Abstract
This work is part of a larger project developing wearable posture monitors for the work environment. We seek to evaluate the predictive power of individual spinal segment vector angles, towards the selection of the optimum angles for posture monitoring. A marker-based optoelectronic motion capture system was used to monitor seated posture for 9 healthy subjects during a range of motion flexion-extension exercise. Machine learning techniques were used to evaluate the prediction accuracy of the component vector angles recorded, and the range of motion for each vector angle was calculated for each subject. The overall flexion vector angle, which encompasses the entire spinal length between the C7 and L4 vertebrae, was determined to be the best predictor angle, due to its predictive accuracy and simplicity, and its relatively larger range of motion in all subjects.
Original language | English (US) |
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Pages (from-to) | 5748-5751 |
Number of pages | 4 |
Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
DOIs | |
State | Published - 2007 |
Event | 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France Duration: Aug 23 2007 → Aug 26 2007 |
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't