Prolonged exposures to hand-transmitted vibrations from grass-cutting machines
have been associated with increasing occurrences of signs of occupational diseases
related to the hand-arm vibration syndrome (HAVS). However, there are no specific
processes available that cover the objective and subjective health cause-effects of the
hand arm vibration risk factors during onsite operations. Most of the existing
vibration control measures have not extensively integrated administration and
engineering techniques to be utilized as health prediction screening models.
Therefore, the main objectives of this study are to integrate the engineering and
administration control approach for reducing HAVS among hand-held grass-cutting
workers and to determine the significant correlation of the objective and subjective
measurement variables of the Hand Arm Vibration Exposure Risk Assessment
(HAVERA) on hand arm vibration symptoms and disorders. The study was
conducted in two stages: evaluation of the HAVERA variables (Stage 1) and
development of the health prediction cause-effect model of the HAVERA process
using multiple linear regressions and feed forward neural network programming
(Stage 2). For the onsite measurement, the daily vibration value depicted an
exceeded exposure action value of 2.5 m/s2 for both hands; and experiences of any
finger colour change were claimed by 80% of the 204 subjects. This shows that the
HAVERA process provided a good indication of HAVS which are reported as
vascular, neurological and musculoskeletal disorders. In the right and left hand
prediction model development, the results of the neural network model demonstrated
a higher reliability performance as compared to the linear model for hand grip
strength and hand numerical scoring assessment. The prediction of the HAVERA
model using the neural network method has been developed for monitoring health
conditions due to hand-transmitted vibrations among hand-held grass-cutting
workers in Malaysia
