A Statistical and AI Analysis of the Frequency Spectrum in the Measurement of the Center of Pressure Track in the Seated Position in Healthy Subjects and Subjects with Low Back Pain

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 10; p. 3011
• Main Authors: Koltermann, Jan Jens, Floessel, Philipp, Hammerschmidt, Franziska, Disch, Alexander C.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.05.2024
• Subjects: Adolescent; Adult; Algorithms; Analysis; Artificial Intelligence; Back pain; Backache; balance board; COP; Cross-Sectional Studies; Female; Healthy Volunteers; Humans; Low Back Pain - diagnosis; Low Back Pain - physiopathology; Male; Methods; Middle Aged; Neural networks; Physiology; Postural Balance - physiology; Posture; Posture - physiology; power density; Pressure; Prospective Studies; Questionnaires; Sitting Position; Young Adult; Germany; power density; AI; COP; balance board
• DOI: 10.3390/s24103011
• ISSN: 1424-8220

Measuring postural control in an upright standing position is the standard method. However, this diagnostic method has floor or ceiling effects and its implementation is only possible to a limited extent. Assessing postural control directly on the trunk in a sitting position and consideration of the results in the spectrum in conjunction with an AI-supported evaluation could represent an alternative diagnostic method quantifying neuromuscular control. In a prospective cross-sectional study, 188 subjects aged between 18 and 60 years were recruited and divided into two groups: “LowBackPain” vs. “Healthy”. Subsequently, measurements of postural control in a seated position were carried out for 60 s using a modified balance board. A spectrum per trail was calculated using the measured CoP tracks in the range from 0.01 to 10 Hz. Various algorithms for data classification and prediction of these classes were tested for the parameter combination with the highest proven static influence on the parameter pain. The best results were found in a frequency spectrum of 0.001 Hz and greater than 1 Hz. After transforming the track from the time domain to the image domain for representation as power density, the influence of pain was highly significant (effect size 0.9). The link between pain and gender (p = 0.015) and pain and height (p = 0.012) also demonstrated significant results. The assessment of postural control in a seated position allows differentiation between “LowBackPain” and “Healthy” subjects. Using the AI algorithm of neural networks, the data set can be correctly differentiated into “LowBackPain” and “Healthy” with a probability of 81%.