Falls can lead to loss of independence and even death [1, 2]. Identifying those at risk of falling is an important clinical task often conducted in e.g., those with visual impairment [3], and the elderly [4,5,6].
Equally, fall risk assessment is of notable importance and
pragmatically useful in people with a movement disorder, such as
Parkinson’s disease (PD) [7,8,9] or Stroke [10,11,12,13]
due to observable functional deficits in motor control. Additionally,
assessing fall risk is equally important during pregnancy [14] where a third of pregnant women may fall [15].
In fact, there is a significant increase in falls from pre-pregnancy to
the 3rd trimester which cannot be fully explained by morphological [16] or biomechanical [17] changes.
A
comprehensive fall risk assessment is multifactorial and a
time-consuming process including but not limited to medication review,
cognitive screening, detailing a history of falls, as well as evaluating
gait, balance [18],
and environmental hazards or hazardous activities that have been
documented in some cases to be responsible for 50% of falls [19]. For timeliness in many settings, assessing gait alone is usually conducted to evaluate intrinsic fall risk [20]. That is convenient as gait is a good marker of global health [21] and fundamental to many activities of daily life [1].
Consequently, a gait assessment with positive outcomes from subjective
evaluation (by an assessor) provides insight into the patient’s
independence and ability to ambulate with minimal fall risk. As
described, an assessment is typically conducted by manual observation
alone, where an assessor examines a person’s gait during a scripted task
(i.e., walking protocol). Often, a protocol may include navigating
(walking around or over) obstacles [22,23,24,25], deliberately challenging the person by increasing gait demands [26].
Yet, that also places extra burden on the assessor, challenging them to
carefully observe the person’s gait during a more complex task.
Instrumentation is needed to optimize assessment protocols while
providing high resolution objective fall risk data.
The
integration of digital technology as an objective standard in fall risk
is not routine. While digital tools may provide clinicians with
high-resolution data to potentially aid in determining a patient’s fall
risk, there is still ongoing work to be done in understanding their full
utility and developing appropriate methods. In recent years, technology
has matured to include a wide selection of digital tools. Of course, 3D
motion capture systems are a perceived gold/reference standard for
human movement analysis, but it lacks practicality and deployment in
habitual settings. Moreover, reflective markers require timely
application. In contrast, wearable devices (i.e., inertial measurement
units, IMUs) are quickly attached and provide clinically relevant gait
characteristics to a millisecond resolution in any environment [27,28,29,30].
An objective gait assessment to inform fall risk is usually conducted within a laboratory with a single IMU on the lower back [30]. Typically, participants are then asked to undertake a protocol representing walking challenges in daily life [31, 32], like obstacle crossing [25].
However, a key IMU limitation is the provision of inertial gait data
only without any insights into navigating behavior and visual attention
allocation to environmental/extrinsic details. Accordingly, there is no
absolute clarity to understand how gait and fall risk is influenced by
other intrinsic (e.g., visual attention) or extrinsic (e.g., obstacles)
factors. For example, a comprehensive instrumented assessment would
better understand how those being assessed allocate visual attention
along their walking path for safe navigation while also determining the
role of attention when e.g., peripheral obstacles cause a distraction.
Supplementing IMU data with video data from video-based eye tracking
wearable glasses could better define intrinsic and extrinsic factors,
providing a contemporary and pragmatic approach to fall risk assessment
with easily attached wearables. (Indeed, eye tracking offers an avenue
for exploring neurocognitive changes as a reason for increased falls
incidence.)
Commercial eye tracking glasses capture high quality
video data and often in the standardized MP4 format with a resolution of
1920 × 1080. The video contains a superimposed crosshair to display eye
location. Accordingly, videos contain data on the general environment
and specific objects of where the wearer is looking but data processing
of eye-tracker videos is extremely time consuming and needs to be
automated to allow clinical application [33].
Including eye tracking (to identify an object/obstacles of interest)
with IMU data during a range of simulated free-living tasks (e.g.,
obstacle crossing) would provide a novel approach for simultaneously
instrumenting visual attention during gait within a fall risk
assessment. To accomplish this, a suitable methodology to instrument
visual attention from video data must first be established as none
currently exists. Accordingly, a novel vision-aided fall risk assessment
(VAFRA) is proposed in this study.
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