Human upper body motion tracking for human-machine interaction in industrial applications

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Comparison of IMU set-ups for the estimation of gait spatio-temporal parameters in an elderly population

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

Gait Phases Detection in Elderly using Trunk-MIMU System

The increasing interest towards wearable Magnetic Inertial Measurement Units (MIMUs) for gait analysis is justified by their low invasiveness, confirmed repeatability and complete independence from laboratory constraints. However, some crucial doubts about the identification of a suitable sensor set-up and algorithm in different gait conditions and populations still exist. In this context, the principal aim of the present study was to investigate the effect of different walking conditions on the accuracy of gait phases detection with a trunk-MIMU system. Eleven healthy elderly subjects performed gait trials in four different walking conditions (fast speed, normal speed, slow speed and normal speed with dual-task). A stereophotogrammetric system was adopted as gold standard. The accuracy of the estimation of stance and swing phases was evaluated from the comparison of trunk-MIMU to the stereophotogrammetric system. Mean error values smaller than 0.03 s confirmed the accuracy of the tru nk-MIMU algorithm for an elderly population. Consequently, trunk-MIMU system can be considered suitable for the characterization of gait phases in elderly subjects regardless of walking conditions

An ISB-consistent Denavit-Hartenberg model of the human upper limb for joint kinematics optimization: validation on synthetic and robot data during a typical rehabilitation gesture

Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMU s) represent a suitable solution because of their unobtrusiveness, portability, and low-cost. However, the time-integration of the gyroscope angular velocity leads to an unbounded orientation drift affecting both angular and linear displacements over long observation interval. In this work, a Denavit-Hartenberg model of the upper limb was defined in accordance with the guidelines of the International Society of Biomechanics and exploited to design an optimization kinematics process. This procedure estimated the joint angles by minimizing the difference between the modelled and IMU-driven orientation of upper arm and forearm. In addition, reasonable constraints were added to limit the drift influence on the final joint kinematics accuracy. The validity of the procedure was tested on synthetic and experimental data acquired with a robotic arm over 20 minutes. Average rms errors amounted to 2.8 deg and 1.1 for synthetic and robot data, respectively. Clinical Relevance - The proposed method has the potential to improve robustness and accuracy of multi-joint kinematics estimation in the general contexts of home-based tele-rehabilitation interventions. In this respect adoption of multi-segmental kinematic model along with physiological joint constraints could contribute to address current limitations associated to unsupervised analysis in terms of monitoring and outcome assessment

Regeneration of articular cartilage: Scaffold used in orthopedic surgery. A short handbook of available products for regenerative joints surgery

Introduction: Restoring defects of load-bearing connective tissues such as articular cartilage resulting from traumas, degenerative or age-related diseases remains a significant clinical challenge for clinicians due to the limited inherent repair capacity of articular cartilage. Tissue engineering has emerged as a potential alternative to the traditional surgical techniques, as it can be effectively used to regenerate bone, cartilage and the bone-cartilage interface. Several scaffold strategies have been developed and evaluated for osteochondral defect repair. Materials and methods: The classes of polymers (scaffold-based proteins, scaffold-base polysaccharides and synthetic scaffolds) and Hydrogels have been reviewed trough literature and market search. The study focused on their respective properties and analyzed advantages and disadvantages of each of them. Discussion: Clinical studies demonstrated improved cartilage regeneration thanks to the implantation of biomaterials after bone marrow stimulation. New cartilage can be engineered in vivo by transplanting chondrocytes seeded into a three-dimensional scaffold and this novel scaffold has mechanical properties that can be comparable to native cartilage and could be used to repair large osteochondral joints defects. Anyway, there is still space for improvement regarding clinical outcome and tissue quality

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

Experimental Analysis and Multibody Simulation of Electric Kick Scooter Braking Maneuver

Environmental and mobility policies involving big city centers have recently brought to a global spreading of electric micro-vehicles. Electric kick scooters (e-scooters) are indeed leading this category for popularity since their friendly integration in existing urban mobility systems and vehicle sharing services. The main peculiarities distinguishing e-scooters from other motorized two-wheeled vehicles stand in the small vehicle inertia and the driver’s standing position. Since e-scooters are quite recent, scientific research aiming at improving safety and ride comfort is still in progress. The purpose of this research activity is an experimental and numerical investigation of e-scooter emergency braking maneuvers. To achieve this goal, a commercial vehicle is analyzed. On-road tests are conducted instrumenting both the e-scooter and the rider to monitor vehicle performance and human body kinematics. A multibody model representing the whole system is developed in Matlab/Simscape Multibody. Specific attention is given to the rider model. Viscous-elastic properties are included in the joints to reproduce the rider’s dynamic response during the braking maneuver. The model parameters are identified through an optimization process, matching simulated results with experimental data. Results show that the heavy braking of the electric kick scooter is strictly dependent on the rider dynamics. Moreover, a passive human body model can properly represent the human response in this specific maneuver

Comparison of different motion capture setups for Gait Analysis : Validation of spatio-temporal parameters estimation

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

Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

(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

Modeling and Kinematic Optimization of the Human Upper Limb for Collaborative Robotics

The emerging research field developed to optimize the collaboration of human-robot systems for Industry 4.0 gives a central role to the tracking of human motion. Inertial Measurement Units (IMUs) represent a suitable solution to unobtrusively monitor workers in the industrial environment. However, the computation of IMUs orientation usually causes drift problems and affects the kinematics estimate. Moreover, the traditional Euler angles decomposition from the mutual independent orientation of IMUs is affected by mathematical singularities and it does not include joint constraints to avoid violation of physiological motion range. To overcome these limitations, this work aimed at developing a Denavit-Hartenberg upper limb model consistent with standard biomechanical guidelines and an optimization framework for the real-time tracking of human motion. At each time step, the joint variables of the model were estimated minimizing the difference between the modeled segments orientations and those obtained with the sensor fusion. The proposed method was validated with synthetic and real robot data, verifying the influence of a considerable drift on the estimate accuracy. Finally, a comparison between the optimized joint kinematics and the one obtained with traditional methods was made