AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence

• URL: http://arxiv.org/abs/2508.03436v1
• Date: Tue, 05 Aug 2025 13:24:15 GMT
• Title: AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence
• Authors: Davide Gabrielli, Bardh Prenkaj, Paola Velardi, Stefano Faralli,
• Abstract summary: We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients.<n>Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns.
• Score: 4.494833548150712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce AI on the Pulse, a real-world-ready anomaly detection system that continuously monitors patients using a fusion of wearable sensors, ambient intelligence, and advanced AI models. Powered by UniTS, a state-of-the-art (SoTA) universal time-series model, our framework autonomously learns each patient's unique physiological and behavioral patterns, detecting subtle deviations that signal potential health risks. Unlike classification methods that require impractical, continuous labeling in real-world scenarios, our approach uses anomaly detection to provide real-time, personalized alerts for reactive home-care interventions. Our approach outperforms 12 SoTA anomaly detection methods, demonstrating robustness across both high-fidelity medical devices (ECG) and consumer wearables, with a ~ 22% improvement in F1 score. However, the true impact of AI on the Pulse lies in @HOME, where it has been successfully deployed for continuous, real-world patient monitoring. By operating with non-invasive, lightweight devices like smartwatches, our system proves that high-quality health monitoring is possible without clinical-grade equipment. Beyond detection, we enhance interpretability by integrating LLMs, translating anomaly scores into clinically meaningful insights for healthcare professionals.

Related papers

• Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models [87.66870367661342]
  Large language models (LLMs) are used in AI applications in healthcare.<n>Red-teaming framework that continuously stress-test LLMs can reveal significant weaknesses in four safety-critical domains.<n>A suite of adversarial agents is applied to autonomously mutate test cases, identify/evolve unsafe-triggering strategies, and evaluate responses.<n>Our framework delivers an evolvable, scalable, and reliable safeguard for the next generation of medical AI.
  arXiv  Detail & Related papers  (2025-07-30T08:44:22Z)
• Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG Monitors [2.03647070634706]
  Heart2Mind is a human-centered contestable psychiatric disorder diagnosis system using wearable electrocardiogram (ECG) monitors.<n>The system comprises three key components: (1) a Cardiac Monitoring Interface (CMI) for real-time data acquisition from Polar H9/H10 devices; (2) a Multi-Scale Temporal-Frequency Transformer (MSTFT) that processes RRI time series through integrated time-frequency domain analysis; and (3) a Contestable Diagnosis Interface (CDI) combining Self-Adversarial Explanations (SAEs) with contestable Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2025-05-16T18:21:08Z)
• A Vector-Quantized Foundation Model for Patient Behavior Monitoring [43.02353546717171]
  This paper introduces a novel foundation model based on a modified vector quantized variational autoencoder, specifically designed to process real-world data from smartphones and wearable devices.<n>We leveraged the discrete latent representation of this model to effectively perform two downstream tasks, suicide risk assessment and emotional state prediction, on different held-out clinical cohorts without the need of fine-tuning.
  arXiv  Detail & Related papers  (2025-03-19T14:01:16Z)
• Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings [0.0]
  This study introduces an AI-driven platform for continuous and passive patient monitoring in hospital settings.<n>The platform provides real-time insights into patient behavior and interactions through video analysis.<n>The dataset, compiled in collaboration with 11 hospital partners, encompasses over 300 high-risk fall patients.
  arXiv  Detail & Related papers  (2024-12-17T18:23:33Z)
• Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences [4.308104021015939]
  This paper presents a methodology for stress detection from a wristband based on a universal model for time series, UniTS.<n>We show that our proposed model considerably surpasses 12 top-performing methods on three benchmark datasets.
  arXiv  Detail & Related papers  (2024-07-04T10:46:09Z)
• VAE-IF: Deep feature extraction with averaging for fully unsupervised artifact detection in routinely acquired ICU time-series [1.9665926763554147]
  We propose a novel fully unsupervised approach to detect artifacts in minute-by-minute resolution ICU data without prior labeling or signal-specific knowledge. Our approach combines a variational autoencoder (VAE) and an isolation forest (IF) into a hybrid model to learn features and identify anomalies. We show that our unsupervised approach achieves comparable sensitivity to fully supervised methods and generalizes well to an external dataset.
  arXiv  Detail & Related papers  (2023-12-10T18:03:40Z)
• VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG [8.100646331930953]
  An accurate and efficient epileptic seizure onset detection can significantly benefit patients. Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), often result in cumbersome and non-portable solutions. We propose a novel Video-based Seizure detection model via a skeleton-basedtemporal Vision Graph neural network.
  arXiv  Detail & Related papers  (2023-11-24T15:07:29Z)
• New Epochs in AI Supervision: Design and Implementation of an Autonomous Radiology AI Monitoring System [5.50085484902146]
  We introduce novel methods for monitoring the performance of radiology AI classification models in practice. We propose two metrics - predictive divergence and temporal stability - to be used for preemptive alerts of AI performance changes.
  arXiv  Detail & Related papers  (2023-11-24T06:29:04Z)
• Electrical Grid Anomaly Detection via Tensor Decomposition [41.94295877935867]
  Previous work has shown that dimensionality reduction-based approaches can be used for accurate identification of anomalies in SCADA systems. In this work, we novelly apply the tensor decomposition method Canonical Polyadic Alternating Poisson Regression with a probabilistic framework, to identify anomalies in SCADA systems. In our experiments, we model real-world SCADA system data collected from the electrical grid operated by Los Alamos National Laboratory.
  arXiv  Detail & Related papers  (2023-10-12T18:23:06Z)
• Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection [43.905374104261014]
  We propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student) They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. Our experiments show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data.
  arXiv  Detail & Related papers  (2023-04-03T15:37:40Z)
• Reducing a complex two-sided smartwatch examination for Parkinson's Disease to an efficient one-sided examination preserving machine learning accuracy [63.20765930558542]
  We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD) This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
  arXiv  Detail & Related papers  (2022-05-11T09:12:59Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)

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.