Infrared thermography (IRT) has gained significant popularity in biomedical instrumentation. One of its important applications is the contactless monitoring of the human respiration rate (RR). This paper reports application of IRT for human RR monitoring using a passive thermography approach. The variation in nostril temperature during breathing is observed using a thermal camera, and the breathing signals (BS) are obtained. The BS have a low signal to noise ratio (SNR), which is improved by using the Butterworth filter. The signals are monitored under different breathing and ambient conditions, and the heat exchange mechanisms occurring under these scenarios are discussed in detail. Further, the emissivity value of the human volunteers of different complexion and skin types is analyzed. The mean emissivity value obtained in the study is 0.98.
The respiration rate (RR) plays an important role in the determination of the human health condition. However, the presently used conventional RR techniques are contact-based processes that cause discomfort, skin damage, epidermal stripping, etc. They often pose problems for babies having delicate skin, making them vulnerable to skin infections. Also, the present day neonatal intensive care units are dark from the inside, which limits the use of optical technologies in the same. Infrared Thermography (IRT) is a safe and non-contact alternative, which overcomes these issues. This paper presents the application of passive IRT in monitoring the human RR. The breathing signals obtained are noisy and are filtered using the Butterworth filter. The “Ensemble of regression trees” computer vision algorithm is used to automate the tracking of nostrils in real-time, during object occlusion, and random head motion. The “Logistic regression classifier” is implemented to characterize the respiration rate of the volunteers as normal, abnormal, Bradypnea (slow breathing), or Tachypnea (fast breathing). The Validation accuracy, Training accuracy, and Testing accuracy of the classifier are obtained as 97.5%, 98%, and 95%, respectively. The Sensitivity, Specificity, Precision, G-mean, and F-measure are also computed. Further, the Standard deviation of the classier is obtained as 0.02.
Infrared thermography (IRT) has evolved as an important biomedical tool in recent years. One major application of IRT is the reliable monitoring of human respiration rate (RR) in a contactless manner. This method is especially useful in case of babies with delicate skin. The present work reports the human RR monitoring using passive IRT, by observing the variation in nasal temperature, during breathing. The observed breathing signal has a low signal to noise ratio (SNR), hence it is denoised using the Infinite Impulse Response (IIR) filters. The IIR filters are compared based on their SNR and Mean Square Error values. The Butterworth filter shows the best filtering performance amongst all the IIR filters, which further improves with increasing filter order. A novel “Breath detection algorithm" (BDA) is designed, that identifies the breaths in the acquired breathing signals as normal or abnormal, and yields the breaths per minute value, in an automated manner. The BDA is tested on 500 breathing signals under different scenarios like normal, slow and fast breathing, and with and without air conditioner and fan. The BDA performance is evaluated by calculating its sensitivity, precision, spurious cycle rate, and missed cycle rate values obtained as 98.4%, 99.19%, 0.80% and 1.6% respectively.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.