ArticleSensors (Basel, Switzerland)2017
Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- From Motion Artifacts to Clinical Insight: Multi-Modal Deep Learning for Robust Arrhythmia Screening in Ambulatory ECG Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Synchronous post-exercise electrocardiogram, phonocardiogram, photoplethysmograms and seismocardiogram.Scientific data · 2025Article
- Clinical applications of smart wearable sensors.iScience · 2023Review
- Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation.Sensors (Basel, Switzerland) · 2023Article
- Edge-Enabled Heart Rate Estimation from Multisensor PPG Signals.Journal of healthcare engineering · 2023Article
- Article
- Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare.Sensors (Basel, Switzerland) · 2021Article
- Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review.Sensors (Basel, Switzerland) · 2020Review
- Practical and Durable Flexible Strain Sensors Based on Conductive Carbon Black and Silicone Blends for Large Scale Motion Monitoring Applications.Sensors (Basel, Switzerland) · 2019Article
- Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study.JMIR cardio · 2019Article
- Unobtrusive Vital Sign Monitoring in Automotive Environments-A Review.Sensors (Basel, Switzerland) · 2018Article
- Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNR S is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.