WildPPG: A Real-World PPG Dataset of Long Continuous Recordings

• URL: http://arxiv.org/abs/2412.17540v1
• Date: Mon, 23 Dec 2024 13:02:45 GMT
• Title: WildPPG: A Real-World PPG Dataset of Long Continuous Recordings
• Abstract summary: We show that state-of-the-art HR estimation methods struggle when processing emphrepresentative data from everyday activities in outdoor environments.<n>We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours.<n>We present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines.
• Score: 22.626523728464615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing \emph{representative} data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216\,hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571\,m above sea level) as well as using cars, trains, cable cars, and lifts for transport -- all of which impacted participants' physiological dynamics. We also present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines.

Related papers

• Analysis of Autonomic Regulation in Cancer Survivors During Daily Physical Activity: A Real-World Wearable ECG Study [1.2178992475191555]
  This study investigates heart rate (HR) and heart rate variability (HRV) responses to physical activity in breast cancer survivors using wearable electrocardiogram (ECG) data collected in real-world settings.
  arXiv  Detail & Related papers  (2026-06-22T15:10:17Z)
• Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables [8.222135147118921]
  We present Multi-site PPG, an in-the-wild physiological dataset collected from four custom-developed wearables.<n>Each device records green and infrared reflective PPG, 3-axis acceleration, and temperature with timestamps for cross-device alignment.<n>Participants wore the devices for multiple days during daytime activities while continuing their normal routines.
  arXiv  Detail & Related papers  (2026-05-18T05:04:52Z)
• Benchmarking Egocentric Visual-Inertial SLAM at City Scale [50.1245744173948]
  This paper introduces a new dataset and benchmark for visual-inertial SLAM with egocentric, multi-modal data.<n>We record hours and kilometers of trajectories through a city center with glasses-like devices equipped with various sensors.<n>We show that state-of-the-art systems developed by academia are not robust to these challenges and we identify components that are responsible for this.
  arXiv  Detail & Related papers  (2025-09-30T17:59:31Z)
• Non-Contact Health Monitoring During Daily Personal Care Routines [33.93756501373886]
  Remote photoplethysmography (r) enables non-contact, continuous monitoring of physiological signals.<n>We present the first long-term r learning dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants.<n>Experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring.
  arXiv  Detail & Related papers  (2025-06-11T13:29:21Z)
• TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection [1.9518933807303265]
  Photoplethysmography ( PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV) Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. We propose the Temporal Attentive U-Net, i.e., TAU, to accurately detect peaks from PPG signals.
  arXiv  Detail & Related papers  (2025-03-13T14:45:08Z)
• Passive Heart Rate Monitoring During Smartphone Use in Everyday Life [28.289115592689644]
  Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during everyday smartphone use.
  arXiv  Detail & Related papers  (2025-03-04T23:28:10Z)
• 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)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case Study [0.0]
  We propose an IoT system for monitoring an indoor ADL using thermal sensor array (TSA) Three classes of ADLs are introduced, which are daily activity, sleeping activity and no-activity respectively. Results have shown that the three activity classes can be identified as well as the persons average temperature during day and night.
  arXiv  Detail & Related papers  (2021-09-06T08:55:09Z)
• DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network [4.340040784481499]
  We define the term 'DigitalExposome' as a conceptual framework that takes us closer to understanding the relationship between environment, personal characteristics, behaviour and wellbeing. We simultaneously collected (for the first time) multisensor data including urban environmental factors.
  arXiv  Detail & Related papers  (2021-01-29T14:55:19Z)
• Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation [9.883261192383612]
  Remote photo-plethysmography (r) uses a camera to estimate a person's heart rate (HR) HRV is a measure of the fine fluctuations in the intervals between heart beats. We introduce a refined and efficient real-time r pipeline with novel filtering and motion suppression.
  arXiv  Detail & Related papers  (2020-12-31T18:57:05Z)
• Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning [0.20999222360659603]
  Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE.
  arXiv  Detail & Related papers  (2020-07-26T12:15:50Z)
• 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)
• AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching [76.4844593082362]
  We investigate the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong baseline for remote HR measurement with architecture search (NAS) Comprehensive experiments are performed on three benchmark datasets on both intra-temporal and cross-dataset testing.
  arXiv  Detail & Related papers  (2020-04-26T05:43:21Z)
• RF Sensing for Continuous Monitoring of Human Activities for Home Consumer Applications [13.353145284926986]
  We report on a successful RF sensing system for home monitoring applications. The system recognizes Activities of Daily Living(ADL) and detects unique motion characteristics. Finding both the transition times and the time-spans of the different motions leads to improved classifications.
  arXiv  Detail & Related papers  (2020-03-21T16:52:26Z)
• Pedestrian orientation dynamics from high-fidelity measurements [65.06084067891364]
  We propose a novel measurement method based on a deep neural architecture that we train on the basis of generic physical properties of the motion of pedestrians. We show that our method is capable of estimating orientation with an error as low as 7.5 degrees. This tool opens up new possibilities in the studies of human crowd dynamics where orientation is key.
  arXiv  Detail & Related papers  (2020-01-14T07:08:31Z)

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.