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

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

Detection of upper limb abrupt gestures for human–machine interaction using deep learning techniques

In the manufacturing industry the productivity is contingent on the workers' well-being, with operators at the center of the production process. Moreover, when human-machine interaction occurs, operators' safety is a key requirement. Generally, typical human gestures in manipulation tasks have repetitive kinetics, however external disturbances or environmental factors might provoke abrupt gestures, leading to improper interaction with the machine. The identification and characterization of these abrupt events has not yet been thoroughly studied. Accordingly, the aim of the current research was to define a methodology to ready identify human abrupt movements in a workplace, where manipulation activities are carried out. Five subjects performed three times a set of 30 standard pick-and-place tasks paced at 20 bpm, wearing magneto-inertial measurement units (MIMUs) on their wrists. Random visual and acoustic alarms triggered abrupt movements during standard gestures. The recorded signals were processed by segmenting each pick-and-place cycle. The distinction between standard and abrupt gestures was performed through a recurrent neural network applied to acceleration signals. Four different pre-classification methodologies were implemented to train the neural network and the resulting confusion matrices were compared. The outcomes showed that appropriate preprocessing of the data allows more effective training of the network and shorter classification time, enabling to achieve accuracy greater than 99% and F1-score better than 90%

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

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