Understanding Variability in Older adults using Inertial Sensors

Falls are the most frequent cause of unintentional injuries among older adults; afflicting 30 percent of persons aged 65 and older and more than 50 percent of persons aged 85 and older. There is a serious need for strategies to prevent falls in elderly individuals, but an important challenge in fall prevention is the paucity of objective evidence regarding the mechanisms that lead directly to falls. There exists no mechanisms about how to predict and manage elderly falls, which has multifactorial risk factors associated with its occurrence in the elderly. As the U.S. population continues to age, both the number of falls as well as the cost of treatment of fall injuries will continue to grow. Decades of research in fall prevention has not led to a decrease in the fall incidence; thus new strategies need to be introduced to understand and prevent falls.

Aging reduces the adaptability of various physical and environmental stressors that hinder stability and balance maintenance and may therefore result in a fall. Movement variability in an individual's task performance can be used to assess the limitations of the movement control system. Maintaining variation in movement engenders flexible and adaptable modalities for elderly individuals to prevent falls in an unpredictable and ever changing external environment. Conversely, excessive variability of movement may drive the control system closer to its stability limits during balance and walking tasks.

Accordingly, inertial sensors are an emerging wearable technology that can facilitate noninvasive monitoring of fall prone individuals in clinical settings. This research examined the potential of inertial sensors for use in clinical settings, and evaluated their effectiveness in comparison to mature laboratory systems (i.e., force platform and camera system). Study findings showed a relationship between movement variability and fall risk among healthy young and older adults. Further, the outcomes of this work translates to the clinical environment to better understand the health status (leading to frailty) of cardiac patients; reflected by the underlying adaptability of the control system, but requires further improvements if to be used as robust clinical tool.

This research provides the groundwork for rapid clinical assessments in which its validity and robustness should be investigated in future efforts.