The radiation workers in India use extremity dosemeter in occupational areas where there is likelihood of receiving significant dose to extremities of the body. Algorithm used for the estimation of whole body dose and extremity dose of wrist are same and is based on slab phantom calibration. However, internationally, different phantoms are recommended for calibration of dosemeter used for extremity and whole body. The recommended quantity for estimation of dose to extremity is the personal dose equivalent Hp(0.07) at 0.07 mm depth. In light of this, an algorithm was developed for estimation of wrist dose in terms of Hp(0.07) based on calibration performed on ISO recommended pillar phantom. Performance of the dosemeter for the estimation of Hp(0.07) with new algorithm was carried out at different angular exposures of photon beams and mixed field of photon and beta. Comparison of results obtained for two types of ISO phantoms (slab and pillar) is also performed for photon beams to highlight the uncertainty caused due to the use of the slab phantom. In case of beta radiation, it was found that the size and shape of the phantom has very little influence in the response of the dosemeter. Performance of the dosemeter using ISO slab and ISO pillar phantoms for beta radiation using the prevalent algorithm was carried out and found to be within the uncertainty limits laid down by ISO except at angle 60°.

The first study on fitting dose data for workers was performed by Gale(  1) in 1965 where log-normal and normal distributions were used. Since then, various models of dose distribution have been proposed. The log-normal distribution and its different forms have been widely used for fitting the dose data. Most of the studies included one or two distributions under consideration. In this study five distributions are considered for fitting and four distributions are selected based on observation of Cullen–Frey graph. The Akaike’s Information criteria (AIC) and Bayesian Information criteria (BIC) are applied to find the suitable distribution to fit the occupational dose data. The maximum likelihood method was used for calculation of AIC, BIC values and parameter estimation. A computer code is written in R-language and environment for statistical computing and graphics for analysis of occupational dose data of three institutions.