3 research outputs found
Recognition of Smoking Gesture Using Smart Watch Technology
Diseases resulting from prolonged smoking are the most common preventable
causes of death in the world today. In this report we investigate the success
of utilizing accelerometer sensors in smart watches to identify smoking
gestures. Early identification of smoking gestures can help to initiate the
appropriate intervention method and prevent relapses in smoking. Our
experiments indicate 85%-95% success rates in identification of smoking gesture
among other similar gestures using Artificial Neural Networks (ANNs). Our
investigations concluded that information obtained from the x-dimension of
accelerometers is the best means of identifying the smoking gesture, while y
and z dimensions are helpful in eliminating other gestures such as: eating,
drinking, and scratch of nose. We utilized sensor data from the Apple Watch
during the training of the ANN. Using sensor data from another participant
collected on Pebble Steel, we obtained a smoking identification accuracy of
greater than 90% when using an ANN trained on data previously collected from
the Apple Watch. Finally, we have demonstrated the possibility of using smart
watches to perform continuous monitoring of daily activities.Comment: 7 pages, Published originally at HIMS in 201
Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents
Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff
and resources. Machine Learning (ML) tools can provide efficient decision
support to caregivers. However, ML models require large volumes of data for the
most accurate results, which is typically not feasible in the chaotic nature of
a chemical MCI. This study examines the application of four statistical
dimension reduction techniques: Random Selection, Covariance/Variance,
Pearson's Linear Correlation, and Principle Component Analysis to reduce a
dataset of 311 hazardous chemicals and 79 related signs and symptoms (SSx). An
Artificial Neural Network pipeline was developed to create comparative models.
Results show that the number of signs and symptoms needed to determine a
chemical culprit can be reduced to nearly 40 SSx without losing significant
model accuracy. Evidence also suggests that the application of dimension
reduction methods can improve ANN model performance accuracy.Comment: 8 Pages to be submitted to CSCE-HIMS 202
Deep Learning at the Edge
The ever-increasing number of Internet of Things (IoT) devices has created a
new computing paradigm, called edge computing, where most of the computations
are performed at the edge devices, rather than on centralized servers. An edge
device is an electronic device that provides connections to service providers
and other edge devices; typically, such devices have limited resources. Since
edge devices are resource-constrained, the task of launching algorithms,
methods, and applications onto edge devices is considered to be a significant
challenge. In this paper, we discuss one of the most widely used machine
learning methods, namely, Deep Learning (DL) and offer a short survey on the
recent approaches used to map DL onto the edge computing paradigm. We also
provide relevant discussions about selected applications that would greatly
benefit from DL at the edge.Comment: 7 Pages, 79 References, CSCI201