Project

CoViS: Contactless Vital Signs Monitoring in Nursing Homes using a Multimodal Approach (Ref: POCI-01-02B7-FEDER-070090)

Goal: Reinforce research, technological development and innovation

Synthesis: In the context of a pandemic situation such as the one we are currently experiencing with COVID-19, the need for continuous, accurate, and real-time monitoring of the health conditions of groups at risk, such as the elderly, is crucial, due to the fast spread of the disease and the need to act quickly to contain its evolution. Continuous monitoring of vital signs, such as body temperature and cardio-pulmonary rates, can be crucial in early detection and prediction of the COVID-19 disease, that rapidly progress and particularly affect this population group.
Conventional clinical methods used for monitoring vital signs are contact-based, i.e. require the use of contact sensors that need to be precisely attached by a health professional, are less convenient for repeatable measurements, and not practical for long-duration monitoring. On the other hand, contactless vital signs monitoring using radar-based techniques, or IR-thermal imaging, do not require the attachment of physical electrodes and is of great value for the elderly population in the specific context of nursing houses, because it removes the need of using wires, being more comfortable and less invasive for the patients.

This project aims to design a low-cost contactless IoT edge device for real-time vital signs monitoring (cardio-pulmonary rates and body temperature) using a multimodal approach based on state-of-the-art Doppler radar techniques and IR thermal imaging. The device can be placed in nursing homes, above the beds where patients rest, allowing the continuous acquisition of data and its processing without any type of contact and invasiveness.

PROJETOS DE I&DT EMPRESAS EM COPROMOÇÃO :: COVID-19

Reference: POCI-01-02B7-FEDER-070090

Consortium:
- WAVECOM, Soluções de Rádio, S.A. (Promotor)
- Instituto de Telecomunicações
- Instituto Politécnico de Viana do Castelo
- Instituto Superior de Engenharia de Lisboa

Eligible Costs: 379 197,03 € (Total)

Funding (FEDER - COMPETE 2020): 236 819,35 € (Total)

Funding (FEDER - LISBOA 2020): 71 191,12 € (Total)

Approval Date: 02-10-2020

Date: 15 October 2020 - 14 July 2021

Updates
0 new
0
Recommendations
0 new
0
Followers
0 new
7
Reads
0 new
25

Project log

Sérgio Ivan Lopes
added a research item
Continuous monitoring of vital signs like body temperature and cardio-pulmonary rates can be critical in the early prediction and diagnosis of illnesses. Optical-based methods, i.e., RGB cameras and thermal imaging systems, have been used with relative success for performing contactless vital signs monitoring, which is of great value for pandemic scenarios, such as COVID-19. However, to increase the performance of such systems, the precise identification and classification of the human body parts under screening can help to increase accuracy, based on the prior identification of the Regions of Interest (RoIs) of the human body. Recently, in the field of Artificial Intelligence, Machine Learning and Deep Learning techniques have also gained popularity due to the power of Convolutional Neural Networks (CNNs) for object recognition and classification. The main focus of this work is to detect human body parts, in a specific position that is lying on a bed, through RGB and Thermal images. The proposed methodology focuses on the identification and classification of human body parts (head, torso, and arms) from both RGB and Thermal images using a CNN based on an open-source implementation. The method uses a supervised learning model that can run in edge devices, e.g. Raspberry Pi 4, and results have shown that, under normal operating conditions, an accuracy in the detection of the head of 98.97% (98.4% confidence) was achieved for RGB images and 96.70% (95.18% confidence) for thermal images. Moreover, the overall performance of the thermal model was lower when compared with the RGB model.
Sérgio Ivan Lopes
added a research item
This paper proposes a low cost IoT solution to detect head movements and positions of a patient by means of Force Sensing Resistors positioned on a pillow and connected to a micro-controller collecting patient data anytime, when sleeping, sending it to the cloud and making it available to healthcare professionals. The impact of this work is focused on monitoring sleep quality, using low-cost and easy to use pillows in an ambulatory scenario, without the need of expensive and dedicated sleeping rooms for sleep monitoring, which most of the times affect patient sleep and degrades the quality of the measurement.In this case it is possible to monitor the patient’s behavior throughout the entire sleep, important for detecting factors causing minor head and neck injuries and even checking for events of long pauses in respiratory rate.
Sérgio Ivan Lopes
added a project goal
Reinforce research, technological development and innovation
Synthesis: In the context of a pandemic situation such as the one we are currently experiencing with COVID-19, the need for continuous, accurate, and real-time monitoring of the health conditions of groups at risk, such as the elderly, is crucial, due to the fast spread of the disease and the need to act quickly to contain its evolution. Continuous monitoring of vital signs, such as body temperature and cardio-pulmonary rates, can be crucial in early detection and prediction of the COVID-19 disease, that rapidly progress and particularly affect this population group.
Conventional clinical methods used for monitoring vital signs are contact-based, i.e. require the use of contact sensors that need to be precisely attached by a health professional, are less convenient for repeatable measurements, and not practical for long-duration monitoring. On the other hand, contactless vital signs monitoring using radar-based techniques, or IR-thermal imaging, do not require the attachment of physical electrodes and is of great value for the elderly population in the specific context of nursing houses, because it removes the need of using wires, being more comfortable and less invasive for the patients.
This project aims to design a low-cost contactless IoT edge device for real-time vital signs monitoring (cardio-pulmonary rates and body temperature) using a multimodal approach based on state-of-the-art Doppler radar techniques and IR thermal imaging. The device can be placed in nursing homes, above the beds where patients rest, allowing the continuous acquisition of data and its processing without any type of contact and invasiveness.
PROJETOS DE I&DT EMPRESAS EM COPROMOÇÃO :: COVID-19
Reference: POCI-01-02B7-FEDER-070090
Consortium:
- WAVECOM, Soluções de Rádio, S.A. (Promotor)
- Instituto de Telecomunicações
- Instituto Politécnico de Viana do Castelo
- Instituto Superior de Engenharia de Lisboa
Eligible Costs: 379 197,03 € (Total)
Funding (FEDER - COMPETE 2020): 236 819,35 € (Total)
Funding (FEDER - LISBOA 2020): 71 191,12 € (Total)
Approval Date: 02-10-2020