Physics

Work (physics)

Pelvis

Physiology

Psychotherapist

Inertial measurement unit

Biology

Displacement (psychology)

Wearable computer

Engineering

Surgery

Biomechanics

Embedded system

Artificial intelligence

Psychology

Ecology

Computer science

Trunk

Body segment

Kinematics

Simulation

Medicine

Classical mechanics

Mechanical engineering

Physical medicine and rehabilitation

Sensors

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p &lt; 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p &lt; 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach