Clinical fall risk assessments often involve questionnaires
or functional assessments of posture, gait, cognition,
and other fall risk factors [17]. These clinical
assessments can be subjective, qualitative [17,18], and
use threshold assessment scores to binarily categorize
people as fallers and non-fallers. This oversimplifies
geriatric fall risk, which is more accurately modeled
by a continuum of fall risk with fuzzy boundaries between
multiple risk categories, such as low, moderate,
and high fall risk. Sensors that measure whole body
motion [19], ground reaction forces [20], and electromyographic
signals [21] provide objective, quantitative
measures for fall risk assessment. However, the associated
equipment is typically located in a gait laboratory
and requires a time consuming setup that is
difficult to practically integrate into typical clinic
schedules. This limits the testing location and frequency.
A wearable system that can efficiently capture
and analyze quantitative mobility data could improve
fall risk assessment.