Industry 4.0 has promoted the concept of automation, supporting workers with robots while maintaining their central role in the factory. To guarantee the safety of operators and improve the effectiveness of the human-robot interaction, it is important to detect the movements of the workers. Wearable inertial sensors represent a suitable technology to pursue this goal because of their portability, low cost, and minimal invasiveness. The aim of this narrative review was to analyze the state-of-the-art literature exploiting inertial sensors to track the human motion in different industrial scenarios. The Scopus database was queried, and 54 articles were selected. Some important aspects were identified: (i) number of publications per year; (ii) aim of the studies; (iii) body district involved in the motion tracking; (iv) number of adopted inertial sensors; (v) presence/absence of a technology combined to the inertial sensors; (vi) a real-time analysis; (vii) the inclusion/exclusion of the magnetometer in the sensor fusion process. Moreover, an analysis and a discussion of these aspects was also developed.

Inertial measurement units (IMUs) are increasingly used in gait analysis because of their portability, low cost and long-distance measuring. The aim of this study focused on the comparison of 2 different inertial sensors setups and algorithms respect to a reference measure system for the estimation of gait spatio-temporal parameters. (a) A marker stereo photogrammetric system (Optitrack) was used as reference goldstandard. Two IMU setups were adopted for the comparison of different algorithms: (b) a single sensor positioned on the trunk, and (c) 2 sensors positioned on the heels. Three healthy subjects performed gait trials at 3 different self-selected speeds: (α) normal, (β) slower than normal, (γ) higher than normal. Data analysis considered signals that had been registered simultaneously by the three setup instrumentations. Among the IMUs signals, acceleration and angular velocity were considered and used for gait parameters estimation. Signal post-processing was performed through algorithms analyzing signals with respect to the local sensor axes, avoiding cumbersome pre-processing. The absolute errors among the spatio-temporal gait parameters obtained by the 3 setups were evaluated. Overall, IMUs allowed an accurate parameters estimation, at all the performed velocities. A consistent correspondence was confirmed by the comparison of estimated spatio-temporal parameters. However, for most of them, the trunk configuration revealed smaller errors with respect to the heels configuration. This may be explained by a better identification of gait events guaranteed by the trunk IMU algorithm. The results demonstrated the suitability and accuracy of the trunk IMU setup and algorithm for the estimation of spatio-temporal parameters during gait. Besides, the trunk IMU setup requires the use of only one sensor instead of two.

(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.

The increasing average age emphasizes the importance of gait analysis in elderly populations. Inertial Measurement Units (IMUs) represent a suitable wearable technology for the characterization of gait by estimating spatio-temporal parameters (STPs). However, the location of inertial sensors on the human body and the associated algorithms for the estimation of gait STPs play a fundamental role and are still open challenges. Accordingly, the aim of this work was to compare three IMUs set-ups (trunk, shanks, and ankles) and correspondent algorithms to a gold standard optoelectronic system for the estimation of gait STPs in a healthy elderly population. In total, 14 healthy elderly subjects walked barefoot at three different speeds. Gait parameters were assessed for each IMUs set-up and compared to those estimated with the gold standard. A statistical analysis based on Pearson correlation, Root Mean Square Error and Bland Altman plots was conducted to evaluate the accuracy of IMUs. Even though all tested set-ups produced accurate results, the IMU on the trunk performed better in terms of correlation ( R ≥ 0.8), RMSE (0.01–0.06 s for temporal parameters, 0.03–0.04 for the limp index), and level of agreement (−0.01 s ≤ mean error ≤ 0.01 s, −0.02 s ≤ standard deviation error ≤ 0.02 s), also allowing simpler preparation of subjects and minor encumbrance during gait. From the promising results, a similar experiment might be conducted in pathological populations in the attempt to verify the accuracy of IMUs set-ups and algorithms also in non-physiological patterns.

The increasing number of postural disorders emphasizes the central role of the vertebral spine during gait. Indeed, clinicians need an accurate and non-invasive method to evaluate the effectiveness of a rehabilitation program on spinal kinematics. Accordingly, the aim of this work was the use of inertial sensors for the assessment of angles among vertebral segments during gait. The spine was partitioned into five segments and correspondingly five inertial measurement units were positioned. Articulations between two adjacent spine segments were modeled with spherical joints, and the tilt–twist method was adopted to evaluate flexion–extension, lateral bending and axial rotation. In total, 18 young healthy subjects (9 males and 9 females) walked barefoot in three different conditions. The spinal posture during gait was efficiently evaluated considering the patterns of planar angles of each spine segment. Some statistically significant differences highlighted the influence of gender, speed and imposed cadence. The proposed methodology proved the usability of inertial sensors for the assessment of spinal posture and it is expected to efficiently point out trunk compensatory pattern during gait in a clinical context.

The rapid spread of Inertial Measurement Units (IMUs) has revolutionized human motion analysis, providing significant advantages over traditional systems