42 citations · 139 across the 31 of their papers we have counts for
14 papers · 1 filter
Self-Supervised Dynamical System Representations for Physiological Time-Series
Yenho Chen, Maxwell A. Xu, James M. Rehg +1
The effectiveness of self-supervised learning (SSL) for physiological time series depends on the ability of a pretraining objective to preserve information about the underlying phy…
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…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings
Mithun Saha, Maxwell A. Xu, Wanting Mao +3
Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse he…
PyPulse: A Python Library for Biosignal Imputation
Kevin Gao, Maxwell A. Xu, James M. Rehg +1
We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from m…
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
Hui Wei, Maxwell A. Xu, Colin Samplawski +3
Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subje…