Instrumentation & Measurement Magazine 24-6 - 81

co-morbidities such as Multiple Sclerosis (MS), Parkinson's
and fibromyalgia [22].
A similar sensor to the pressure sensor is known as a force
sensor, which can be of varying technologies such as photoelectronic,
piezoresistive or capacitive sensors [28]. Force
sensors can be placed in an array on the ground called force
plates or force platforms and are used to measure the force
transmitted to the floor while walking, or the ground reaction
force (GRF) [21]. A GRF measurement alone can be used to
study gait patterns; force plates have been used to differentiate
between Parkinson's gait and normal gait, to classify CP gait
and MS gait, and identify spinal cord injury subjects with and
without assistance [29]. Force plates are often installed in gait
laboratories and are not always a feasible option for at-home
monitoring. A recent, novel force sensor designed to measure
shear force on skin during transfers is in development to assess
forces applied to skin by patient transfer systems; future
improvements may see more force sensors in home environments
[30], [31]. It has been shown that GRF measurements can
be extracted from pressure sensors (as opposed to force sensors)
in wearable insoles, suggesting that wearable pressure
sensor-based insoles may be a suitable at-home gait analysis
replacement for force plate measurements in gait laboratories
[29], [32].
Image-based Sensing
Image-based systems involve one or more cameras in addition
to extensive image processing. The camera number, type and
orientation depend on the desired measurement. Camera setups
can be single view, multiple view (multiple cameras) or
linearly scanning camera view [21]. Image-based systems are
often designed for lab use and therefore are not always easy or
feasible in a home environment. Image processing techniques
have been used in algorithms designed to extract specific gait
measurements. The stereoscopic vision algorithm uses several
cameras to capture gait in different planes and determine
depth of points of interest in the scene [21]. Time of flight systems
(ToF) use near infrared light, visible light cameras, signal
modulation and the phase-shift principle to measure distances
in the scene [21]. Structured light algorithm projects visible
light patterns and, through calculating the deformation of
these patterns in space, the subject movement can be quantified
[21].
Thermal infrared video is another common form of image
capture for gait. Thermal frames are often binarized in
pre-processing and then subjected to measurement directed
techniques. In gait recognition, a modified link model and
SVM classification have been used to characterize and classify
gait features as belonging to a specific subject [33]. The Radon
transform, used in imaging for its line projection properties,
has been fused with other extracted features to characterize
and classify gait features [34]. The wavelet transform has
been used preceding support vector machines to recognize
gait patterns [35]. There are numerous techniques used in gait
recognition, where the commonality between all methods lies
in the general procedure: binarization, feature extraction and
September 2021
pattern recognition through machine learning techniques [21],
[33]-[35]. With respect to gait analysis, infrared technology has
also been used in a structured light manner to extract depth
measurements and analyze gait in mice [21], [36]. This technology
is used in gait analysis for humans, and has been shown to
be effective in full body gait analysis, as well as in detecting affected
gait; the technology has been used differentiate between
patients with and without Parkinson's disease with 92% accuracy
[37], [38]. It has also been used to automatically evaluate
a subject's performance in regards to a clinical tool called the
Tinetti test, which consists of evaluating a subject's ability to
move from sitting to standing, and standing to walking [39].
Additional Sensing Modalities
Ultra-Wide Band (UWB) impulse radar is an ambient technology
more recently explored for its ability to detect biosignals
such as heart rate and respiration rate [40]-[43]. It has high
range resolution, high penetrability (enabling through-wall
imaging) and has the ability to pick up very fine body movements
as a result of the heart and lungs [40]-[43]. Recent work
has shown UWB can separate and evaluate body sway from
respiration and heart rate [40]. UWB is therefore also able to
extract body sway in addition to heart rate and breathing rate,
where the variability and stability of one's posture has previously
been evaluated via pressure sensor arrays on the floor
[40]. It has been suggested that quantifying the variability and
stability of trunk movements during gait may aid in early diagnosis
of dementia [18]. Evaluating body sway can provide
insight on the relative risk of falls for a given subject [40]. UWB
is also capable of distinguishing gait movement from other simultaneous
biomechanical movements, such as arm swings
and biosignals, and has been used to extract human gait measurements
such as speed, step length and step phase as well as
to detect transfers and falls [44], [45]. In addition to body sway,
posture is also recognized as being important in the development
of pressure ulcers while sitting or lying. Pressure and
temperature sensors have been used to assess sitting posture
and risk of pressure ulcer development [46], [47].
Several clinical mobility tools incorporate movement
transfers, as transfers have been shown to be related to adverse
outcomes such as global health decline and falls [9], [48].
The same sensors that can be used to evaluate gait and posture
can be used to evaluate sit-to-stand transfers. Structured
light depth sensors have been used to automatically assess
the timed-up-and-go test. Pressure data extracted from pressure
mats underneath a subject's hip bones have been used to
evaluate duration, stability, and bouncing in sit-to-stand transfers
[26], [49]-[51]. Pressure data has been used in conjunction
with ultrasound sensors to not only evaluate a sit-to-stand
transfer, but determine if the subject received assistance [52].
Assistance is an important factor in evaluating mobility, as
the requirement of assistance indicates the subject is not completely
immobile, but not capable of performing the transfer
independently [9]. A more recent technology using Wi-Fi signals
has been successful in distinguishing between a sitting
down movement, a standing up movement and sitting still (a
IEEE Instrumentation & Measurement Magazine
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