Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals

• URL: http://arxiv.org/abs/2412.09758v2
• Date: Fri, 16 May 2025 05:24:58 GMT
• Title: Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals
• Authors: Yunfei Luo, Yuliang Chen, Asif Salekin, Tauhidur Rahman,
• Abstract summary: We propose the first multi-modal and ubiquitous foundation model designed to extract informative representations from wearable sensing data.<n>Specifically, we design a channel-aware attention mechanism with a shared special liaison token to detect signal patterns in both intra-sensor and inter-sensors.<n>Our model shows exceptional generalizability across 11 public wearable sensing datasets, spanning 18 applications in mental health, body state inference, vital sign estimation, and disease risk evaluation.
• Score: 2.370585289844609
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time-series foundation models excel at tasks like forecasting across diverse data types by leveraging informative waveform representations. Wearable sensing data, however, pose unique challenges due to their variability in patterns and frequency bands, especially for healthcare-related outcomes. The main obstacle lies in crafting generalizable representations that adapt efficiently across heterogeneous sensing configurations and applications. To address this, we propose NormWear, the first multi-modal and ubiquitous foundation model designed to extract generalized and informative representations from wearable sensing data. Specifically, we design a channel-aware attention mechanism with a shared special liaison [CLS] token to detect signal patterns in both intra-sensor and inter-sensors. This helps the model to extract more meaningful information considering both time series themselves and the relationships between input sensors. This helps the model to be widely compatible with various sensors settings. NormWear is pretrained on a diverse set of physiological signals, including PPG, ECG, EEG, GSR, and IMU, from various public datasets. Our model shows exceptional generalizability across 11 public wearable sensing datasets, spanning 18 applications in mental health, body state inference, vital sign estimation, and disease risk evaluation. It consistently outperforms competitive baselines under zero-shot, partial-shot, and full-shot settings, indicating broad applicability in real-world health applications.

Related papers

• Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals [36.08910150609342]
  We present a cardiac sensing foundation model (CSFM) that learns unified representations from vast, heterogeneous health records.<n>Our model is pretrained on an innovative multi-modal integration of data from multiple large-scale datasets.<n> CSFM consistently outperforms traditional one-modal-one-task approaches.
  arXiv  Detail & Related papers  (2025-06-23T20:58:12Z)
• Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities [9.785262633953794]
  Physio Omni is a foundation model for multimodal physiological signal analysis. It trains a decoupled multimodal tokenizer, enabling masked signal pre-training. It achieves state-of-the-art performance while maintaining strong robustness to missing modalities.
  arXiv  Detail & Related papers  (2025-04-28T09:00:04Z)
• Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics [8.692647930497936]
  We use conformal analysis to quantify the predictive uncertainty of a vision transformer based foundation model. We show how this can be used as a fairness metric to evaluate the robustness of the feature embeddings of the foundation model.
  arXiv  Detail & Related papers  (2025-03-31T08:06:00Z)
• Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification [25.47662257105448]
  We introduce Sparseformer, a transformer specifically designed for MedTS classification.<n>We propose a sparse token-based dual-attention mechanism that enables global modeling and token compression.<n>Our model outperforms 12 baselines across seven medical datasets under supervised learning.
  arXiv  Detail & Related papers  (2025-03-19T13:22:42Z)
• Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs [0.0]
  This study is the first to explore the feasibility of synthesizing motion gestures for allergic rhinitis from wearable IoT device data using Generative Adversarial Networks (GANs)<n>We also focus on these AI models' performance in terms of fidelity, diversity, and privacy.
  arXiv  Detail & Related papers  (2024-12-09T11:15:47Z)
• Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine [0.0]
  This work introduces a systematic pipeline for evaluating foundation models' transfer capabilities in medical contexts.<n>First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios.<n>Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods.<n>Third, the pipeline validates these representations through specific downstream medical tasks.
  arXiv  Detail & Related papers  (2024-12-04T16:17:09Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition [9.072495000412943]
  We introduce SensorLLM, a framework that enables Large Language Models (LLMs) to perform human activity recognition (HAR) from wearable sensor data.<n>We construct SensorQA, a question-answering dataset of human-intuitive sensor-text pairs spanning diverse HAR scenarios.<n>Our results show that, guided by human-intuitive alignment, SensorLLM becomes an effective sensor learner, reasoner, and classifier--generalizing across varied HAR settings.
  arXiv  Detail & Related papers  (2024-10-14T15:30:41Z)
• HMF: A Hybrid Multi-Factor Framework for Dynamic Intraoperative Hypotension Prediction [2.7807763048110337]
  Intraoperative hypotension (IOH) prediction using Mean Arterial Pressure (MAP) is a critical research area with significant implications for patient outcomes during surgery. Existing approaches predominantly employ static modeling paradigms that overlook the dynamic nature of physiological signals. We introduce a novel Hybrid Multi-Factor (HMF) framework that reformulates IOH prediction as a blood pressure forecasting task.
  arXiv  Detail & Related papers  (2024-09-17T10:46:41Z)
• Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications [0.7499722271664147]
  Biosignal acquisition is key for healthcare applications and wearable devices. Existing solutions often require large and expensive datasets and/or lack robustness and interpretability. We propose the Spatial Adaptation Layer (SAL), which can be prepended to any biosignal array model. We also introduce learnable baseline normalization (LBN) to reduce baseline fluctuations.
  arXiv  Detail & Related papers  (2024-09-12T14:06:12Z)
• Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models [8.798959872821962]
  This paper outlines an approach in the domain of federated survival analysis, specifically the Cox Proportional Hazards (CoxPH) model. We present an FL approach that employs feature-based clustering to enhance model accuracy across synthetic datasets and real-world applications.
  arXiv  Detail & Related papers  (2024-07-20T18:34:20Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction [27.521188262343596]
  We introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN)<n>THGNN aggregates historical data from neighboring nodes to accurately capture the temporal dynamics and spatial correlations within the stream of sensor data.<n>We have validated the effectiveness of our approach through comprehensive experiments.
  arXiv  Detail & Related papers  (2024-05-07T14:08:57Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Latent Temporal Flows for Multivariate Analysis of Wearables Data [0.9990687944474738]
  We introduce Latent Temporal Flows, a method for multivariate time-series modeling tailored to this setting. We show that the proposed method consistently outperforms the state-of-the-art in multi-step forecasting benchmarks.
  arXiv  Detail & Related papers  (2022-10-14T02:54:34Z)
• Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications [60.26261850082012]
  We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
  arXiv  Detail & Related papers  (2022-09-29T17:44:14Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.