activity
20182022
most citedInstant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging

103 citations · 169 across the 8 of their papers we have counts for

collaborators

16 papers

cs.CV20224 cited

Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images

Jitesh Joshi, Nadia Bianchi-Berthouze, Youngjun Cho

Segmentation of thermal facial images is a challenging task. This is because facial features often lack salience due to high-dynamic thermal range scenes and occlusion issues. Limi…

q-bio.NC20212 cited

Bridging the gap between emotion and joint action

M. M. N. Bieńkiewicz, A. Smykovskyi, T. Olugbade +5

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd.…

cs.LG20204 cited

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

Chongyang Wang, Yuan Gao, Akhil Mathur +3

Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states. Existing automatic protec…

eess.AS202016 cited

Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation

Akhil Mathur, Fahim Kawsar, Nadia Berthouze +1

This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Ada…

eess.AS2020

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

Akhil Mathur, Anton Isopoussu, Fahim Kawsar +2

Mobile and embedded devices are increasingly using microphones and audio-based computational models to infer user context. A major challenge in building systems that combine audio…

cs.HC202031 cited

Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Ahmed Alqaraawi, Martin Schuessler, Philipp Weiß +2

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various expl…