Automatic learning for improvement in joint mobility in the elderly

In Europe, the elderly are the majority group, an issue that implies specific attention. On many occasions, they need to be taken care of by other people. It is essential to talk about the gait, this plays an important role and it is necessary to maintain a joint width of movement adequate in the hip, knee and ankle so that the standing and walking are functional. Among the physical factors that limit mobility in the elderly are the articular and capsular, which can produce an immobility of the person and the appearance of pain, causing a significant deterioration in the quality of life of the elderly, a question that will give rise in most cases to the appearance of depressive symptoms. It is an obvious fact that public coverage must offer health activities to promote the health of this group. The purpose of this work is to document in a bibliographic and visual way a table of physical exercises and psychological techniques for its future implantation within the Primary Health Care (PHC) and the Machine Learning tools to develop a virtual gerontology nurse to assist in deploying the exercises at home. After an adequate automatic learning and health education, with greater implication of the nursing specialist in geriatrics and gerontology (GS) and once exercised, allow the elderly to develop with a maximum of autonomy and independence at home, as well as reduce the doses of drugs, an issue that will benefit health expenditure. We are proposing a Virtual Agent based in Computer Vision, Artificial Intelligence and Natural Interfaces that can perform unattended sessions for joint mobility improvement

Search for new physics in aμX data at DØ using SLEUTH: A quasi-model-independent search strategy for new physics

We present a quasi-model-independent search for the physics responsible for electroweak symmetry breaking. We define final states to be studied, and construct a rule that identifies a set of relevant variables for any particular final state. A new algorithm ("SLEUTH") searches for regions of excess in those variables and quantifies the significance of any detected excess. After demonstrating the sensitivity of the method, we apply it to the semi-inclusive channel eμX collected in 108 pb-1 of pp̄ collisions at √s=1.8 TeV at the DØ experiment during 1992-1996 at the Fermilab Tevatron. We find no evidence of new high pT physics in this sample. ©2000 The American Physical Society