Title
A convenient photo-based approach for assessing body posture
Abstract
Good body posture is important because it helps to reduce the risk of musculoskeletal injuries and permanent distortions which may interfere with efficient functioning of the body. Individuals need to be aware that good body posture is vital for a healthy quality of life, especially as one ages. However, an individual's ability to detect poor posture can be difficult as a faulty posture tends to feel normal after continuous practice. Moreover, consulting a physician about one's posture may not always be feasible. As such, we propose a convenient photo-based approach for assessing body posture from a side profile image of an individual. Shape features extracted by processing these images, such as the curves from an individual's back are used to train a Support Vector Machine and a Naïve Bayes classification models to predict good or poor posture. Results from the experiments conducted with 144 participants demonstrate the effectiveness of our approach.
Year
DOI
Venue
2014
10.1109/ICMEW.2014.6890654
Multimedia and Expo Workshops
Keywords
Field
DocType
Bayes methods,feature extraction,image classification,injuries,muscle,shape recognition,support vector machines,Naïve Bayes classification model,body posture,faulty posture,image processing,musculoskeletal injury,permanent distortion,photo-based approach,quality of life,shape feature extraction,side profile image,support vector machine,body posture,classification,feature extraction,image processing,shape features,support vector machine
Structured support vector machine,Computer vision,Feature vector,Pattern recognition,Naive Bayes classifier,Feature (computer vision),Computer science,Support vector machine,Image processing,Feature extraction,Poor posture,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
1945-7871
1
0.48
References 
Authors
2
5
Name
Order
Citations
PageRank
Ligaj Pradhan193.04
Chengcui Zhang210.48
Danielle K. Powell310.48
David B Allison41389.96
Olivia Affuso520.85