The motion influence on respiration rate estimation from low-resolution thermal sequences during attention focusing tasks

Kwaśniewska, Alicja; Rumiński, Jacek; Wtorek, Jerzy

doi:10.1109/embc.2017.8037100

Cited by 4 publications

(2 citation statements)

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“…Taking it into account, the future work will focus on reducing the network size by using smaller inputs and deployment of better techniques for increasing the receptive field, i.e., atrous convolutions [ 51 ], or depth-separable convolutions [ 52 ]. It would be also important to analyze the influence of motion on the quality of the extracted signal [ 53 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

Szankin,

Kwasniewska,

Ruminski

2023

J. Imaging

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As healthcare costs continue to rise, finding affordable and non-invasive ways to monitor vital signs is increasingly important. One of the key metrics for assessing overall health and identifying potential issues early on is respiratory rate (RR). Most of the existing methods require multiple steps that consist of image and signal processing. This might be difficult to deploy on edge devices that often do not have specialized digital signal processors (DSP). Therefore, the goal of this study is to develop a single neural network realizing the entire process of RR estimation in a single forward pass. The proposed solution builds on recent advances in video recognition, capturing both spatial and temporal information in a multi-path network. Both paths process the data at different sampling rates to capture rapid and slow changes that are associated with differences in the temperature of the nostril area during the breathing episodes. The preliminary results show that the introduced end-to-end solution achieves better performance compared to state-of-the-art methods, without requiring additional pre/post-processing steps and signal-processing techniques. In addition, the presented results demonstrate its robustness on low-resolution thermal video sequences that are often used at the embedded edge due to the size and power constraints of such systems. Taking that into account, the proposed approach has the potential for efficient and convenient respiratory rate estimation across various markets in solutions deployed locally, close to end users.

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Section: Discussionmentioning

confidence: 99%

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

Szankin,

Kwasniewska,

Ruminski

2023

J. Imaging

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“…The NIH Chest X-ray dataset [31] was used in this study. It contains over 100,000 samples gathered from over 30,000 patients, many of whom have been identified with advanced lung diseases.…”

Section: Datasetmentioning

confidence: 99%

Can AI See Bias in X-ray Images?

Szankin

Kwaśniewska²

2022

IJNDI

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Article Can AI See Bias in X-ray images? Kwasniewska Alicja *, and Szankin Maciej * 1 SiMa Technologies Inc. 226 Airport Parkway, Suite 550 San Jose, CA 95110, United States 2 Intel Corporation 16409 W Bernardo Dr Suite 100, San Diego, CA 92127, United States * Correspondence: alicja.kwasniewska@sima.ai; maciej.szankin@intel.com Received: 8 November 2022 Accepted: 13 November 2022 Published: 22 December 2022 Abstract: Recent advances in artificial intelligence (AI) have shown promising results in various image-based systems, improving accuracy and throughput, while reducing latency. All these factors are crucial in healthcare and have generated increased interest in this technology. However, there are also multiple challenges integrating AI in existing systems, such as poor explainability, data imbalance and bias. These challenges affect the reliability of the neural networks used in AI applications. The limitations may significantly affect the quality and cost of medical care by introducing false positive diagnosis. The false positives subsequently lead to increased stress in patients and necessitate additional testing and procedures. Lack of rich data representing all socio-economic groups can also undermine reliable decisions for underrepresented groups. Although various studies discussed techniques that may help with bias mitigation, to the best of our knowledge, no practical experiments have been conducted so far that compare different reweighting approaches using convolutional neural networks (CNN). This work focuses on in-depth explanatory analysis of chest X-ray datasets to understand and quantify the problem of class imbalance and bias. After that, various topologies of binary classifications are compared, followed by practical applications of loss reweighting techniques and comparison of their influence of privileged, underprivileged, and overall population. Experiments proved that high classification accuracy can be achieved using an efficient model topology suitable for embedded devices, making it possible to run locally without the need for cloud processing. Preliminary results showed that performance of the model for the underprivileged class can be improved by 15% if proper weighting factors are obtained and applied during the training procedure.

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Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review

Harford

Catherall²,

Gerry

et al. 2019

Physiol. Meas.

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Objective: Over the last 15 years, developments in camera technology have coincided with increased availability and affordability. This has led to an increasing interest in using these technologies in healthcare settings. Image-based monitoring methods potentially allow multiple vital signs to be measured concurrently using a non-contact sensor. We have undertaken a systematic review of the current availability and performance of these monitoring methods. Approach: A multiple database search was conducted using MEDLINE, Embase, CINAHL, Cochrane Library, OpenGrey, IEEE Xplore Library and ACM Digital Library to July 2018. We included studies comparing image-based heart rate, respiratory rate, oxygen saturation and blood pressure monitoring methods against one or more validated reference device(s). Each included study was assessed using the modified GRRAS criteria for reporting bias. Main results: Of 30 279 identified studies, 161 were included in the final analysis. Twenty studies (20/161, 12%) were carried out on patients in clinical settings, while the remainder were conducted in academic settings using healthy volunteer populations. The 18–40 age group was best represented across the identified studies. One hundred and twenty studies (120/161, 75%) estimated heart rate, followed by 62 studies (62/161, 39%) estimating respiratory rate. Fewer studies focused on oxygen saturation (11/161, 7%) or blood pressure (6/161, 4%) estimation. Fifty-one heart rate studies (51/120, 43%) and 24 respiratory rate studies (24/62, 39%) used Bland–Altman analysis to report their results. Of the heart rate studies, 28 studies (28/51, 55%) showed agreement within industry standards of 5 beats per minute. Only two studies achieved this within clinical settings. Of the respiratory rate studies, 13 studies (13/24, 54%) showed agreement within industry standards of 3 breaths per minute, but only one study achieved this in a clinical setting. Statistical analysis was heterogeneous across studies with frequent inappropriate use of correlation. The majority of studies (99/161, 61%) monitored subjects for under 5 min. Three studies (3/161, 2%) monitored subjects for over 60 min, all of which were conducted in hospital settings. Significance: Heart rate and respiratory rate monitoring using video images is currently possible and performs within clinically acceptable limits under experimental conditions. Camera-derived estimates were less accurate in the proportion of studies conducted in clinical settings. We would encourage thorough reporting of the population studied, details of clinically relevant aspects of methodology, and the use of appropriate statistical methods in future studies. Systematic review registration: PROSPERO CRD42016029167 Protocol: https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-017-0615-3

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The motion influence on respiration rate estimation from low-resolution thermal sequences during attention focusing tasks

Cited by 4 publications

References 14 publications

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

Can AI See Bias in X-ray Images?

Availability and performance of image-based, non-contact methods of monitoring heart rate, blood pressure, respiratory rate, and oxygen saturation: a systematic review

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