Osteoporosis is characterized by low Bone Mineral Density (BMD). This illness has a highcost impact in all developed countries. In this work, we propose a supervised approach to classify the BMD status of post-menopausal women based on an experimental non-invasive analysis of static and dynamic baropodometry. A questionnaire on nutritional habits and lifestyle and previous fractures was drawn up. Sixty women in amenorrhea > 12 months and age > 45 years were included and divided in 3 groups (Normal, Osteopenic and Osteoporotic) according to T-score values. Those with neurological or musculoskeletal disorders, history of vestibulopathies, uncorrected visual deficit or drug use were excluded. Static and Dynamic Baropodometry was performed to all the enrolled women and a preliminary processing was carried on to select the most relevant features via Principal Component Analysis (PCA). Subsequently, two supervised classifiers based on the selected 27 features were designed and tested and their results were discussed as a promising tool for screening subjects suffering from both bone and muscle functional decline.
A Supervised Approach to Classify the Status of Bone Mineral Density in Post-Menopausal Women through Static and Dynamic Baropodometry
Loconsole, Claudio;
2018-01-01
Abstract
Osteoporosis is characterized by low Bone Mineral Density (BMD). This illness has a highcost impact in all developed countries. In this work, we propose a supervised approach to classify the BMD status of post-menopausal women based on an experimental non-invasive analysis of static and dynamic baropodometry. A questionnaire on nutritional habits and lifestyle and previous fractures was drawn up. Sixty women in amenorrhea > 12 months and age > 45 years were included and divided in 3 groups (Normal, Osteopenic and Osteoporotic) according to T-score values. Those with neurological or musculoskeletal disorders, history of vestibulopathies, uncorrected visual deficit or drug use were excluded. Static and Dynamic Baropodometry was performed to all the enrolled women and a preliminary processing was carried on to select the most relevant features via Principal Component Analysis (PCA). Subsequently, two supervised classifiers based on the selected 27 features were designed and tested and their results were discussed as a promising tool for screening subjects suffering from both bone and muscle functional decline.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.