Effective Posture Classification Using Statistically Significant Data From Flexible Pressure Sensors

• Published in: IEEE journal on flexible electronics Vol. 3; no. 5; pp. 173 - 180
• Main Authors: Yoon, Jungeun, Moon, Aekyeung, Son, Seung Woo
• Format: Journal Article
• Language: English
• Published: IEEE 01.05.2024
• Subjects: Classification; Estimation; Feature extraction; flexible and printable sensor; Flexible electronics; IoT monitoring; Monitoring; posture monitoring; Pressure measurement; pressure sensor; Pressure sensors; Support vector machines
• ISSN: 2768-167X; 2768-167X
• DOI: 10.1109/JFLEX.2024.3400151

Advancements in flexible and printable sensor technologies to overcome the limitations of conventional rigid counterparts offer an excellent opportunity to design various healthcare applications for humans, and their potential flexibility can be used in real-time health monitoring and personalized physical conditions with minimal or no inconvenience. However, managing a large volume of obtained sensor datasets and ensuring accurate predictions can take time and effort. While statistical analysis and the Pearson correlation coefficient can reduce data volume, whether this would lead to losing important information and affect downstream application performance is still being determined. In this article, we use posture classification as an exemplar of timely services in digital healthcare, especially for bedsores or decubitus ulcers. Our sensors, placed under hospital beds, have a thickness of just 0.4 mm and collect pressure data from 28 sensors (<inline-formula> <tex-math notation="LaTeX">7 \times 4 </tex-math></inline-formula>) at an 8-Hz cycle, categorizing postures into four types from five patients. We then collected sensor data to explore the possibility of using a small number of pressure sensors for patient posture classification. Next, we apply a statistical analysis to the datasets obtained to select the featured sensor data cells and evaluate the performance of posture classification models on various groups of sensors. Our evaluation involves the analysis of reduced datasets through statistical methods and the Pearson correlation coefficient. The classification performance using datasets comprising five featured and 28 sensors are 0.93 and 0.99, respectively. These results suggest comparable performance and the viability of useful classifiers for both the cases. Consequently, comparable posture classification performance can be achieved using only 17.9% of the entire dataset.