PHYSICS OF VIBRATION AND HOW VIBRATION TRANSMITTED TO THE HAND IS CAPTURED

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Related research – connecting data mining and HAV

After conducting literature research, there were no research papers regarding developing new VEMs for chainsaws by using rpm ranges or the mode of the chainsaw as independent variables. On the other hand, there are several research papers that predict vibration exposure for HAV and whole-body vibration (WBV) by considering other variables e.g. age of vibration exposed workers. Below are the papers of relevant research with a short explanation of each.

Whole-body vibration exposure

(Clay, Milosavljevic, & Trask, 2015) conducted a study to measure vibration exposure for the whole body. The purpose behind this study was to develop statistical predictive models to predict WBV exposure using self-reported predictors. The target group in this study was farmers that operate on agricultural quad bikes and the data collection included 130 farmers. To create the predictive models, they considered the following variables: farmer age, estimated quad bike driving hours (to estimate exposure time) and the type of the quad bike rear suspension. The outcome of this research was four predictive models where model three and four had similar characteristics.

Hand grip strength prediction of hand held grass cutter workers.

(Ali, Azmir, Ghazali, Yahya, & Song, 2015) conducted a research to predict the hand grip strength. Since exposure to HAV causes disability to hand grip strength, the objective of this paper is to develop predictive models to evaluate the factors that affect hand grip strength. Non-linear neural network and linear multiple regression were used as data mining techniques. For this study 204 hand-held grass cutter workers of age between fifteen and fifty-six were selected. The input data was the following: age, weight, height, working experience and estimated hand vibration exposure, which was collected using a case study.
The dependent variable for this study was the hand grip strength for both hands, while the independent variables were age, weight, height, working experience and estimated hand vibration exposure. The results showed that both linear multiple regression and neural networks modeling techniques were successfully able to predict the hand grip strength of the workers. According to the paper, the non-linear neurological network algorithm performed much better than the linear multiple regression model.

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1 Introduction
1.1 BACKGROUND.
1.2 PURPOSE AND RESEARCH QUESTIONS
1.3 DELIMITATIONS
2 Theoretical background
2.1 PHYSICS OF VIBRATION AND HOW VIBRATION TRANSMITTED TO THE HAND IS CAPTURED
2.2 EFFECTS OF HAND-ARM VIBRATION ON HUMAN BODY.
2.3 STANDARDS AND GUIDELINES FOR HAV
2.4 VIBRATION ACCELERATION CALCULATION AT HUSQVARNA.
2.5 DATA MINING
2.6 RELATED RESEARCH – CONNECTING DATA MINING AND HAV
3 Method and implementation
3.1 DESIGN RESEARCH.
3.2 EXPERIMENTS
4 Findings and analysis .
4.1 DATA ANALYSIS
4.2 RQ1: HOW DOES DATA GATHERED FROM PRACTICAL EXPERIENCE DIFFER FROM ISO VIBRATION TEST CODE ASSUMPTIONS? .
4.3 RQ2: HOW CAN THE ISO 5349 VIBRATION EXPOSURE ESTIMATION MODEL (VEM_0) BE MODIFIED TO BE MORE ACCURATE?
4.4 RQ3: TO WHAT EXTENT CAN THE ISO 5349 VIBRATION EXPOSURE ESTIMATION MODEL BE MADE MORE ACCURATE BY TAKING INTO CONSIDERATION ADDITIONAL RPM RANGES APART FROM IDLE, ENGAGED AND RACING RPM VALUES?.
5 Discussion and conclusions
5.1 DISCUSSION OF METHOD
5.2 DISCUSSION OF FINDINGS .
5.3 CONCLUSIONS
6 References
7 Appendices