A collaboration between University of Virginia engineers, clinicians, and the school’s Institute of Aging, has teamed up with AFrame Digital, a company building mobile sensors and networks, to create a large scale real-time gait monitoring system. Using wrist worn sensors, the goal of the project is to keep an eye on geriatric patients, looking for signs of declining walking ability. To make the sensors truly effective with a high level of predictability, the first major hurdle will be to test the system and find which signs coming from the sensors point toward future problems that may warrant a prescription for a walking stick.
John Lach, an associate professor in the Charles L. Brown Department of Electrical and Computer Engineering [University of Virginia], has been researching and developing wireless body sensors for the past five years. In this application, the sensors can be worn like a wristwatch. Using parameters determined in a gait laboratory directed by D. Casey Kerrigan, a professor in the School of Medicine’s Department of Physical Medicine and Rehabilitation, Lach has developed sensors that can quantitatively measure the walking patterns that are likely to lead to falls.
Lach’s sensors, now about the size of a digital watch face, can measure and transmit data on a wide range of human motion, including linear acceleration, or how fast patients move in a straight path, and rotational rate, which together provide six degrees of freedom motion capture. The sensors are now in their third generation of development and, thanks to the living laboratory model, they will now evolve with faster prototyping cycles that use continuous feedback from the patients.
Currently, monitoring gait-related problems typically requires patients to visit a health care facility, where they walk on a pressure sensitive treadmill and are monitored by video cameras. While accurate, this approach is costly and limited in its application.
AFrame’s nonintrusive wireless monitoring and alerting system is designed to enhance the independence and security of residents and patients as they move about in a long-term care facility during their activities of daily living, so that they experience greater peace of mind and confidence for a fuller and more active lifestyle. Data from sensors is securely analyzed in the system to provide a private and nonintrusive indication of status to caregivers.