<p>In recent years, there has been an increase in the
popularity of wearable sensors such as electroencephalography (EEG) sensors,
electromyography (EMG) sensors, gyroscopes, accelerometers, and
photoplethysmography (PPG) sensors. This work is focused on PPG sensors, which
are used to measure heart rate in real time. They are currently used in many
commercial products such as Fitbit Watch and Muse Headband. Due to their low
cost and relative implementation simplicity, they are easy to add to
custom-built wearable devices.</p><p><br></p>
<p>We built an Arduino-based wearable wrist sensor-kit that
consists of a PPG sensor in addition to other low cost commercial biosensors to
measure biosignals such as pulse rate, skin temperature, skin conductivity, and
hand motion. The purpose of the sensor-kit is to analyze the effects of stress
on students in a classroom based on changes in their biometric signals. We
noticed some failures in the measured PPG signal, which could negatively affect
the accuracy of our analysis. We conjectured that one of the causes of failure
is movement. Therefore, in this thesis, we build automatic failure detection
methods and use these methods to study the effect of movement on the signal.</p><p><br></p>
<p>Using the sensor-kit, PPG signals were collected in two
settings. In the first setting, the participants were in a still sitting
position. These measured signals were manually labeled and used in signal
analysis and method development. In the second setting, the signals were
acquired in three different scenarios with increasing levels of activity. These
measured signals were used to investigate the effect of movement on the
reliability of the PPG sensor. </p><p><br></p>
<p>Four types of failure detection methods were developed:
Support Vector Machines (SVM), Deep Neural Networks (DNN), K-Nearest Neighbor
(K-NN), and Decision Trees. The classification accuracy is evaluated by
comparing the resulting Receiver Operating Characteristic (ROC) curves, Area
Above the Curve (AAC), as well as the duration of failure and non-failure
sequences. The DNN and Decision Tree results are found to be the most promising
and seem to have the highest error detection accuracy. </p>
<p> </p>
<p>The proposed classifiers are also used to assess the
reliability of the PPG sensor in the three activity scenarios. Our findings
indicate that there is a significant presence of failures in the measured PPG
signals at rest, which increases with movement. They also show that it is hard
to obtain long sequences of pulses without failure. These findings should be
taken into account when designing wearable systems that use heart rate values
as input.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8040773 |
| Date | 14 May 2019 |
| Creators | Deena Alabed (6634382) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Photoplythesmogram_PPG_Signal_Reliability_Analysis_in_a_Wearable_Sensor-Kit/8040773 |
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