Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

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A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽

10.3390/s21113719 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3719

Author(s):

Aoxin Ni ◽

Arian Azarang ◽

Nasser Kehtarnavaz

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Video Camera ◽

Measurement Methods ◽

Rate Measurement ◽

Learning Methods ◽

The Public ◽

Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit

npj Digital Medicine ◽

10.1038/s41746-019-0199-5 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 7

Author(s):

Mauricio Villarroel ◽

Sitthichok Chaichulee ◽

João Jorge ◽

Sara Davis ◽

Gabrielle Green ◽

...

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Respiratory Rate ◽

Preterm Infants ◽

Neonatal Intensive Care ◽

Mean Absolute Error ◽

Vital Signs ◽

Absolute Error ◽

Video Camera ◽

The Mean

AbstractThe implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

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A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Sustainability ◽

10.3390/su12093760 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3760 ◽

Cited By ~ 1

Author(s):

Manuel Woschank ◽

Erwin Rauch ◽

Helmut Zsifkovits

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Scientific Literature ◽

Future Research ◽

Industrial Enterprises ◽

Smart Logistics ◽

Research Findings ◽

Ongoing Development ◽

Research Initiatives

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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Human Resource Management (HRM) and Information Technology (IT): Some Empirical Evidences in the Context of Saudi Arabia

Journal of Education and Vocational Research ◽

10.22610/jevr.v3i1.47 ◽

2012 ◽

Vol 3 (1) ◽

pp. 28-34

Author(s):

Razali Bin Mat

Keyword(s):

Cost Effectiveness ◽

Resource Management ◽

Human Resource Management ◽

Human Resource ◽

Future Research ◽

Implementation Gap ◽

Future Research Agenda ◽

It Implementation ◽

Research Findings ◽

Organizational Performances

This research is intended to examine the relationships among the use of IT by organizations, HRM performances on its functional tasks on productivity, efficiency, and cost-effectiveness, organizational performances, the IT implementation gap, HRM transformation gap, and organizational performances gap. The HRM practitioners who are registered as members of Arabian Society of Human Resource Management (ASHRM) were randomly selected as respondents to response to the questions in the questionnaire. All the four hypotheses were statistically supported. The use of IT had significant correlations with the HRM functional performances based on productivity, efficiency, and cost-effectiveness. In addition, the IT implementation gap proved to be significant to the overall performances of HRM functional task. Several practical implications from the research findings and future research agenda were discussed at the end of the report.

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Non-contact physiological monitoring of post-operative patients in the intensive care unit

npj Digital Medicine ◽

10.1038/s41746-021-00543-z ◽

2022 ◽

Vol 5 (1) ◽

Author(s):

João Jorge ◽

Mauricio Villarroel ◽

Hamish Tomlinson ◽

Oliver Gibson ◽

Julie L. Darbyshire ◽

...

Keyword(s):

Heart Rate ◽

Respiratory Rate ◽

Vital Sign ◽

Vital Signs ◽

Reference Value ◽

Absolute Error ◽

Video Camera ◽

Clinical Observations ◽

Operative Care ◽

Respiratory Events

AbstractProlonged non-contact camera-based monitoring in critically ill patients presents unique challenges, but may facilitate safe recovery. A study was designed to evaluate the feasibility of introducing a non-contact video camera monitoring system into an acute clinical setting. We assessed the accuracy and robustness of the video camera-derived estimates of the vital signs against the electronically-recorded reference values in both day and night environments. We demonstrated non-contact monitoring of heart rate and respiratory rate for extended periods of time in 15 post-operative patients. Across day and night, heart rate was estimated for up to 53.2% (103.0 h) of the total valid camera data with a mean absolute error (MAE) of 2.5 beats/min in comparison to two reference sensors. We obtained respiratory rate estimates for 63.1% (119.8 h) of the total valid camera data with a MAE of 2.4 breaths/min against the reference value computed from the chest impedance pneumogram. Non-contact estimates detected relevant changes in the vital-sign values between routine clinical observations. Pivotal respiratory events in a post-operative patient could be identified from the analysis of video-derived respiratory information. Continuous vital-sign monitoring supported by non-contact video camera estimates could be used to track early signs of physiological deterioration during post-operative care.

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Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

BMJ Innovations ◽

10.1136/bmjinnov-2019-000354 ◽

2020 ◽

Vol 6 (2) ◽

pp. 55-71 ◽

Cited By ~ 2

Author(s):

Stephanie Soon ◽

Hafdis Svavarsdottir ◽

Candice Downey ◽

David George Jayne

Keyword(s):

Blood Pressure ◽

Heart Rate ◽

Oxygen Saturation ◽

Vital Signs ◽

Wearable Devices ◽

Outpatient Setting ◽

Future Research ◽

Validity And Reliability ◽

Vital Signs Monitoring ◽

Improve Patient

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring.A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature.A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified.Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device.

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CEO Advice Seeking: An Integrative Framework and Future Research Agenda

Journal of Management ◽

10.1177/0149206319885430 ◽

2019 ◽

Vol 46 (6) ◽

pp. 771-805

Author(s):

Shenghui Ma ◽

Yasemin Y. Kor ◽

David Seidl

Keyword(s):

Research Agenda ◽

Chief Executive ◽

Strategic Decision ◽

Future Research ◽

Integrative Framework ◽

Future Research Agenda ◽

Advice Seeking ◽

The Board Of Directors ◽

Research Findings ◽

New Research

In this paper, we review the burgeoning but dispersed literature on chief executive officer (CEO) advice seeking, which has important effects on strategic decision making, the CEO’s and the board of directors’ effectiveness, and firms’ entrepreneurial orientation, innovativeness, and financial performance. We synthesize research findings about the key features of CEO advice seeking and its antecedents and outcomes across multiple levels of analysis. On the basis of our review, we identify important research gaps and develop a future research agenda that outlines new research questions and empirical foci that extend the current scope of analysis. We also highlight promising new theories and underutilized methods suitable for this area of research. With an integrative review and research agenda, we hope to stimulate cross-fertilization of different lines of inquiry and encourage new research that shines a spotlight on the remaining puzzles of CEO advice-seeking research.

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Influence of Crystalline Admixtures and Their Synergetic Combinations with Other Constituents on Autonomous Healing in Cracked Concrete—A Review

Materials ◽

10.3390/ma15020440 ◽

2022 ◽

Vol 15 (2) ◽

pp. 440

Author(s):

Yuanzhu Zhang ◽

Runwei Wang ◽

Zhi Ding

Keyword(s):

High Performance ◽

Research Progress ◽

Cement Matrix ◽

Future Research ◽

Self Healing ◽

Matrix Composites ◽

Expansive Agent ◽

Practical Engineering ◽

Research Findings ◽

Fundamental Research

Crystalline admixtures (CAs) are new materials for promoting self-healing in concrete materials to repair concrete cracks. They have been applied to tunnel, reservoir dam, road, and bridge projects. The fundamental research and development of CAs are needed concerning their practical engineering applications. This paper reviews the current research progress of commercial CAs, including self-made CA healing cracks; the composition of CA; healing reaction mechanism; the composition of healing products; distribution characteristics of healing products; the influence of service environment and crack characteristics on the healing performance of CA; and coupling healing performance of CA with fiber, expansive agent, and superabsorbent polymers. The current research findings are summarized, and future research recommendations are provided to promote the development of high-performance cement matrix composites.

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Design rationale and performance evaluation of Wavelet Health Wristband: bench-top validation of a wrist-worn physiological signal recorder (Preprint)

10.2196/preprints.11040 ◽

2018 ◽

Author(s):

Onur Dur ◽

Colleen Rhoades ◽

Sally Man Suen Ng ◽

Ragwa Elsayed ◽

Reinier van Mourik ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Respiration Rate ◽

Vital Signs ◽

High Signal ◽

Signal Quality ◽

Absolute Deviation ◽

Physiological Signal ◽

And Performance

BACKGROUND Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient and scalable way to collect personal health data remotely. The Wavelet Health Platform and the Wavelet Wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals including resting heart rate, heart rate variability, and respiration rate. OBJECTIVE This study aims to evaluate the accuracy of the biometrics estimates and signal quality of the wristband. METHODS Measurements collected from 35 subjects using the Wavelet Wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. RESULTS The heart rate, heart rate variability (SDNN) and respiration rate estimates matched within 0.6 ± 0.9 bpm, 7 ± 10 ms and 1 ± 1 brpm mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable to that obtained from measurements from a finger-clip plethysmography device. CONCLUSIONS The accuracy of the biometrics estimates and high signal quality indicate that the Wristband PPG device is suitable for performing pulse wave analysis and measuring vital signs.

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Reflecting on future research concerning the added value of FM

Facilities ◽

10.1108/f-09-2012-0070 ◽

2014 ◽

Vol 32 (13/14) ◽

pp. 856-870 ◽

Cited By ~ 7

Author(s):

Per Anker Jensen ◽

Theo J.M. van der Voordt ◽

Christian Coenen ◽

Anna-Liisa Sarasoja

Keyword(s):

Business Performance ◽

Added Value ◽

Future Research ◽

Facilities Management ◽

Strategic Partner ◽

Content Type ◽

Future Research Agenda ◽

The Future ◽

Research Findings ◽

Corporate Social

Purpose – This paper aims to summarize recent research findings and reflections on The Added Value of Facilities Management (FM) and to outline perspectives for future research and development of the added value of FM. Design/methodology/approach – The article is based on reflections on contributions to the recently published book “The Added Value of Facilities Management” and related future studies, as well as further exploration of five main themes. Findings – Added value is expected to be central in the future development of FM, which is confirmed by recent foresight studies. There is a need for a better understanding of alignment between FM and core business, performance measurement methods and how models such as the FM Value Map can be of value to the involved stakeholders. Corporate social responsibility (CSR), sustainability and branding have great potential to add value and to elevate FM to become a strategic partner with corporate top management. Management of stakeholders’ perception of value and relationships are essential aspects as well and need further attention. Research limitations/implications – The article is based on the conclusions of several studies that aimed to explore items for further research, on the ideas of all co-authors of “The Added Value of Facilities Management” anthology and on further exploration of five main themes, and not on an extensive review of recommendations for further research to be found in a huge number of research reports. Practical implications – The findings and ideas for further research on the added value of FM deliver input to further professionalization of FM. Originality/value – This paper provides important input to the future research agenda on the added value of FM and sheds new light on five particular research topics.

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