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

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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An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments

Sensors ◽

10.3390/s19132955 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2955 ◽

Cited By ~ 7

Author(s):

Saif Saad Fakhrulddin ◽

Sadik Kamel Gharghan ◽

Ali Al-Naji ◽

Javaan Chahl

Keyword(s):

Heart Rate ◽

Urban Areas ◽

Mean Absolute Error ◽

Fall Detection ◽

Absolute Error ◽

First Aid ◽

Elderly Persons ◽

Rate Measurement ◽

Time Saving ◽

Heart Rate Measurement

For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10−5° and 2.01 × 10−5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.

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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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On the target area tracking method for heart rate measurement using deep learning strategy

Eleventh International Conference on Digital Image Processing (ICDIP 2019) ◽

10.1117/12.2539746 ◽

2019 ◽

Author(s):

Jiancheng Zou ◽

Sai Zhang ◽

Bailin Ge

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Learning Strategy ◽

Target Area ◽

Rate Measurement ◽

Tracking Method ◽

Heart Rate Measurement

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Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement

2019 IEEE/CVF International Conference on Computer Vision (ICCV) ◽

10.1109/iccv.2019.00024 ◽

2019 ◽

Cited By ~ 13

Author(s):

Zitong Yu ◽

Wei Peng ◽

Xiaobai Li ◽

Xiaopeng Hong ◽

Guoying Zhao

Keyword(s):

Heart Rate ◽

Deep Learning ◽

Video Enhancement ◽

Rate Measurement ◽

Heart Rate Measurement ◽

End To End

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Comparison between GPS data collection and heart rate measurement methods to estimate energy expenditure of grazing cows

Nihon Chikusan Gakkaiho ◽

10.2508/chikusan.84.67 ◽

2013 ◽

Vol 84 (1) ◽

pp. 67-76 ◽

Cited By ~ 1

Author(s):

Yasuhiro NAKAGAWA ◽

Kazato OISHI ◽

Hiromichi MAENO ◽

Mikinori HIRANO ◽

Masayuki YOSHIOKA ◽

...

Keyword(s):

Heart Rate ◽

Data Collection ◽

Energy Expenditure ◽

Measurement Methods ◽

Gps Data ◽

Rate Measurement ◽

Heart Rate Measurement

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Combining Post Sentiments and User Participation for Extracting Public Stances from Twitter

Applied Sciences ◽

10.3390/app10228035 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8035

Author(s):

Jenq-Haur Wang ◽

Ting-Wei Liu ◽

Xiong Luo

Keyword(s):

Social Media ◽

Deep Learning ◽

Short Term Memory ◽

Absolute Error ◽

User Participation ◽

Sentiment Classification ◽

Context Dependency ◽

Long Distance ◽

Learning Methods ◽

The Public

With the wide popularity of social media, it’s becoming more convenient for people to express their opinions online. To better understand what the public think about a topic, sentiment classification techniques have been widely used to estimate the overall orientation of opinions in post contents. However, users might have various degrees of influence depending on their participation in discussions on different topics. In this paper, we address the issues of combining sentiment classification and link analysis techniques for extracting stances of the public from social media. Since social media posts are usually very short, word embedding models are first used to learn different word usages in various contexts. Then, deep learning methods such as Long Short-Term Memory (LSTM) are used to learn the long-distance context dependency among words for better estimation of sentiments. Third, we consider the major user participation in popular social media by adjusting the users weights to reflect their relative influence in user-post interaction graphs. Finally, we combine post sentiments and user influences into a total opinion score for extracting public stances. In the experiments, we evaluated the performance of our proposed approach for tweets about the 2016 U.S. Presidential Election. The best performance of sentiment classification can be observed with an F-measure of 72.97% for LSTM classifiers. This shows the effectiveness of deep learning methods in learning word usage in social media contexts. The experimental results on stance extraction showed the best performance of 0.68% Mean Absolute Error (MAE) in aggregating public stances on election candidates. This shows the potential of combining tweet sentiments and user participation structures for extracting the aggregate stances of the public on popular topics. Further investigation is needed to verify the performance in different social media sources.

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