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 < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 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.

The object of this study was to develop an in vitro bioengineered three-dimensional vascularized skeletal muscle tissue, named eX-vivo Muscle Engineered Tissue (X-MET). This new tissue contains cells that exhibit the characteristics of differentiated myotubes, with organized contractile machinery, undifferentiated cells, and vascular cells capable of forming "vessel-like" networks. X-MET showed biomechanical properties comparable with that of adult skeletal muscles; thus it more closely mimics the cellular complexity typical of in vivo muscle tissue than myogenic cells cultured in standard monolayer conditions. Transplanted X-MET was able to mimic the activity of the excided EDL muscle, restoring the functionality of the damaged muscle. Our results suggest that X-MET is an ideal in vitro 3D muscle model that can be employed to repair muscle defects in vivo and to perform in vitro studies, limiting the use of live animals.