Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators' Psychological Status and Pain Syndromes.

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators' psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks-concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators' poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).

The compliance of head-mounted industrial PPE by using deep learning object detectors.

The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author's collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)-which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types-while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.

In vitro and in silico testing of partially and fully bioresorbable vascular scaffold.

Coronary artery disease (CAD), one of the leading causes of death globally, occurs due to the growth of atherosclerotic plaques in the coronary arteries, causing lesions which restrict the flow of blood to the myocardium. Percutaneous transluminal coronary angioplasty (PTCA), including balloon angioplasty and coronary stent deployment is a standard clinical invasive treatment for CAD. Coronary stents are delivered using a balloon catheter inserted across the lesion. The balloon is inflated to a nominal pressure, opening the occluded artery, deploying the stent and improving the flow of blood to the myocardium. All stent manufacturers have to perform standard in vitro mechanical testing under different physiological conditions. In this study, partially and fully bioresorbable vascular scaffolds (BVS) from Boston Scientific Limited have been examined in vitro and in silico for three different test methods: inflation, radial compression and crush resistance. We formulated a material model for poly-L-lactic acid (PLLA) and implemented it into our in-house software tool. A comparison of the different experimental results is presented in the form of graphs showing displacement-force curves, diameter - load curves or diameter - pressure curves. There is a strong correlation between simulation and real experiments with a coefficient of determination (R2) > 0.99 and a correlation coefficient (R) > 0.99. This preliminary study has shown that in-silico tests can mimic the applicable ISO standards for mechanical in vitro stent testing, providing the opportunity to use data generated using in-silico testing to partially or fully replacing the mechanical testing required for regulatory submission.

A computational study of circulating large tumor cells traversing microvessels.

Circulating tumor cells (CTCs) are known to be a harbinger of cancer metastasis. The CTCs are known to circulate as individual cells or as a group of interconnected cells called CTC clusters. Since both single CTCs and CTC clusters have been detected in venous blood samples of cancer patients, they needed to traverse at least one capillary bed when crossing from arterial to venous circulation. The diameter of a typical capillary is about 7µm, whereas the size of an individual CTC or CTC clusters can be greater than 20µm and thus size exclusion is believed to be an important factor in the capillary arrest of CTCs - a key early event in metastasis. To examine the biophysical conditions needed for capillary arrest, we have developed a custom-built viscoelastic solid-fluid 3D computational model that enables us to calculate, under physiological conditions, the maximal CTC diameter that will pass through the capillary. We show that large CTCs and CTC clusters can successfully cross capillaries if their stiffness is relatively small. Specifically, under physiological conditions, a 13µm diameter CTC passes through a 7µm capillary only if its stiffness is less than 500Pa and conversely, for a stiffness of 10Pa the maximal passing diameter can be as high as 140µm, such as for a cluster of CTCs. By exploring the parameter space, a relationship between the capillary blood pressure gradient and the CTC mechanical properties (size and stiffness) was determined. The presented computational platform and the resulting pressure-size-stiffness relationship can be employed as a tool to help study the biomechanical conditions needed for capillary arrest of CTCs and CTC clusters, provide predictive capabilities in disease progression based on biophysical CTC parameters, and aid in the rational design of size-based CTC isolation technologies where CTCs can experience large deformations due to high pressure gradients.

Mapping cyclic stretch in the postpneumonectomy murine lung.

In many mammalian species, the removal of one lung [pneumonectomy (PNX)] is associated with the compensatory growth of the remaining lung. To investigate the hypothesis that parenchymal deformation may trigger lung regeneration, we used respiratory-gated micro-computed tomography scanning to create three-dimensional finite-element geometric models of the murine cardiac lobe with cyclic breathing. Models were constructed of respiratory-gated micro-computed tomography scans pre-PNX and 24 h post-PNX. The computational models demonstrated that the maximum stretch ratio map was patchy and heterogeneous, particularly in subpleural, juxta-diaphragmatic, and cephalad regions of the lobe. In these parenchymal regions, the material line segments at peak inspiration were frequently two- to fourfold greater after PNX; some regions of the post-PNX cardiac lobe demonstrated parenchymal compression at peak inspiration. Similarly, analyses of parenchymal maximum shear strain demonstrated heterogeneous regions of mechanical stress with focal regions demonstrating a threefold increase in shear strain after PNX. Consistent with previously identified growth patterns, these subpleural regions of enhanced stretch and shear strain are compatible with a mechanical signal, likely involving cyclic parenchymal stretch, triggering lung growth.

Multiscale modeling of circular and elliptical particles in laminar shear flow.

Drug delivery systems for cancer prevention and pain management have been improved related to classical cancer chemotherapy. Nanotechnology with nanoparticles offers new ways in transport of drug molecules and contrast agents by the blood flow through the circulatory system. In this study, we use multiscale mesoscopic bridging procedure of the finite elements (FE) coupled with dissipative particle dynamics (DPD) and lattice Boltzmann (LB) method to model the motion of circular and elliptical particles in a 2-D laminar flow. Four examples are considered: 1) one sedimenting cylinder in a channel, 2) two sedimenting cylinders in a channel, 3) motion of four elliptical particles in a linear shear flow, and 4) motion of circular and elliptical particle in the arterial bifurcation geometry. A good agreement with solution from the literature available was found. These results show that the multiscale approach with coupled FE and DPD/LB methods can effectively be applied to model motion of micro/nanoparticles for a drug delivery system.

Computer simulation of flow and mixing at the duodenal stump after gastric resection.

AIM: To investigate the flow and mixing at the duodenal stump after gastric resection, a computer simulation was implemented. METHODS: Using the finite element method, two different Billroth II procedure cases (A and B) were modeled. Case A was defined with a shorter and almost straight duodenal section, while case B has a much longer and curved duodenal section. Velocity, pressure and food concentration distribution were determined and the numerical results were compared with experimental observations. RESULTS: The pressure distribution obtained by numerical simulation was in the range of the recorded experimental results. Case A had a more favorable pressure distribution in comparison with case B. However, case B had better performance in terms of food transport because of more continual food distribution, as well as better emptying of the duodenal section. CONCLUSION: This study offers insight into the transport process within the duodenal stump section after surgical intervention, which can be useful for future patient-specific predictions of a surgical outcome.