Non-Invasive Vital Sign Monitoring

Latest papers

• Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation

  Qian Hong, Cheng Bian, Xiao Zhou, Xiaoyu Li, Yelei Li, Zijing Zeng · 27 November 2025

  Translating non-invasive signals such as photoplethysmography (PPG) and ballistocardiography (BCG) into clinically meaningful signals like arterial blood pressure (ABP) is vital for continuous, low-cost healthcare monitoring. However, temporal misalignment in multimodal signal transformation impairs…

• Towards Characterizing Knowledge Distillation of PPG Heart Rate Estimation Models

  Kanav Arora, Girish Narayanswamy, Shwetak Patel, Richard Li · 25 November 2025

  Heart rate estimation from photoplethysmography (PPG) signals generated by wearable devices such as smartwatches and fitness trackers has significant implications for the health and well-being of individuals. Although prior work has demonstrated deep learning models with strong performance in the he…

• A Nutrition Multimodal Photoplethysmography Language Model

  Kyle Verrier, Achille Nazaret, Joseph Futoma, Andrew C. Miller, Guillermo Sapiro · 25 November 2025

  Hunger and satiety dynamics shape dietary behaviors and metabolic health, yet remain difficult to capture in everyday settings. We present a Nutrition Photoplethysmography Language Model (NPLM), integrating continuous photoplethysmography (PPG) from wearables with meal descriptions. NPLM projects PP…

• Structured Contrastive Learning for Interpretable Latent Representations

  Zhengyang Shen, Hua Tu, Mayue Shi · 20 November 2025

  Neural networks exhibit severe brittleness to semantically irrelevant transformations. A mere 75ms electrocardiogram (ECG) phase shift degrades latent cosine similarity from 1.0 to 0.2, while sensor rotations collapse activity recognition performance with inertial measurement units (IMUs). We identi…

• Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters

  Emanuele Palumbo, Sorawit Saengkyongam, Maria R. Cervera, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Christina Heinze-Deml, Antoine Wehenkel · 19 November 2025

  Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output, require invasive measurements, e.g., arterial pressure waveforms (APW). As a non-invasive alternative, photoplethysmogra…

• Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity

  Xiaoyu Zheng, Sijung Hu, Vincent Dwyer, Mahsa Derakhshani, Laura Barrett · 12 November 2025

  Opto-physiological monitoring including photoplethysmography (PPG) provides non-invasive cardiac and respiratory measurements, yet motion artefacts (MAs) during physical activity degrade its signal quality and downstream estimation concurrently. An attention-mechanism-based generative adversarial ne…

• PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation

  Juntong Ni, Saurabh Kataria, Shengpu Tang, Carl Yang, Xiao Hu, Wei Jin · 10 November 2025

  Photoplethysmography (PPG) is widely used in wearable health monitoring, yet large PPG foundation models remain difficult to deploy on resource-limited devices. We present PPG-Distill, a knowledge distillation framework that transfers both global and local knowledge through prediction-, feature-, an…

• Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors

  Assma Habadi, Milos Zefran, Lijuan Yin, Woojin Song, Maria Caceres, Elise Hu, Naoko Muramatsu · 10 November 2025

  Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshot…

• Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification

  Yangyang Zhao, Matti Kaisti, Olli Lahdenoja, Tero Koivisto · 4 November 2025

  Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm monitoring, yet suffers from significant vulnerability to motion artifacts and physiological noise. Many existing appro…

• A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis

  Ciaran Bench, Oskar Pfeffer, Vivek Desai, Mohammad Moulaeifard, Lo\"ic Coquelin, Peter H. Charlton, Nils Strodthoff, Nando Hegemann, Philip J. Aston, Andrew Thompson · 4 November 2025

  In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable risk of poor performance when deployed in pr…

• PULSE: Privileged Knowledge Transfer from Electrodermal Activity to Low-Cost Sensors for Stress Monitoring

  Zihan Zhao, Masood Mortazavi, Ning Yan · 29 October 2025

  Electrodermal activity (EDA), the primary signal for stress detection, requires costly hardware often unavailable in real-world wearables. In this paper, we propose PULSE, a framework that utilizes EDA exclusively during self-supervised pretraining, while enabling inference without EDA but with more…

• Path-specific effects for pulse-oximetry guided decisions in critical care

  Kevin Zhang, Yonghan Jung, Divyat Mahajan, Karthikeyan Shanmugam, Shalmali Joshi · 28 October 2025

  Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent suppleme…