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Journal : JOIV : International Journal on Informatics Visualization

Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning Arisandy, -; Erfianto, Bayu; Setyorini, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2654

Abstract

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.
Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation Erfianto, Bayu; Rahmatsyah, Andrian
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.889

Abstract

Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.