A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

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

Gait parameters of elderly subjects in single-task and dual-task with three different MIMU set-ups

The increasing average age of the population emphasizes the strong correlation between cognitive decline and gait disorders of elderly people. Wearable technologies such as magnetic inertial measurement units (MIMUs) have been ascertained as a suitable solution for gait analysis. However, the relationship between human motion and cognitive impairments should still be investigated, considering outcomes of different MIMU set-ups. Accordingly, the aim of the present study was to compare single-task and dual-task walking of an elderly population by using three different MIMU set-ups and correlated algorithms (trunk, shanks, and ankles). Gait sessions of sixteen healthy elderly subjects were registered and spatio-temporal parameters were selected as outcomes of interest. The analysis focused both on the comparison of walking conditions and on the evaluation of differences among MIMU set-ups. Results pointed out the significant effect of cognition on walking speed (p = 0.03) and temporal parameters (p ≤ 0.05), but not on the symmetry of gait. In addition, the comparison among MIMU configurations highlighted a significant difference in the detection of gait stance and swing phases (for shanks-ankles comparison p < 0.001 in both single and dual tasks, for trunk-ankles comparison p < 0.001 in single task and p < 0.01 in dual task). Overall, cognitive impact and MIMU set-ups revealed to be fundamental aspects in the analysis of gait spatio-temporal parameters in a healthy elderly population

Evaluation of spinal posture during gait with inertial measurement units

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

Wearable MIMUs for the identification of upper limbs motion in an industrial context of human-robot interaction

The automation of human gestures is gaining increasing importance in manufacturing. Indeed, robots support operators by simplifying their tasks in a shared workspace. However, human-robot collaboration can be improved by identifying human actions and then developing adaptive control algorithms for the robot. Accordingly, the aim of this study was to classify industrial tasks based on accelerations signals of human upper limbs. Two magnetic inertial measurement units (MIMUs) on the upper limb of ten healthy young subjects acquired pick and place gestures at three different heights. Peaks were detected from MIMUs accelerations and were adopted to classify gestures through a Linear Discriminant Analysis. The method was applied firstly including two MIMUs and then one at a time. Results demonstrated that the placement of at least one MIMU on the upper arm or forearm is suitable to achieve good recognition performances. Overall, features extracted from MIMUs signals can be used to define and train a prediction algorithm reliable for the context of collaborative robotics

Multi-segments kinematic model of the human spine during gait

The complex biomechanical structure of the human spine requires a deep investigation to properly describe its physiological function and its kinematic contribution during motion. The computational approach allows the segmentation of the human spine into several rigid bodies connected by 3D joints. Despite the numerous solutions proposed by previous literature studies based on both inertial and stereophotogrammetric systems, the modelling of the human spine is characterized by some limitations such as the lack of standardization. Accordingly, the present preliminary study focused on the development of a multi-segments kinematic model of the human spine and its validation during gait trials. Three-dimensional spinal angular patterns and ranges of motion of one healthy young subject were considered as outcomes of interest. They were obtained by applying the YXZ Euler angles convention to the custom model. First, results were compared with those of the standard Plug-in-Gait full-body model, which segments the human spine into pelvis and trunk segments. Then, outcomes of the multi-segments model were compared with those obtained using the Tilt-Twist method. Overall, results stressed the importance of the spine segmentation, the major angular contributions of spinal regions during gait (Medium-Lumbar segments for lateral bending and flexion-extension, Thoracic-Medium segments for axial rotation), and the reliability of the proposed custom model (differences between Euler angles method and Tilt-Twist method lower than 0.5° in most cases). Future analysis on a larger healthy population and in the clinical context might be implemented to optimize, standardize and validate the proposed human spine model

Collection and analysis of human upper limbs motion features for collaborative robotic applications

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

Upper limbs cranking for post-stroke rehabilitation: A pilot study on healthy subjects

Since one of the major consequences of stroke is hemiparesis, the rehabilitation of upper limbs is necessary to improve the quality of life. Arm cranking gesture represents an alternative rehabilitation tool, especially if accompanied by a biofeedback involving and motivating patients. The aim of this pilot study was twofold: (1) to evaluate the effect of a visual and virtual biofeedback on arm cranking gesture and (2) to estimate the duration of pull and push phases of the crank cycle. Nine healthy and young subjects were involved in the test and were asked to perform the arm cranking gesture in different conditions. A stereophotogrammetric system was adopted to create a virtual, visual and real time biofeedback of cadence, to measure the real cadence of participants and to estimate push and pull phases durations. Results showed that the biofeedback helped subjects to follow an externally imposed cadence. Furthermore, the pull phase resulted to be slightly longer than the push one, although the angular amplitude of the two phases suggested they were the same

Estimation of Force Effectiveness and Symmetry During Kranking Training

The third Sustainable Development Goal of the 2030 Agenda promotes healthy lives and well-being for all people of all ages. A good way to ensure a healthy lifestyle is to perform daily physical activity. Among different exercises of cardiovascular training, kranking is a program that involves arm-cranking gesture performed on a stationary handbike. In order to correctly perform this activity, biomechanical parameters have to be monitored. The present pilot study aimed at developing a setup for the quantitative evaluation of the force effectiveness and symmetry during different conditions of upper limbs kranking. One healthy young subject performed different tasks of steady-state cycling on varying cadence, braking torque, and motion pattern. Strain gauges positioned on the handles of a commercial arm-cranking machine allowed the estimation of total and effective forces applied by the user. Moreover, an optical motion capture system was adopted to evaluate the kinematics of the upper limbs during the movement. Comparing the total and the effective forces, the effectiveness of the gesture was evaluated for all testing conditions. Overall, results suggest that the developed setup is adequate to efficaciously identify possible alterations of performance parameters during upper limbs kranking

Using a robot calibration approach toward fitting a human arm model

In the context of Industry 4.0, the human-robot interaction (HRI) can be improved by tracking the human arm in the workspace shared with the robot. This goal takes advantage of a customized human arm modeling and it should be conveniently achieved with a limited number of sensors and a reduced computational time. In this paper, considering the analogy between human and robotic arms, a new method for the identification of a custom-made human arm model was inspired by a robot calibration process. The Denavit-Hartenberg (DH) parameters of the arm model were estimated recording a suitable number of hand poses. Hence, a robotic arm was exploited to test the new method. To simplify the fitting procedure of a reliable robot model, the minimum number of the necessary end-effector (EE) poses was investigated. Through an optoelectronic system, the EE pose trajectory of a UR3 robot was recorded. The optimization of the DH parameters was repeatedly run decreasing the downsampling frequency of the acquired data and then the trajectory error was evaluated. A new reference dataset of robot configurations was acquired permutating the joints degrees of freedom among values of 0, +90, or −90°. Hence, the method to fit the model considering few EE poses was tested on six robot configurations randomly selected from the dataset. Overall, trajectory errors highlighted the applicability of this method in the context of HRI