Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Healthcare · 8 authors, 5 centres

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The system was deployed in a hospital for the elderly, utilizing devices such as a Fitbit wristband, smart scale, blood pressure monitor, and camera to gather data on vital signs and movement over a period from September 15 to December 28, 2022, accumulating 1.412 TB of data. The paper focuses on the system's technical components, data processing pipelines, and the decision support model, which achieved an average classification error of 92%. Limitations noted include the complexity of data pipelines due to device manufacturers' cloud services and the challenge of ensuring consistent device use by patients with dementia.

STUDY TYPE

other

SPECIALTY

geriatrics

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10138364

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

Healthcare

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ABSTRACT

Abstract The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff. Keywords: geriatric care, IoT, vital parameters, posture recognition, image recognition, deep learning, non-contact monitoring

CITATION EXPORT

Agne Paulauskaite-Taraseviciene, Julius Siaulys, Kristina Sutiene et al.. Geriatric Care Management System Powered by the IoT and Computer Vision Techniques. Healthcare. (2023). https://doi.org/10.3390/healthcare11081152

AUTHORS

8 · 5 CENTRES

Agne Paulauskaite-Taraseviciene, Julius Siaulys, Kristina Sutiene, Titas Petravicius, Skirmantas Navickas, Marius Oliandra, Andrius Rapalis, Justinas Balciunas

1Faculty of Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
2Department of Mathematical Modeling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
3Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, 51423 Kaunas, Lithuania
4Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania
5Faculty of Medicine, Vilnius University, Universiteto 3, 01513 Vilnius, Lithuania