Fall Risk Prediction using Wearable Sensors in Elderly Individuals

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 got treatment in emergency departments, and approximately 21,700 of those culminated in death, with direct medical costs totaling $30 billion. 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. We investigated the potential of inexpensive wearable wireless sensors as an alternative to the force platform