48 citations · 179 across the 37 of their papers we have counts for
38 papers
AdaNODEs: Test Time Adaptation for Time Series Forecasting Using Neural ODEs
Ting Dang, Soumyajit Chatterjee, Hong Jia +3
Test time adaptation (TTA) has emerged as a promising solution to adapt pre-trained models to new, unseen data distributions using unlabeled target domain data. However, most TTA m…
ConSensus: Multi-Agent Collaboration for Multimodal Sensing
Hyungjun Yoon, Mohammad Malekzadeh, Sung-Ju Lee +2
Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However, accurately interpreting heterog…
Listening to the Mind: Earable Acoustic Sensing of Cognitive Load
Xijia Wei, Ting Dang, Khaldoon Al-Naimi +3
Earable acoustic sensing offers a powerful and non-invasive modality for capturing fine-grained auditory and physiological signals directly from the ear canal, enabling continuous…
CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models
Yuxuan Shu, Peter H. Charlton, Fahim Kawsar +2
The electrocardiogram (ECG) is a key diagnostic tool in cardiovascular health. Single-lead ECG recording is integrated into both clinical-grade and consumer wearables. While self-s…
Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Famá, Roberto Pereira, Charalampos Kalalas +4
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored.…
AdaBet: Gradient-free Layer Selection for Efficient Training of Deep Neural Networks
Irene Tenison, Soumyajit Chatterjee, Fahim Kawsar +1
To utilize pre-trained neural networks on edge and mobile devices, we often require efficient adaptation to user-specific runtime data distributions while operating under limited c…
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
Taesik Gong, Fahim Kawsar, Chulhong Min
Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of t…
PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision
Arnav M. Das, Chi Ian Tang, Fahim Kawsar +1
Sensing human motions through Inertial Measurement Units (IMUs) embedded in personal devices has enabled significant applications in health and wellness. Labeled IMU data is scarce…
OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning
Alessandro Montanari, Ashok Thangarajan, Khaldoon Al-Naimi +4
Sensory earables have evolved from basic audio enhancement devices into sophisticated platforms for clinical-grade health monitoring and wellbeing management. This paper introduces…
SoundCollage: Automated Discovery of New Classes in Audio Datasets
Ryuhaerang Choi, Soumyajit Chatterjee, Dimitris Spathis +3
Developing new machine learning applications often requires the collection of new datasets. However, existing datasets may already contain relevant information to train models for…
PaPaGei: Open Foundation Models for Optical Physiological Signals
Arvind Pillai, Dimitris Spathis, Fahim Kawsar +1
Photoplethysmography (PPG) is the leading non-invasive technique for monitoring biosignals and cardiovascular health, with widespread adoption in both clinical settings and consume…
Using Self-supervised Learning Can Improve Model Fairness
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides +3
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and la…
An AI-Native Runtime for Multi-Wearable Environments
Chulhong Min, Utku Günay Acer, SiYoung Jang +5
The miniaturization of AI accelerators is paving the way for next-generation wearable applications within wearable technologies. We introduce Mojito, an AI-native runtime with adva…
Time-bound Contextual Bio-ID Generation for Minimalist Wearables
Adiba Orzikulova, Diana A. Vasile, Fahim Kawsar +1
As wearable devices become increasingly miniaturized and powerful, a new opportunity arises for instant and dynamic device-to-device collaboration and human-to-device interaction.…
Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras
Chulhong Min, Juheon Yi, Utku Gunay Acer +1
Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing visual anal…
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature…
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides +3
Self-supervised learning (SSL) has become the de facto training paradigm of large models where pre-training is followed by supervised fine-tuning using domain-specific data and lab…
Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables
Taesik Gong, Si Young Jang, Utku Günay Acer +2
The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrate…
Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing
Mohammad Malekzadeh, Fahim Kawsar
In split inference, a deep neural network (DNN) is partitioned to run the early part of the DNN at the edge and the later part of the DNN in the cloud. This meets two key requireme…
The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models
Dimitris Spathis, Fahim Kawsar
Large Language Models (LLMs) have demonstrated remarkable generalization across diverse tasks, leading individuals to increasingly use them as personal assistants and universal com…
The State of Algorithmic Fairness in Mobile Human-Computer Interaction
Sofia Yfantidou, Marios Constantinides, Dimitris Spathis +3
This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive ana…
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh +3
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approac…
Towards personalised music-therapy; a neurocomputational modelling perspective
Nicole Lai, Marios Philiastides, Fahim Kawsar +1
Music therapy has emerged recently as a successful intervention that improves patient's outcome in a large range of neurological and mood disorders without adverse effects. Brain n…
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Chi Ian Tang, Lorena Qendro, Dimitris Spathis +3
Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is intr…