Abdullatif Alwasel

Bedsores, also known as pressure ulcers, are wounds caused by the applied external force (pressure) on body segments, thereby preventing blood supply from delivering the required elements to the skin tissue. Missing elements hinder the skin’s ability to maintain its health. It poses a significant threat to patients that have limited mobility. A new patented mattress design and alternative suggested designs aimed to reduce pressure are investigated in this paper for their performance in decreasing pressure. A simulation using Ansys finite element analysis (FEA) is carried out for comparison. Three-dimensional models are designed and tested in the simulation for a mattress and human anthropometric segments (Torso and Hip). All designs are carried out in solidworks. Results show that the original design can redistribute the pressure and decrease it up to 17% less than the normal mattress. The original design shows better ability to decrease the absolute amount of pressure on the body. However, increasing the surface area of the movable parts results in less pressure applied to the body parts. Thus, this work suggests changing the surface area of the cubes from 25 to 100 cm².

The purpose of this paper was to detect and separate the cluster intensity provided by Iron oxide nanoparticles (IO-NPs), in the MRI images, to investigate the drug delivery effectiveness. IO-NPs were attached to the macrophages and inserted into the eye of the inflamed mouse’s calf. The low resolution of MRI and the tiny dimension of the IO-NPs made the situation challenging. IO-NPs serve as a marker, due to their strong intensity in the MRI, enabling us to follow the track of the macrophages. An image processing procedure was developed to estimate the position and the amount of IO-NPs spreading inside the inflamed mouse leg. A fuzzy Clustering algorithm was adopted to select the region of interest (ROI). A 3D model of the femoral region was used for the detection and then the extraction IO-NPs in the MRI images. The results achieved prove the effectiveness of the proposed method to improve the control process of targeted drug delivered. It helps in optimizing the treatment and opens a promising novel research axis for nanomedicine applications.

The COVID-19 pandemic tested medical facilities’ readiness in terms of the number of available mechanical ventilators. Most countries raced to stock up on ventilators, which created a surge in demand and short in supply. Furthermore, other means of coping with the demand were proposed, such as using additive manufacturing. The purpose of this paper was to test whether the addition of 3D-printed splitters would help deliver required tidal volume to each patient, while supporting four patients on a single ventilator for 24 hours on pressure mode at 25-cm H2O, and to determine whether a fifth patient can be ventilated. The ventilation of four human lungs was simulated using 3D printed parts, a single ventilator, four test-lungs, and standard tubing. Peak pressure, positive end-expiratory pressure, total tidal volume, individual tidal volume, total minute volume, and individual tidal volume data were collected. Usage of a 3D printed small size splitter enabled a 26% increase in individual tidal volume compared to standard tubing and a series of two-way splitters. The ventilator was able to supply the required pressure and tidal volume for 24 hours. A single ventilator with a four-way splitter can ventilate four patients experiencing respiratory failure for at least 24 hours without interruption. The equipment cannot sustain ventilating a fifth patient owing to minute volume limitation. This study expands on an earlier study that tested similar circuitry and reveals that the desired individual tidal volume is achieved. However, further research is required to provide the monitoring ability of individual patient parameters and prevention of cross-contamination.

Masons regularly perform physically strenuous and demanding duties that may exceed a safe limit. Such activities can contribute to an early retirement for masons, resulting in a shortage of skilled craft workers. Previous ergonomic studies have observed that workers develop safer and more productive work techniques as they gain experience. This study aims to analyze relationships between body loads, experience, and work methods. Specifically, we expanded a previous pilot study by increasing the number of participants from 21 masons to 66 masons. Participants completed a prebuilt standard concrete masonry unit (CMU) lead wall using 45 CMUs. Motion capture suits were used to capture masons’ motions, and a combined biomechanical-productivity analysis was carried out to determine the loads experienced by major body joints. Exploiting the larger dataset, this study assessed how different experience groups load their joints and adjust their work techniques as the work height changes. The results suggested that experienced journeymen adopt similar work techniques distinct from those of less experienced workers. Further, training apprentices to adopt these work methods can help reduce occupational injuries and improve productivity. The results show that the journeymen with more than 20 years of experience adopt safer and more productive work techniques distinct from those of less experienced workers. The present study contributes to the body of knowledge on masons’ safety and productivity by providing an in-depth understanding of the linkage between body loads, work experience, techniques, and productivity. Additionally, the findings in this study are expected to have a greater impact when they are adopted to apprentice-training methods and applied to other high musculoskeletal-disorders-risk trades.

Summary: Due to physically demanding tasks in construction, workers are exposed to significant safety and health risks. Measuring and evaluating body kinematics while performing tasks helps to identify the fundamental causes of excessive physical demands, enabling practitioners to implement appropriate interventions to reduce them. Recently, non-invasive or minimally invasive motion capture approaches such as vision-based motion capture systems and angular measurement sensors have emerged, which can be used for in-field kinematics measurements, minimally interfering with on-going work. Given that these approaches have pros and cons for kinematic measurement due to adopted sensors and algorithms, an in-depth understanding of the performance of each approach will support better decisions for their adoption in construction. With this background, the authors evaluate the performance of vision-based (RGB-D sensor-, stereovision camera-, and multiple camera-based) and an angular measurement sensor-based (i.e., an optical encoder) approach to measure body angles through experimental testing. Specifically, measured body angles from these approaches were compared with the ones obtained from a marker-based motion capture system that has less than 0.1 mm of errors. The results showed that vision-based approaches have about 5–10 degrees of error in body angles, while an angular measurement sensor-based approach measured body angles with about 3 degrees of error during diverse tasks. The results indicate that, in general, these approaches can be applicable for diverse ergonomic methods to identify potential safety and health risks, such as rough postural assessment, time and motion study or trajectory analysis where some errors in motion data would not significantly sacrifice their reliability. Combined with relatively accurate angular measurement sensors, vision-based motion capture approaches also have great potential to enable us to perform in-depth physical demand analysis such as biomechanical analysis that requires full-body motion data, even though further improvement of accuracy is necessary. Additionally, understanding of body kinematics of workers would enable ergonomic mechanical design for automated machines and assistive robots that helps to reduce physical demands while supporting workers' capabilities.

A large portion of the injuries incurred on construction sites is due to the lack of posture awareness among labors and crew while performing highly physical tasks. Most of these injuries are caused by bad and inexpert poses. Posture and gesture awareness is therefore a critical issue that can substantially decrease the number of injuries on construction sites. This paper presents a framework for identifying the level of expertise based using a machine learning-based classification algorithm. Expert and inexpert performance are identified, in order to detect unsafe and/or unproductive postures taken workers, and avoid injuries to improve productivity. The proposed framework has two major components: (1) codebook generation based on a sensor-based body joints model representing the poses taken by the labors, and (2) training a support vector machine (SVM)-based classifier for identifying expert vs inexpert performance. A set of experiments, with twenty-one masons with varying levels-of-expertise, is designed for verification and validation of the proposed methodology. An inertial measurement unit (IMU) suit, with 17 sensors, is used for data collection. While the method is applicable to any types of construction trades, this study focuses on masonry brick laying tasks. Results show that the classifier is capable of identifying the two forms with a reasonable accuracy. Moreover, SVM linear kernel classifier generates the most accurate results with lowest computational cost in classification. The low computational cost of this classifier makes it feasible for on-the-field deployment.

Masonry is a highly physical occupation with a low retention rate in the workforce beyond 5 years. Predominantly, workers leave the workforce because of injuries. In particular, musculoskeletal disorders are depleting the construction workforce. Previous studies have contrasted the safety records of journeymen versus novices. However, no study to date has examined the combined effects of experience level on safety, productivity, and the balance between them. The purpose of this study is to fill this gap and to investigate the underlying reasons for these effects. Twenty-one masons distributed in four experience levels were recruited to build a 72-block six-course wall using standard concrete masonry units. Participants’ body kinematics were tracked using inertial measurement units and video cameras simultaneously. Biomechanical analysis was carried out to examine the loads experienced by major body joints during bricklaying. Results indicate that journeymen with more than 5 years of experience as well as novice masons work in ergonomically safer ways than apprentices with 1 and 3 years of experience. It was found that apprentices gain proficiency and increase productivity at the cost of higher risk of musculoskeletal injury. The results indicate that those masons who gain experience in ergonomic safety and productivity in tandem are more likely to remain in the workforce beyond the 5-year mark. Thus, it is essential to alter apprentice training to guarantee this outcome.

A novel application of phase-space warping (PSW) method to detect fatigue in the musculoskeletal system is presented. Experimental kinematic, force, and physiological signals are used to produce a fatigue metric. The metric is produced using time-delay embedding and PSW methods. The results showed that by using force and kinematic signals, an overall estimate of the muscle group state can be achieved. Further, when using electromyography (EMG) signals the fatigue metric can be used as a tool to evaluate muscles activation and load sharing patterns for individual muscles. The presented method will allow for fatigue evolution measurement outside a laboratory environment, which open doors to applications such as tracking the physical state of players during competition, workers in a plant, and patients undergoing in-home rehabilitation.

As muscles fatigue, their passive and active mechanical properties change increasing the susceptibility of the human body to damage. The state-of-the-art technique for muscle fatigue detection, EMG signals, is cumbersome. This paper presents a technique to detect fatigue by tracking a kinematic parameter of the musculoskeletal system. The method uses the time-history of a single joint angle to detect fatigue in the lower limbs. A sensor is mounted to the knee joint to measure the knee flexion angle. Time delay embedding is used to track the orbit of knee joint motions in a reconstructed phase-space. The reconstructed phase-space allows us to obtain information about other body parts and joints of the lower limb in addition to the knee joint, since they are all connected in an open kinematic chain. Long-time drift in the orbit location and shape in phase-space is quantified and used as a measure of lower limb fatigue. The proposed technique presents a mobile, wireless, and cheap method to assess fatigue that can act as an early warning system for the lower limb.