Low back pain affects one in three workers in the world and is among the biggest causes of absence from work. Almost 75% of back injuries occur when lifting loads. In warehousing, agriculture and construction, for example, workers are continuously handling loads manually. If incorrectly performed, these tasks put the workers at risk of back pain, injuries, and musculoskeletal disorders. Monitoring how the loads are lifted is key to quickly detecting which workers are showing dangerous behaviors, so that they can be (re)trained to perform the task safely, thereby reducing the risk of injury. This paper presents an AI-based system that exploits wearable sensors to assess the safety level of workers lifting loads. The system consists of a reflective safety jacket equipped with two barometric altimeters, a triaxial accelerometer, and a triaxial magnetometer. The sensors of the jacket continuously record these signals during the workday. The system then fuses the data from the two barometric altimeters in order to detect when the worker lifted loads. A neural classifier uses the signals recorded by the accelerometer and magnetometer to determine whether or not the task was performed safely. The system was tested on 30 workers and achieved an accuracy of 95.6%.
Assessing the Risk of Low Back Pain and Injury via Inertial and Barometric Sensors
Pistolesi, Francesco
Primo
;Lazzerini, BeatriceSecondo
2020-01-01
Abstract
Low back pain affects one in three workers in the world and is among the biggest causes of absence from work. Almost 75% of back injuries occur when lifting loads. In warehousing, agriculture and construction, for example, workers are continuously handling loads manually. If incorrectly performed, these tasks put the workers at risk of back pain, injuries, and musculoskeletal disorders. Monitoring how the loads are lifted is key to quickly detecting which workers are showing dangerous behaviors, so that they can be (re)trained to perform the task safely, thereby reducing the risk of injury. This paper presents an AI-based system that exploits wearable sensors to assess the safety level of workers lifting loads. The system consists of a reflective safety jacket equipped with two barometric altimeters, a triaxial accelerometer, and a triaxial magnetometer. The sensors of the jacket continuously record these signals during the workday. The system then fuses the data from the two barometric altimeters in order to detect when the worker lifted loads. A neural classifier uses the signals recorded by the accelerometer and magnetometer to determine whether or not the task was performed safely. The system was tested on 30 workers and achieved an accuracy of 95.6%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
