Real time Stress Detection Through ECG and PPG measurements
Dr. Mohamed Younis, Tasnim Nishat Islam, Department of Computer Science and Electrical Engineering
Mental health has drawn increased attention in recent years, motivated by the social impact of the COVID-19 pandemic. Given the need to detect issues in early stages, relying on the conventional doctor visit based approach is not scalable and is too costly. The development of wearable mental health monitoring solutions is an effective means for filling the gap where individuals are provided with an assessment that can help them in adjusting their lifestyle. Stress is one of the most causes of mental health problems. We opt to serve such a goal by devising a novel wearable-based approach for detecting stress in real time. In this paper, we are experimenting with two benchmark datasets in this domain, namely, the WESAD Dataset and the SWELL Knowledge Work Dataset. For these datasets, the subjects are shown some videos; based on the situation provided, the subjects document their reaction by rating their feelings like valence, arousal, etc. Specifically, we are exploring the use of Electrocardiogram (ECG) and Photoplethysmography (PPG) data. Although Electroencephalogram (EEG) is usually used to assess stress, acquisition of EEG data is much more logistically-involving and expensive than ECG and PPG data. To determine the relevant features, we are studying how ECG and PPG can be correlated with EEG data using a third dataset, called Deap. To assess stress, we apply ConvLSTM and ResNet to the ECG and PPG signals. The paper evaluates the different statistical features and their computational complexity.
Sustaining Anonymity of Critical Nodes in Ad-hoc and Sensor Networks
Mohamed Younis, Computer Science and Electrical Engineering
Jon Ward, Computer Science and Electrical Engineering
Yousef Ebrahimi, Computer Science and Electrical Engineering
Rania El-badry, Computer Science and Electrical Engineering
Sami Alsemairi, Computer Science and Electrical Engineering
Many applications have drawn interest in wireless sensor networks (WSNs) in recent years. Most notable among these WSN applications are those serving in hostile environments, such as combat field reconnaissance, border protection, and security surveillance, where the network may be subject to adversary’s attacks. Typically a WSN is composed of a large number of sensor nodes that monitor their surroundings and report their measurements to a nearby base-station. The base-station interfaces the network to remote users and often tasks the sensors and manages their operation. Given the role that the base-station plays, it is the most attractive target for an adversary who opts to inflict maximum damage to the operation of the WSN. The fact that the base-station is the sink of all data traffic makes it vulnerable. Packet encryption would not be a sufficient countermeasure since an adversary can intercept the individual wireless transmission and employ traffic analysis techniques to follow the data paths. Since all active routes ends at the base-station, the adversary may be able to determine its location and launch targeted attacks. In this project we investigate metrics for assessing the base-station anonymity, and develop techniques for countering traffic analysis and concealing the location, identity and role of the base-station.