Fall risk prediction using wearable wireless sensors

Falls are the primary cause of accidental death and injury-related visits to emergency departments in the elderly population. In 2010 alone, 2.3 million nonfatal fall injuries were treated in emergency departments, and approximately 21,700 of those culminated in death, with direct medical costs totaling $30 billion.1 Accordingly, fall prevention requires techniques for accurate assessment of fall risk of individuals, whereas traditional diagnostics entail retrospective observation and rudimentary subjective inspection. Thus, the greatest need for elderly individuals—and health care in general—is arguably predictive techniques and technologies to distinguish elderly individuals at risk of falls. Postural stability measures have been used to determine the risk of fall for a given individual to provide optimal prevention, diagnosis, and treatment.2, 3 Assessments typically occur periodically, performed by physicians who rely on patient self-reports and visual inspection in their diagnosis, measures that lack objectivity and sensitivity, and consequently often culminate in data inaccuracies. Advanced assessments likely include a referral to a laboratory force platform that may be travel-dependent and economically unfeasible. Accordingly, we investigated the potential of inexpensive wearable wireless sensors as an alternative to the force platform.4 One hundred community-dwelling elderly volunteers (56–90 years old, mean age 74:3 ̇ 7:6 years) participated in this study. Subjects’ histories of falls were recorded for the previous 2 years, with an emphasis on frequency and characteristics of falls. Subjects with at least one fall in the prior year were classified as fallers and the others as nonfallers. The study was conducted in four different community centers using a force plate and an inertial measurement unit (IMU). This study was approved by the Virginia Tech Institutional Review Board and was conducted in collaboration with Northern Virginia Fall Prevention Figure 1. Placing the fall risk monitor on an elderly person during the motion-capture experiment.