Real‐time vibration monitoring and analysis of agricultural tractor drivers using an IoT‐based system

Abstract

Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore‐and‐aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real‐field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat‐to‐head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x‐, y‐, and z‐axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID‐19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x‐, y‐, and z‐axes. The vibration energy was found to be dominant along the z‐axis than the x‐ and y‐axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x‐, y‐, and z‐axes depicted the low‐frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y‐ and z‐axes when compared with that along the x‐axis. The frequency range of 4–7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.

Artificial Neural Network-Based Model for Assessing the Whole-Body Vibration of Vehicle Drivers

“…The proposed approach for generating individualised WBV assessment models provides a potential tool for use with Internet of Things (IoT) technologies. Previous studies have noted that the current development and growth in the area of IoT offers new avenues for healthcare and smart health monitoring [62][63][64]. The integration of the IoT with the proposed models could play a vital role in real-time monitoring and safety enhancement during driving activities.…”