Video motion magnification for monitoring of vital signals using a perceptual model

Brieva, Jorge; Moya-Albor, Ernesto; Gomez-Coronel, Sandra L.; Ponce, Hiram

doi:10.1117/12.2256971

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…The work presented in [43] used the Laplacian pyramid as a spatial decomposition. However, in recent works, the Hermite transform (HT) has demonstrated that it performs a better reconstruction of the magnified sequence [33,44] instead of the Laplacian approach.…”

Section: Motion Magnificationmentioning

confidence: 99%

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Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

2023

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The breathing rate monitoring is an important measure in medical applications and daily physical activities. The contact sensors have shown their effectiveness for breathing monitoring and have been mostly used as a standard reference, but with some disadvantages for example in burns patients with vulnerable skins. Contactless monitoring systems are then gaining attention for respiratory frequency detection. We propose a new non-contact technique to estimate the breathing rate based on the motion video magnification method by means of the Hermite transform and an Artificial Hydrocarbon Network (AHN). The chest movements are tracked by the system without the use of an ROI in the image video. The machine learning system classifies the frames as inhalation or exhalation using a Bayesian-optimized AHN. The method was compared using an optimized Convolutional Neural Network (CNN). This proposal has been tested on a Data-Set containing ten healthy subjects in four positions. The percentage error and the Bland–Altman analysis is used to compare the performance of the strategies estimating the breathing rate. Besides, the Bland–Altman analysis is used to search for the agreement of the estimation to the reference.The percentage error for the AHN method is 2.19±2.1 with and agreement with respect of the reference of ≈99%.

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Section: Motion Magnificationmentioning

confidence: 99%

“…Chebin et al during a spirometry study extracted the motion of the face and the shoulders [28]. Other works tracked the subtle motions using the magnification method, detecting the invisible motions due to the respiratory rhythm [29][30][31][32][33][34]. Different methods based on tracking color changes are proposed.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

2023

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“…As has been reviewed by Refs. [1,14], motion illusions like these can arise from local phase changes that are interpreted as global motions. In the engine example, the edge intensities are varying the most and are ampli ed more by the MEMAD lter than the more homogeneous parts of the engine.…”

Section: Motion Illusionmentioning

confidence: 99%

Real-time motion amplification on mobile devices

Voss

2022

Preprint

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A simple motion amplification algorithm suitable for real-time applications on mobile devices is presented. It is based on motion enhancement by moving average differencing (MEMAD), a temporal high-pass filter for video streams. MEMAD can amplify small moving objects or subtle motion in larger objects. It is computationally sufficiently simple to be implemented in real time on smartphones. In the specific implementation as an Android phone app, MEMAD is demonstrated on examples chosen such as to motivate applications in the engineering, biological, and medical sciences.

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“…Several works have used magnification video motion technique to track subtle motions [22]. This last technique allows revelation of invisible motions due to respiratory rhythm [23][24][25][26][27]. The third category uses hybrid techniques applying image-based and non-image-based approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Al-Najia and Chahl [30] present a remote respiratory monitoring system to magnify motion using the Wavelet decomposition obtaining smaller errors in RR estimation than with use of traditional Laplacian decomposition. Other works [27,32] show that the magnification Hermite approach allows a better reconstruction and a better robustness to the noise than traditional Laplacian decomposition used in [22]. The camera distance to the subject is an important parameter; in general, systems use short distances but as shown by Al-Najia et al [23], magnification techniques allow usage of long ranges for monitoring vital signs.…”

Section: Introductionmentioning

confidence: 99%

A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

2020

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The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for the automatic strategy is 3.28 ± 3.33% with and agreement with respect of the reference of ≈98%.

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Video motion magnification for monitoring of vital signals using a perceptual model

Cited by 4 publications

References 10 publications

Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

Real-time motion amplification on mobile devices

A Contactless Respiratory Rate Estimation Method Using a Hermite Magnification Technique and Convolutional Neural Networks

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