1,591 research outputs found
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
Kinematic and dynamic assessment of trunk exoskeleton
In Industry 4.0, wearable exoskeletons have been proposed as collaborative robotic devices to partially assist workers in heavy and dangerous tasks. Despite the recent researches, proposed prototypes and commercial products, some open issues concerning development, improvements and testing still exist. The current pilot study proposed the assessment of a proper biomechanical investigation of passive trunk exoskeleton effects on the human body. One healthy subject performed walking, stoop and semisquat tasks without, with exoskeleton no support and with exoskeleton with support. 3D Kinematic (angles, translations) and dynamic (interface forces) parameters of both human and exoskeleton were estimated. Some differences were pointed out comparing task motions and exoskeleton conditions. The presented preliminary test revealed interesting results in terms of different human joints coordination, interface forces exchanged at contact points and possible misalignment between human and device. The present study could be considered as a starting point for the investigation of exoskeleton effectiveness and interaction with the user
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
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
Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System
In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The “virtual stiffness” method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations
Evaluation on Implementing an Active Braking System in Wheelchair Rear-Mounted Power-Assisted Device
Power-Assisted Devices (PADs) for wheelchairs are becoming popular tools to enhance propulsion capabilities and to assist wheelchair users to perform daily activities. PADs include Pushrim Activated Power-Assisted Wheelchairs, joy-stick-driven wheels, front-end attachments, and rear-end attachments. Considering the latest, they are not equipped with any active braking system. This could affect the handling of the wheelchair and introduce safety concerns.
The paper aims to assess the performance of a rear add-on during driving and braking conditions, and to investigate the implementation and effectiveness of a servo braking system. A dynamic multibody model of a wheelchair has been developed and the dynamic of the system has been analyzed. To enhance the braking effectiveness, an additional preload torque between the wheelchair and the device has been modelled. Simulations have been performed for different braking torques. The results show that the introduction of a mounting preload positively affects the braking effectiveness, and it assists the user to perform part of the braking action
Blockchain for electronic medical record: assessing stakeholders' readiness for successful blockchain adoption in health-care
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
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