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The influence of a dynamic elastic garment on musculoskeletal and respiratory wellness in computer users

Decker M., Gomas K. A., Narvy S. J., Vangsness C. T.

International Journal of Occupational Safety and Ergonomics

2016

Vol. 22, No. 4

550--556

Background. Evidence is growing that computer users are at increased risk of developing musculoskeletal disorders, particularly those involving the upper extremity, with significant financial cost and lost productivity. Objective. The purpose of this study was to determine the short-term effects of wearing a dynamic elastic garment (Posture Shirt ; AlignMed, USA) on musculoskeletal wellness and health in the computer workplace. Methods. Ninety-six computer users were evaluated. The Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire was completed. A functional assessment of posture, lung function, and grip strength was performed after wearing the Posture Shirt for 4 weeks. A training log was kept to track usage of the garment, as well as weekly sensations of fatigue, productivity, and energy level. Results. After 4 weeks, there was statistically significant improvement in forward shoulder and head posture, thoracic kyphosis, and grip strength. Improvements in spirometry measures did not meet statistical significance. Postural fatigue and muscular fatigue decreased by 21% and 29%, respectively, and energy level and productivity increased by 20% and 13%, respectively. Conclusion. This prospective study demonstrated positive short-term impact of the Posture Shirt on both subjective and objective measures of posture, lung function, grip strength, fatigue, and productivity.

Latency of Neighborhood Based Recommender Systems

Chojnacki S., Kłopotek M.

Fundamenta Informaticae

2015

Vol. 139, nr 3

229--248

Latency of user-based and item-based recommenders is evaluated. The two algorithms can deliver high quality predictions in dynamically changing environments. However, their response time depends not only on the size, but also on the structure of underlying datasets. This constitutes a major drawback when compared to two other competitive approaches i.e. content-based and modelbased systems. Therefore, we believe that there exists a need for comprehensive evaluation of the latency of the two algorithms. During a typical worst case scenario analysis of collaborative filtering algorithms two assumption are made. The first assumption says that data are stored in dense collections. The second assumption states that large amount of computations can be performed in advance during the training phase. As a result it is advised to deploy user-based system when the number of users is relatively small. Item-based algorithms are believed to have better technical properties when the number of items is small. We consider a situation in which the two assumptions are not necessarily met. We show that even though the latency of the two methods depends heavily on the proportion of users to items, this factor does not differentiate the two methods. We evaluate the algorithms with several real-life datasets. We augment the analysis with both graph-theoretical and experimental techniques.