Assessing the Risk of Low Back Pain and Injury via Inertial and Barometric Sensors

Autor: Beatrice Lazzerini, Francesco Pistolesi
Rok vydání: 2020
Předmět:
Inertial frame of reference
neural network
occupational safety and health
Computer science
02 engineering and technology
Back injury
manual handling of loads
0202 electrical engineering
electronic engineering
information engineering
medicine
Back pain
Electrical and Electronic Engineering
low back pain
Simulation
smart clothes
wearable sensors
020208 electrical & electronic engineering
risk assessment
artificial intelligence
Industry 4.0
medicine.disease
Low back pain
Computer Science Applications
machine learning
classification
Activity recognition
artificial intelligence
classification
Industry 4.0
low back pain
machine learning
manual handling of loads
neural network
occupational safety and health
risk assessment
smart clothes
wearable sensors
Control and Systems Engineering
Activity recognition
medicine.symptom
Information Systems
Zdroj: IEEE Transactions on Industrial Informatics. 16:7199-7208
ISSN: 1941-0050
1551-3203
DOI: 10.1109/tii.2020.2992984
Popis: 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 article presents a system based on artificial intelligence (AI) 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%.
Databáze: OpenAIRE
Externí odkaz: