Proceedings Article | 1 August 2023
KEYWORDS: Sensors, Education and training, Data modeling, Feature extraction, Machine learning, Windows, Intelligent sensors, Visual process modeling, Thin films, Infrared sensors
Prolonged and poor sitting postures are major causes of many musculoskeletal disorders, such as herniated discs, cervical spondylosis, back fasciitis, and scoliosis, and hence proper postures are essential for maintaining good health. Towards a pervasive and low-cost healthcare system, we in this study design and implement a sitting posture monitoring system, named iGuard, with a pressure sensor array. Specifically, we use five sensors to sense the sitting posture information and naturally annotate the training sensor readings over a while. Afterward, we segment the streaming data and extract a variety of time-domain and frequency-domain features from the segments to train a sitting posture recognition model. Finally, comparative experiments are conducted to evaluate the performance of iGuard in distinguishing normal, left-leaning, right-leaning, forward-leaning, backward-leaning, left-leg crossed, and right-leg crossed sitting postures. Particularly, we consider three different evaluation schemes, i.e., subject-dependent setting, subject-independent setting, and cross-subject setting. Experimental results demonstrate the effectiveness of iGuard.
