| dc.description.abstract | The BioMKR wearable device developed by Prediktor Medical is aiming to be the
first non-invasive continuous glucose monitoring device on the consumer market.
The data gathered by the device, however, contains movement artifacts that limit
its performance. The artifacts responsible for this deviation is believed to be correlated
with the external forces acting on the device. Therefore, the use of a force
sensor as an auxiliary measurement is evaluated. A Force Sensing Resistor is integrated
with the BioMKR circuitry to enable stand-alone logging of data. The main
purpose for this study is to evaluate the possibility of using the force measurements
to characterize the signal perturbations, and use the data set to compensate for
faulty readings.
This thesis evaluates the influence the external forces captured by the FSR sensor
has on the recorded near infrared signals. Two prototypes have been tested,
where the second prototype needed additional improvement of the FSR actuating
system. Different adapters for the actuating system was developed, and their performance
are measured with respect to FSR signal stability, sensitivity and skin
irritation. Test conducted with the prototypes replicate real use of the device, and
consists of strap test, night test, temperature test and external force test.
The results from night testing have been modeled using PLSR, which showed
some predictive properties. Building a model from one session and using it to predict
the FSR from another session proved unsuccessful, implying weak correlation
between the FSR signal and NIR signal. The alteration of the optical properties
of the tissue as a reponse to applied pressure is believed to affect the NIR signals
in a way that the FSR alone is not able to model.
Using the force signal as an event marker for NIR changes, however, proved to
be very good. It can be shown that the FSR successfully captures every significant
NIR change as a result from involuntary movement during sleep.
It is advised that the force signal should be used together with accelerometer
and other BioMKR parameters in determine the level of trust the resulting NIR
signal has in estimating the correct blood glucose. Further evaluation on filtering
methods to compensate for erroneous BioMKR data is also advised. | |
