4 research outputs found
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data
In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data
In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p < 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities
Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks
The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention without the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subject’s data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks algorithms in the management of Type 1 diabetes
Usability and Security of Different Authentication Methods for an Electronic Health Records System
We conducted a survey of 67 graduate students enrolled in the Privacy and
Security in Healthcare course at Indiana University Purdue University
Indianapolis. This was done to measure user preference and their understanding
of usability and security of three different Electronic Health Records
authentication methods: single authentication method (username and password),
Single sign-on with Central Authentication Service (CAS) authentication method,
and a bio-capsule facial authentication method. This research aims to explore
the relationship between security and usability, and measure the effect of
perceived security on usability in these three aforementioned authentication
methods. We developed a formative-formative Partial Least Square Structural
Equation Modeling (PLS-SEM) model to measure the relationship between the
latent variables of Usability, and Security. The measurement model was
developed using five observed variables (measures). - Efficiency and
Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results
obtained highlight the importance and impact of these measures on the latent
variables and the relationship among the latent variables. From the PLS-SEM
analysis, it was found that security has a positive impact on usability for
Single sign-on and bio-capsule facial authentication methods. We conclude that
the facial authentication method was the most secure and usable among the three
authentication methods. Further, descriptive analysis was done to draw out the
interesting findings from the survey regarding the observed variables.Comment: HEALTHINF21 at the 14th International Joint Conference on Biomedical
Engineering Systems and Technologies (BIOSTEC 2021