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 estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

In recent years, the use of inertial-based systems has been applied to remote rehabilitation, opening new perspectives for outpatient assessment. In this study, we assessed the accuracy and the concurrent validity of the angular measurements provided by an inertial-based device for rehabilitation with respect to the state-of-the-art system for motion tracking. Data were simultaneously collected with the two systems across a set of exercises for trunk and lower limbs, performed by 21 healthy participants. Additionally, the sensitivity of the inertial measurement unit (IMU)-based system to its malpositioning was assessed. Root mean square error (RMSE) was used to explore the differences in the outputs of the two systems in terms of range of motion (ROM), and their agreement was assessed via Pearson’s correlation coefficient (PCC) and Lin’s concordance correlation coefficient (CCC). The results showed that the IMU-based system was able to assess upper-body and lower-limb kinematics with a mean error in general lower than 5° and that its measurements were moderately biased by its mispositioning. Although the system does not seem to be suitable for analysis requiring a high level of detail, the findings of this study support the application of the device in rehabilitation programs in unsupervised settings, providing reliable data to remotely monitor the progress of the rehabilitation pathway and change in patient’s motor function.

Excessive values of force on L5/S1 joint can cause work-related musculoskeletal disorders, such as low back pain. Currently, the reference solution for estimating such variables is the combination of optoelectronic system and force platform, used for calculating the bottom up inverse dynamics in laboratory settings. Here we propose and validate a novel, completely wearable solution, composed by twelve inertial measurement units and pressure insole sensors. We validate the wearable solution with respect to the output of the reference solution, with data collected simultaneously on a subject performing lifting and releasing tasks with two different loads. The results are encouraging towards the use of the wearable methodology, considering the great impact of such a solution in a real manufacturing scenario.

Individuals with patellofemoral pain syndrome present biomechanical alterations of lower limb kinematics and difficulties in postural control. Taping techniques like McConnell’s were adopted to reduce pain and avert lower extremity injury, especially on the knee joint complex. Although the patellar taping appears to be an effective tool in reducing patellofemoral pain by stabilizing the patellofemoral joint, its immediate effects on balance, and trunk and lower limb kinematics are still not clear. The assessment of the immediate taping-induced biomechanical modifications on balance and kinematics could provide a deeper insight into the mechanical actions of the McConnell’s. Nine healthy young adults (age: 29.1 ± 4.7 years) were equipped with eight wireless inertial measurement units placed on trunk and lower limbs. Participants were asked to perform the Star Excursion Balance Test (SEBT) and the Single Leg Squat (SLS) task in five different directions on a force plate. Kinematics of lumbosacral and lower limb joints, as well as balance indices such as spatiotemporal parameters of the center of pressure, were estimated with and without the McConnell taping. No mechanical effects were observed for spatiotemporal indices of the center of pressure and for all considered kinematic parameters, except for kinematics of the knee. Results demonstrated significant differences due to taping condition (p=0.01) for the knee joint in the SLS task. A similar trend was observed in SEBT. This interesting result highlights the instantaneous effect of McConnell taping on knee joint complex, without impairing any self-selected strategies adopted by healthy subjects.