10 citations · 24 across the 12 of their papers we have counts for
9 papers · 1 filter
TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series
Aditya Radhakrishnan, Maxwell A. Xu, Wanting Mao +3
Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar. Because few augmentations reliably preserve meaning in t…
Variable Input Masked Autoencoders for Wrist-Worn Accelerometry
Harish Haresamudram, Wanting Mao, Aditya Radhakrishnan +2
Large-scale repositories of wearable sensor data have enabled the development of foundation models for human activities and behavior. However, existing models are pretrained using…
Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training
Richard Goldman, Varun Komperla, Thomas Ploetz +1
Discovering high performing model architectures for wearables-based Human Activity Recognition (HAR) applications is challenging. The astonishing diversity and variability due to d…
Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals
Wanting Mao, Maxwell A Xu, Harish Haresamudram +3
Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide…
Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition -- And Ways to Overcome Them
Harish Haresamudram, Apoorva Beedu, Mashfiqui Rabbi +3
Cross-modal contrastive pre-training between natural language and other modalities, e.g., vision and audio, has demonstrated astonishing performance and effectiveness across a dive…
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research
Harish Haresamudram, Hrudhai Rajasekhar, Nikhil Murlidhar Shanbhogue +1
The astonishing success of Large Language Models (LLMs) in Natural Language Processing (NLP) has spurred their use in many application domains beyond text analysis, including weara…
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Harish Haresamudram, Irfan Essa, Thomas Ploetz
Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived t…
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
Harish Haresamudram, Irfan Essa, Thomas Ploetz
The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in signifi…
Contrastive Predictive Coding for Human Activity Recognition
Harish Haresamudram, Irfan Essa, Thomas Ploetz
Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promisi…