Whole-body vibration is a health hazard for operators of construction machinery. The level of whole-body vibration exposure on the operator is governed by three different factors; performance of the suspension system of the machine, planning of the work and the skills of the operator.In this research work it is investigated whether there is a potential in bringing down the level of whole-body vibration exposure by educating operators of backhoe loaders. This is carried out by an experimental setup. Six experienced operators participated in the experiments carried out on two different sizes of backhoe loaders. Each operator had to complete three different tasks without any kind of instructions. Subsequently they got a short education on eco-driving and vibration avoidance and carried out the tasks once more. Time duration, whole-body vibration exposure and fuel consumption was registered before and after education.The result of the short education was an average reduction in the wholebody vibration exposure of 22.5%. And for all completed tasks expect one a considerably fuel saving was obtained too -up to 38%. This experiment * Tel.: +45 98371333; Fax.: +45 98379912Email addresses: thl@m-tech.aau.dk (Thomas H. Langer), tki@hydrema.com (Thorkil K. Iversen), nika@mercantec.dk (Niels K. Andersen), oom@m-tech.aau.dk (Ole Ø. Mouritsen), michael.r.hansen@uia.no (Michael R. Hansen) Preprint submitted to International Journal of Industrial ErgonomicsApril 25, 2012 demonstrates that education of the operator will improve the occupational health and save fuel. The results also indicate that these improvements can be obtained without reduction in productivity as the instructions become a habit for the operators. Thus it is profitable for the employer to educate the employees operating construction machinery. The findings of this work is highly relevant to the construction industry. It shows a great potential in reducing damaging vibration and at the same time reduce fuel consumption. It also emphasizes the need for better education of machine operators.

For people living with an ostomy, development of peristomal skin complications (PSCs) is the most common post-operative challenge. A visual sign of PSCs is discoloration (redness) of the peristomal skin often resulting from leakage of ostomy output under the baseplate. If left unattended, a mild skin condition may progress into a severe disorder; consequently, it is important to monitor discoloration and leakage patterns closely. The Ostomy Skin Tool is current state-of-the-art for evaluation of peristomal skin, but it relies on patients visiting their healthcare professional regularly. To enable close monitoring of peristomal skin over time, an automated strategy not relying on scheduled consultations is required. Several medical fields have implemented automated image analysis based on artificial intelligence, and these deep learning algorithms have become increasingly recognized as a valuable tool in healthcare. Therefore, the main objective of this study was to develop deep learning algorithms which could provide automated, consistent, and objective assessments of changes in peristomal skin discoloration and leakage patterns. A total of 614 peristomal skin images were used for development of the discoloration model, which predicted the area of the discolored peristomal skin with an accuracy of 95% alongside precision and recall scores of 79.6 and 75.0%, respectively. The algorithm predicting leakage patterns was developed based on 954 product images, and leakage area was determined with 98.8% accuracy, 75.0% precision, and 71.5% recall. Combined, these data for the first time demonstrate implementation of artificial intelligence for automated assessment of changes in peristomal skin discoloration and leakage patterns.