As the “Baby Boom” generation moves into retirement age, there is a need to offer in-home care for elders extending independence in their own homes (aging in place). Falls and consequent injuries are a real danger for this population. Falls are a major cause of long periods of hospitalization and convalescence for many elders. Often they are forced to undergo surgical interventions for broken hip joints or limbs. Such a serious injury may occur to elders living alone and potentially go undetected for hours, and sometimes days. In some cases, such incidents can result in death. In response, many products, services and devices have been designed to help monitor the health and safety of individuals in their own home setting.
Moreover, it has been shown that the general walking patterns of an individual can serve as a health status indicator. In fact, certain diseases can directly affect the gait, stride, pace and the overall walking pattern of an individual. Using a gait monitoring device, those changes in gait patterns would be detected, and thus creating an opportunity for early intervention and possibly the prevention of injury as the result of declining condition from disease or natural aging process.
In an effort to reduce the risk of falls and resultant debilitating injuries, detect gait changes early and increase quality of life issues for the elders, Medical Automation Research Center (MARC), at the University of Virginia, is developing a passive Gait Monitoring Device as a part of an in-home monitoring system.
The MARC gait monitor comprises a highly sensitive and selective sensor technology that is capable of measuring footfalls on the floor. The sensor’s output signal will create a unique signature based in part on the individual’s weight, gait, stride and average pace. The signal is then stored in a computer database for establishing a baseline for the individual. Using this established baseline, analysis and comparison of gait patterns can be made over time. Consequently, gradual deviation from the baseline pattern can be detected, implying a change in an individual’s health status, either for better or worse.
A gait change that might predict a fall (such as the appearance of a pronounced limp) can prompt an automatic alert from the monitoring system to notify the individual, their informal caregiver and the formal health care provider, using a communications interface, such that a preventive intervention may be taken.
Additionally, a detected fall followed by no gait signal indicates a situation where the user is potentially unconscious or unable to initiate an emergency call. In such a case the monitoring system can be preprogrammed to alert the caregivers, via e-mail, instant messaging or phone to check up on their elders. Thus, the Gait Monitor operates in concert with “pendant” type alert systems but does not replace such emergency response devices.
Preliminary tests on this Gait Monitor have demonstrated extremely encouraging results. The device has thus far proven capable of differentiating normal gait from, shuffling, limping, tiptoeing, as well as detecting change in pace, reduced stability (balance) and falling.
A Smart and Passive Floor-Vibration Based Fall Detector for Elderly
Majd Alwan, Prabhu Jude Rajendran, Steve Kell, David Mack, Siddharth Dalal, Matt Wolfe, Robin Felder. The 2nd IEEE International Conference on Information & Communication Technologies: from Theory to Applications - ICTTA’06, April 24 - 28, 2006 in Damascus, Syria.
A Passive Floor-Vibration Based Fall Detector
Prabhu Rajendran, Majd Alwan, Steve Kell, David Mack, Siddharth Dalal, Proceedings of the Second International Conference on Independence, Aging and Disability, St. Petersburg, FL, February 2-4 2006.
Derivation Of Basic Human Gait Characteristics From Floor Vibrations
Alwan M, Dalal S, Kell SW, Felder RA. 2003 Summer Bioengineering Conference.
Passive Unobtrusive Gait Monitor
Alwan, Dalal, Kell. CBI Steps to Success (2002)