arXiv Machine Learning

Low-cost Embedded Breathing Rate Determination Using 802.15.4z IR-UWB Hardware for Remote Healthcare

arXiv:2504. 03772v3 Announce Type: replace-cross Abstract: Respiratory diseases account for a significant portion of global mortality.

arXiv Machine Learning

Jul 17

Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices

arXiv:2607. 14747v1 Announce Type: cross Abstract: Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis.

By Floriaan Bulten, Yawar Rasheed, Arlene John, Vincenzo Stoico, Ghayoor Gillani

benchmarks

arXiv Machine Learning

Jul 28

On-Device Inference versus Wireless Streaming: Energy-Efficient Multi-Modal Deep Learning for Wearable Cardiovascular Patches

arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.

By Mustafa Fuad Rifet Ibrahim, Tunc Alkanat, Felix Manthey, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger

multimodal benchmarks

arXiv AI

Jun 3

WISE-HAR: A Generalizable Ensemble Deep Learning Framework for WiFi-Based Human Activity Recognition

arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.

By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad

safety

arXiv Machine Learning

Jun 9

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection

arXiv:2602. 08916v3 Announce Type: replace-cross Abstract: Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema.

By Abu Masum, Mehran Moghadam, M. Hassan Najafi, Bige Unluturk, Ulkuhan Guler, Beth A. Beidleman, Sercan Aygun

arXiv Machine Learning

Jul 14

Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing

arXiv:2508. 02349v2 Announce Type: replace-cross Abstract: Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and performance and ultimately improve equine welfare.

By Jeanne I. M. Parmentier (Utrecht University, University of Twente, Inertia Technology B.V), Rhana M. Aarts (Utrecht University), Elin Hernlund (Swedish University of Agricultural Sciences), Marie Rhodin (Swedish University of Agricultural Sciences), Berend Jan van der Zwaag (University of Twente, Inertia Technology B.V)

arXiv Machine Learning

Jul 2

Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

arXiv:2508. 11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia.

By Zahra Mohammadi, Parnian Fazel, Siamak Mohammadi

efficiency

arXiv Machine Learning

Jun 2

ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems

arXiv:2606. 02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems.

By Nagarajan S, Kurian Polachan

computer-vision efficiency safety

Hugging Face Trending Papers

Jun 22

Deep learning-based detection of cessation of breathing in pre-term infants

Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate and oxygen saturation thresholds, often generating high false-positive alarm rates and missing short or irregular events.

multimodal

arXiv Machine Learning

Jun 26

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.

By Andrei Velichko, Mehmet Tahir Huyut

arXiv Machine Learning

Jul 3

Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals

arXiv:2509. 11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.

By Milan Marocchi, Matthew Fynn, Kayapanda Mandana, Yue Rong

llms diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 2

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.

By Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed

benchmarks

arXiv AI

Jul 14

ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification

arXiv:2607. 09680v1 Announce Type: cross Abstract: Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware.

By Anh Tran, Khanh Tran, Cuong Do

efficiency benchmarks

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