Development of a thermographic image instrument using the raspberry Pi embedded system for the study of the diabetic foot

Bayareh, Rafael; Vera, A.; Leija, L.; Gutierrez, J.

doi:10.1109/i2mtc.2018.8409841

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…The device was developed with a Raspberry Pi card as an embedded system, a touch screen and a Lepton Flir sensor as an IR (Infrared) sensor. The codes were established in SPI and I 2 C communications between the computer and the sensor [21]. The system was programmed in C++ with Qt, designing also a database to store the collected information.…”

Section: Infrared Emission Thermography Systemmentioning

confidence: 99%

“…To comply with the creation of a useful database, quantitative data were chosen to support the thermographic images obtained. Each patient has information of vital signs, glycated hemoglobin, glucose, weight and height, as well as the writing of a medical note in which the responsible physician describes the observations at the time of the study to obtain a comparative record of the patient's evolution [21]. In Figure 3b, we can see a photograph captured with this system of a patient in follow-up of a diabetic foot injury.…”

Section: Infrared Emission Thermography Systemmentioning

confidence: 99%

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Proposal of a Non-Invasive Measurement of Physical Properties of Tissues in Patients with Diabetic Foot: Measurement Experiences in Diagnosed Patients

et al. 2023

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Diabetic foot is one of the most serious complications in patients with diabetes mellitus. It is distinguished by the development of ulcerations on the sole of the foot. Before the appearance of these, patients with diabetes suffer changes in the tissues of the foot and nearby tissues. This work proposes systems that measure and identify the changes in the physical characteristics of the foot tissues in two study groups, diabetic patients and healthy subjects, with the purpose of proposing tools to physicians to follow up each patient and identify with certainty the evolution in symptoms. The results of the temperature systems show that there is an average temperature difference of ~2 °C between diabetic patients and healthy subjects. Using an electrical impedance system, a frequency window was found between 5 kHz and 22 kHz, where the impedance is significantly different (p > 0.001) between diabetic and healthy patients. The system oriented to macules on the skin is able to identify the type of macules developed by the diabetic patient. In temperature measurement with images from a smartphone, plantar temperature monitoring was achieved in at-risk areas in uncontrolled environments. The results presented in this work were obtained in a time period from 2014 to 2022. Considering the standardization of this equipment when making a diagnosis regarding the study of tissues in the diabetic foot, it will be possible to detect it early. Through differences between the measurements, we have an indicator of the patient’s evolution, and we must highlight that these systems are easy to install, easy to interpret and low cost. Currently, there are no systems with these characteristics, which is why the early detection of diabetic foot is being widely studied.

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Section: Infrared Emission Thermography Systemmentioning

confidence: 99%

Section: Infrared Emission Thermography Systemmentioning

confidence: 99%

Proposal of a Non-Invasive Measurement of Physical Properties of Tissues in Patients with Diabetic Foot: Measurement Experiences in Diagnosed Patients

et al. 2023

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“…IRT is an alternative method for monitoring superficial body temperatures to detect diseases since abnormal body temperature is a natural indicator of illness or complications [11][12][13][14]. This technique measures radiometric information to obtain correlations between the skin surface temperature and the underlying physiological process, which is commonly visible as a false-color image [15][16][17]. The IR radiation emitted by the skin surface is detected by an array of microbolometers and interpreted as temperature [17].…”

Section: Introductionmentioning

confidence: 99%

“…This technique measures radiometric information to obtain correlations between the skin surface temperature and the underlying physiological process, which is commonly visible as a false-color image [15][16][17]. The IR radiation emitted by the skin surface is detected by an array of microbolometers and interpreted as temperature [17].…”

Section: Introductionmentioning

confidence: 99%

Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis

Mancilla

Phan

Daul

et al. 2021

Sensors

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Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot.

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“…Another work related to our proposal but without the application to fall detection was presented in Bayareh et al, 21 studying the diabetic foot by means of a Raspberry Pi as an embedded system and the Lepton-Flir Development Kit as an IR sensor. The IR sensor was characterized to measure the superficial temperature of the human skin radiometrically.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

López-Medina

Espinilla

Nugent

et al. 2020

International Journal of Distributed Sensor Networks

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The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

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Development of a thermographic image instrument using the raspberry Pi embedded system for the study of the diabetic foot

Cited by 9 publications

References 20 publications

Proposal of a Non-Invasive Measurement of Physical Properties of Tissues in Patients with Diabetic Foot: Measurement Experiences in Diagnosed Patients

Proposal of a Non-Invasive Measurement of Physical Properties of Tissues in Patients with Diabetic Foot: Measurement Experiences in Diagnosed Patients

Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis

Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

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