Instrumentation & Measurement Magazine 26-4 - 40

Fig. 2. Wheelchair with pressure and ultrasonic sensors.
ultrasonic ranging sensor (Sparkfun 15569) and pressure sensor
(Sparkfun 9375). Three pressure sensors are placed on the
seating area (S1, S2, S3), and an ultrasound sensor is placed
on the backrest (S4) of the wheelchair (Fig. 2). The ultrasonic
sensor is employed to measure the distance separating the
backrest and the wheelchair back.
The dataset is relatively small, with 308 data points from
the four sensors. Specifically, there are 88 data points collected
under the right sitting posture, 100 data points under higher
pressure on the right side, 20 data points under higher pressure
on the left side, and 100 data points under higher forward
pressure. For more details on this data, see [7]. Fig. 3 displays
the distribution of the collected pressure and distance
variables and indicates that these datasets are non-Gaussian
distributed. It would challenge traditional dimensionality
reduction techniques, such as PCA, designed based on the
Gaussian assumption of input data. Thus, the ICA model designed
for non-Gaussian data dimension reduction could be
promising.
The introduced monitoring strategy aims to alert an improper
posture to prevent major health problems. Four
scenarios with different sitting postures, which have a distinct
impact on the backbone, are considered to study the
detection performance of the ICA-KD approach (Fig. 1).
The first scenario considers the wheelchair user sitting in
the correct position. The second scenario is when the user is
sitting with higher pressure on the right side, which could
cause possible health problems such as a muscle imbalance,
stress on the liver, and respiratory issues. The third scenario
considers the user posture with higher pressure on
the left side, which may result in muscle imbalance, stress
on the spleen, and respiratory issues. The last scenario is
conducted when the user is sitting with higher forward
pressure, which may cause health problems like back pain
and knee issues. The aim is to alert the user to change sitting
40
Fig. 3. Distribution of the considered pressure and distance data: (a) Pressure
S1; (b) Pressure S2; (c) Pressure S3; (d) Ultrasonic S4.
posture when detecting a bad posture persistently for at
least two minutes.
Detection Results
Three experiments were conducted to demonstrate the benefit
of the proposed ICA-KD scheme in detecting different
improper sitting postures on the wheelchair. Six multivariate
monitoring charts, including PCA-based T2
, squared prediction
error (SPE), and KD monitoring charts and three

Instrumentation & Measurement Magazine 26-4

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