3 research outputs found
Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis
In the upcoming years, artificial intelligence (AI) is going to transform the
practice of medicine in most of its specialties. Deep learning can help achieve
better and earlier problem detection, while reducing errors on diagnosis. By
feeding a deep neural network (DNN) with the data from a low-cost and
low-accuracy sensor array, we demonstrate that it becomes possible to
significantly improve the measurements' precision and accuracy. The data
collection is done with an array composed of 32 temperature sensors, including
16 analog and 16 digital sensors. All sensors have accuracies between
0.5-2.0C. 800 vectors are extracted, covering a range from to 30 to
45C. In order to improve the temperature readings, we use machine
learning to perform a linear regression analysis through a DNN. In an attempt
to minimize the model's complexity in order to eventually run inferences
locally, the network with the best results involves only three layers using the
hyperbolic tangent activation function and the Adam Stochastic Gradient Descent
(SGD) optimizer. The model is trained with a randomly-selected dataset using
640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean
squared error as a loss function between the data and the model's prediction,
we achieve a loss of only 1.47x10 on the training set and 1.22x10
on the test set. As such, we believe this appealing approach offers a new
pathway towards significantly better datasets using readily-available ultra
low-cost sensors.Comment: Corrected typ
Heart rate measurement using the built-in triaxial accelerometer from a commercial digital writing device
Wearable devices are on the rise. Smart watches and phones, fitness trackers
or smart textiles now provide unprecedented access to our own personal data. As
such, wearable devices can enable health monitoring without disrupting our
daily routines. In clinical settings, electrocardiograms (ECGs) and
photoplethysmographies (PPGs) are used to monitor the heart's and respiratory
behaviors. In more practical settings, accelerometers can be used to estimate
the heartrate when they are attached to the chest. They can also help filter
out some noise in ECG signal from movement. In this work, we compare the heart
rate data extracted from the built-in accelerometer of a commercial smart pen
equipped with sensors (STABILO's DigiPen), with a standard ECG monitor
readouts. We demonstrate that it is possible to accurately predict the heart
rate from the smart pencil. The data collection is done with eight volunteers,
writing the alphabet continuously for five minutes. The signal is processed
with a Butterworth filter to cut off noise. We achieve a mean-squared error
(MSE) better than 6.685x10 comparing the DigiPen's computed t
(time between pulses) with the reference ECG data. The peaks' timestamps for
both signals all maintain a correlation higher than 0.99. All computed heart
rates from the pen accurately correlate with the reference ECG signals