the final prototype, and evaluating the system in the homes of 20 seniors. MobileRF will be marketed to (1) the senior independent living community, (2) assisted care facilities, (3) researchers who monitor mobility during clinical trials for assessing the effectiveness of drugs, surgical procedures, and other treatments for illness in older people, and (4) companies who provide patient tracking and health monitoring and want to license MobileRF technology to improve the accuracy and functionality of their current product offering.

Public Health Relevance

The ability to move is a critical function that underlies the quality of life for elders. Changes in aspects of mobility such as walking speed and stride length have been shown to correlate with changes in physical and cognitive function. Development of a low cost system for unobtrusive position tracking and mobility assessment will allow elders to continue living independently in their homes knowing that health care providers are monitoring their well-being and will respond should an emergency arise.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-RPHB-C (11))
Program Officer
Bhattacharyya, Partha
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Embedrf, LLC
United States
Zip Code
Jacobs, Peter G; Wan, Eric A; Schafermeyer, Erich et al. (2014) Measuring in-home walking speed using wall-mounted RF transceiver arrays. Conf Proc IEEE Eng Med Biol Soc 2014:914-7
Paul, Anindya S; Wan, Eric A; Adenwala, Fatema et al. (2014) MobileRF: A Robust Device-Free Tracking System Based On a Hybrid Neural Network HMM Classifier. Proc ACM Int Conf Ubiquitous Comput 2014:159-170