Ramesh Kumar Sah

Ramesh Kumar Sah, Hassan Ghasemzadeh

Stress detection and monitoring is an active area of research with important implications for the personal, professional, and social health of an individual. Current approaches for affective state classification use traditional machine learning algorithms with features computed from multiple sensor modalities. These methods are data-intensive and rely on hand-crafted features which impede the practical applicability of these sensor systems in daily lives. To overcome these shortcomings, we propose a novel Convolutional Neural Network (CNN) based stress detection and classification framework without any feature computation using data from only one sensor modality. Our method is competitive and outperforms current state-of-the-art techniques and achieves a classification accuracy of $92.85\%$ and an $f1$ score of $0.89$. Through our leave-one-subject-out analysis, we also show the importance of personalizing stress models.

Ramesh Kumar Sah, Hassan Ghasemzadeh

Machine learning is used for inference and decision making in wearable sensor systems. However, recent studies have found that machine learning algorithms are easily fooled by the addition of adversarial perturbations to their inputs. What is more interesting is that adversarial examples generated for one machine learning system is also effective against other systems. This property of adversarial examples is called transferability. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from the following perspectives: 1) transferability between machine learning systems, 2) transferability across subjects, 3) transferability across sensor body locations, and 4) transferability across datasets. We found strong untargeted transferability in most cases. Targeted attacks were less successful with success scores from $0\%$ to $80\%$. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreases sharply when the data distribution of the source and target system becomes more distinct. We also provide guidelines for the community for designing robust sensor systems.