Abstract
Humans often cross their legs unconsciously while sitting, which can lead to health problems such as shifts in the center of gravity, lower back pain, reduced blood circulation, and pelvic distortion. Detecting unconscious leg crossing is important for promoting correct posture. In this study, we investigated the detection of leg-crossing postures using machine learning algorithms applied to data from body pressure distribution sensors. Pressure data were collected over 180 s from four male subjects (25.8 ± 6.29 years old) under three conditions: no leg crossing, right-leg crossing, and left-leg crossing. Seven classifiers, including support vector machine (SVM), random forest (RF), and k-nearest neighbors (k-NN), were evaluated based on accuracy, recall, precision, and specificity. Among the tested methods, k-NN demonstrated the highest classification performance, suggesting it may be the most effective approach for identifying leg-crossing postures in this study.
Published Version
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