Novel methods to detect impacts within whole-body vibration time series data

Area of Science:

• Occupational health
• Biomechanics
• Ergonomics

Background:

• Current whole-body vibration (WBV) exposure metrics lack detail on impact number and magnitude.
• Assessing WBV impacts is critical for understanding occupational risks, particularly in agriculture.

Purpose of the Study:

• To present and evaluate three mathematical models for detecting impacts in time series accelerometer data.
• To investigate the association between mechanical/biomechanical variables at impact onset and erector spinae muscle response.
• To highlight the role of trunk posture in WBV-induced biomechanical responses.

Main Methods:

• Collected accelerometer, erector spinae muscle activity, and trunk posture data during agricultural machine operation.
• Developed three candidate mathematical models to identify impacts in WBV data.
• Analyzed associations between impact characteristics, trunk posture, and subsequent bilateral erector spinae muscle activation.

Main Results:

• All three models demonstrated that trunk posture at impact onset strongly predicts erector spinae muscle response.
• These posture-impact associations were absent when impacts were artificially randomized.
• Impact detection models provide a framework for quantifying impact number and magnitude, improving upon metrics like vibration dose value.

Conclusions:

• Trunk posture is a critical factor in the biomechanical response to occupational WBV impacts.
• The proposed models offer a more nuanced approach to WBV exposure assessment by quantifying impacts.
• Future WBV exposure evaluations should integrate trunk posture data for a comprehensive risk assessment.