Abstract— This work presents the development of a fully functional prototype of a wearable smart shoe insole that can monitor arterial oxygen saturation (SpO2) levels at the foot of a diabetic patient using photoplethysmography (PPG)... more
Abstract— This work presents the development of a fully
functional prototype of a wearable smart shoe insole that can
monitor arterial oxygen saturation (SpO2) levels at the foot of a
diabetic patient using photoplethysmography (PPG) signals.
Continuous monitoring of SpO2 levels at foot in patients with
diabetic foot ulcer (DFU) can provide critical information on the
severity of the ulcer, the wound healing process, and alerting
clinicians for critical limb ischemia. The developed oximetry
system seamlessly integrates the Internet of things (IoT) via a
custom-developed Android mobile application, thus enabling “athome”
monitoring. Twenty healthy subjects were tested, and the
insole oximeter was able to successfully estimate SpO2 levels at the
toe. An average error of ≈ 2.6% was calculated for the
measured/estimated SpO2 levels at the subjects’ toe when
compared to a reference oximeter attached to the finger. Perfusion
Index (PI) - which represents the blood flow/supply in tissues - was
used as a method for validating the oximeter readings. It was
observed that fingers (index fingers) generally have larger PI
values when compared to the toe, while PI results of both
monitoring sites were in the acceptable range. In addition, a dataset
was formed for the twenty test subjects, and machine learning
(ML) techniques were applied to predict the SpO2 level and the site
of measurement (Finger or Toe), using multiple linear regression
and classification methods. The ML results show that AC
components of the PPG signals have a more significant
contribution to the SpO2 estimations when compared to the DC
components. In addition, KNN (K=1) classifier, was able to
successfully predict the monitoring sites, with a test accuracy of
96.86%.
functional prototype of a wearable smart shoe insole that can
monitor arterial oxygen saturation (SpO2) levels at the foot of a
diabetic patient using photoplethysmography (PPG) signals.
Continuous monitoring of SpO2 levels at foot in patients with
diabetic foot ulcer (DFU) can provide critical information on the
severity of the ulcer, the wound healing process, and alerting
clinicians for critical limb ischemia. The developed oximetry
system seamlessly integrates the Internet of things (IoT) via a
custom-developed Android mobile application, thus enabling “athome”
monitoring. Twenty healthy subjects were tested, and the
insole oximeter was able to successfully estimate SpO2 levels at the
toe. An average error of ≈ 2.6% was calculated for the
measured/estimated SpO2 levels at the subjects’ toe when
compared to a reference oximeter attached to the finger. Perfusion
Index (PI) - which represents the blood flow/supply in tissues - was
used as a method for validating the oximeter readings. It was
observed that fingers (index fingers) generally have larger PI
values when compared to the toe, while PI results of both
monitoring sites were in the acceptable range. In addition, a dataset
was formed for the twenty test subjects, and machine learning
(ML) techniques were applied to predict the SpO2 level and the site
of measurement (Finger or Toe), using multiple linear regression
and classification methods. The ML results show that AC
components of the PPG signals have a more significant
contribution to the SpO2 estimations when compared to the DC
components. In addition, KNN (K=1) classifier, was able to
successfully predict the monitoring sites, with a test accuracy of
96.86%.
