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Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size. 

Poster presentation 

Poster presentation 

Online Social Networks (OSNs) utilize curation algorithms to present relevant content to users. These algorithms can be manipulated by users with various intentions. We investigate common methods used by manipulators as part of a larger project looking to improve OSN defenses against manipulators. 

Previous studies, both in psychology and linguistics, have shown that individuals with mental illnesses show deviations from normal language use, that these differences can be used to make predictions, and used as a diagnostic tool. Recent studies have shown that machine learning can be used to predict people with mental illnesses based on their writing. However, little attention is paid to the interpretability of the machine learning models. In this talk we will describe our analysis of the machine learning models, the different language patterns that distinguish individuals having mental illnesses from a control group, and the associated privacy concerns. We use a dataset of Tweets that are collected from users who reported a diagnosis of a mental illnesses on Twitter. Given the self-reported nature of the dataset, it is possible that some of these individuals are actively talking about their mental illness on social media. We investigated if the machine learning models are detecting the active mentions of the mental illness or if they are detecting more complex language patterns. We then conducted a feature analysis by creating feature vectors using word unigrams, part of speech tags and word clusters and used feature importance measures and statistical methods to identify important features. This analysis serves two purposes: to understand the machine learning model, and to discover language patterns that would help in identifying people with mental illnesses. Finally, we conducted a qualitative analysis of the misclassifications to understand the potential causes for the misclassifications.