Automatic Ergonomics Evaluation of Sawing and Hammering Activities Using a Wrist-Worn IMU

Tens of millions of workers around the world suffer from occupational diseases related to Repetitive Strain Injuries (RSI), caused by non-ergonomic manual labor. This paper presents an application for automatically evaluating the ergonomics of repetitive hammering and sawing activities using a wrist-worn IMU. The following application automatically detects the time spent and the number of strokes per shift for hammering and sawing activities, and outputs the Occupational Repetitive Action (OCRA) index as per ISO 11228-3:2007 standard on handling low loads at high frequency. The proposed framework involves using Machine Learning models and biomechanical constraints to automatically calculate the OCRA index, which, if exceeds a certain threshold, indicates that the worker has to make some ergonomic changes to their routine starting from the next shift, to avoid chronic health risks. Authors demonstrate the feasibility and robustness of this application by employing the system in an experimental study.