Risks Associated With Tendinitis: Effects From Demographic, Socioeconomic, and Psychological Status Among Brazilian Workers

Background Self-reported tendinitis/tenosynovitis was evaluated by gender, age group, skin color, family income, and educational and psychological status. Methods The study was carried out in a representative sample of formally contracted Brazilian workers from a household survey. A total of 54,660 participants were included. Occupations were stratified according to estimated prevalences of self-reported injuries. Non-conditional logistic regression was performed, and all variables were analyzed in two occupational groups. Results The overall prevalence rate of tendinitis/tenosynovitis was 3.1%: 5.5% in high-prevalence occupations (n=10,726); and 2.5% in low-prevalence occupations (n=43,934). White female workers between the ages of 45 and 64 years and at a higher socioeconomic level were more likely to report tendinitis/tenosynovitis regardless of their occupational category. An adjusted OR = 3.59 [95% CI: 3.15-4.09] was found between tendinitis/tenosynovitis and psychological status. Conclusion Among formally contracted Brazilian workers, higher income can imply greater physical and psychological demands that, regardless of occupational stratum, increase the risk of tendinitis/tenosynovitis. Am. J. Ind. Med. 53:72-79, 2010. (C) 2009 Wiley-Liss, Inc.

Mice and larvae tracking using a particle filter with an auto-adjustable observation model

This paper proposes a novel way to combine different observation models in a particle filter framework. This, so called, auto-adjustable observation model, enhance the particle filter accuracy when the tracked objects overlap without infringing a great runtime penalty to the whole tracking system. The approach has been tested under two important real world situations related to animal behavior: mice and larvae tracking. The proposal was compared to some state-of-art approaches and the results show, under the datasets tested, that a good trade-off between accuracy and runtime can be achieved using an auto-adjustable observation model. (C) 2009 Elsevier B.V. All rights reserved.