Maryam Khalid

Sleep behavior significantly impacts health and acts as an indicator of physical and mental well-being. Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions. While sleep behavior depends on, and is reflected in the physiology of a person, it is also impacted by external factors such as digital media usage, social network contagion, and the surrounding weather. In this work, we propose SleepNet, a system that exploits social contagion in sleep behavior through graph networks and integrates it with physiological and phone data extracted from ubiquitous mobile and wearable devices for predicting next-day sleep labels about sleep duration. Our architecture overcomes the limitations of large-scale graphs containing connections irrelevant to sleep behavior by devising an attention mechanism. The extensive experimental evaluation highlights the improvement provided by incorporating social networks in the model. Additionally, we conduct robustness analysis to demonstrate the system's performance in real-life conditions. The outcomes affirm the stability of SleepNet against perturbations in input data. Further analyses emphasize the significance of network topology in prediction performance revealing that users with higher eigenvalue centrality are more vulnerable to data perturbations.

Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person’s physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person’s physiology, we also incorporate the environment’s impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user’s social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model’s performance.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.

Exhaled breath can be used in retrieving information and creating innovative communication systems. It contains several volatile organic compounds (VOCs) and biological entities that can act as health biomarkers. For instance, the breath of infected human contains a nonnegligible amount of pathogenic aerosol that can spread or remain suspended in the atmosphere. Therefore, the exhaled breath can be exploited as a source's message in a communication setup to remotely scan the bio-information via an aerosol transmission channel. An overview of the basic configuration is presented along with a description of system components with a particular emphasis on channel modeling. Furthermore, the challenges that arise in theoretical analysis and system development are highlighted. Finally, several open issues are discussed to concretize the proposed communication concept.