Noise and vibration exposure on the physiological parameters of bus drivers using machine learning algorithm

In present's circumstances, vehicle oper-ators are subjected to severe health hazards due to extended period of noise, and vibration exposure while operating the vehicle. The aim of this study is to stimu-late noise, vibration, and demographic characteristics on the physiological parameters of bus drivers operating in Dhanbad city using artificial neural network. Equivalent noise exposure, and hand arm vibration are calculated using SLM, and vibration meter at different routes of Dhanbad city. With rise in noise, and vibration exposure, vehicle operator tends to change their posture frequently leading to long term health effects. The experimental results indicate that the proposed method can be a reli-able approach for prediction of physiological parameters with an error less than ±2.0. The descriptive nature of research and meagre sample testing, limits us from arriving at the strong conclusion.