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Currently, several methods are being applied to assess auditory temporal resolution in a controlled clinical environment via the measurements of gap detection thresholds (GDTs). However, these methods face two issues: the relatively long time required to perform the gap detection test in such settings and the potential of inaccessibility to such facilities. This article proposes a fast, affordable, and reliable application-based method for the determination of GDT either inside or outside the soundproof booth. The proposed test and the acoustic stimuli were both developed using the MATLAB® programming platform. GDT is determined when the subject is able to distinguish the shortest silent gap inserted randomly in one of two segments of white noise. GDTs were obtained from 42 normal-hearing subjects inside and outside the soundproof booth. The results of this study indicated that average GDTs measured inside the booth (5.12 ± 1.02 ms) and outside (4.78 ± 1.16 ms) were not significantly different. The measured GDTs were also comparable to that reported in the literature. In addition, the GDT screening time of the proposed method was approximately 5 minutes, a screening time that is much less than that reported by the literature. Data show that the proposed application was fast and reliable to screen GDT compared to the standard method currently used in clinical settings.

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...

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.

SummaryDue 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.

This paper presents a framework to classify work poses among groups of masons during the building of a standard wall of concrete masonry units. The experience of the group composed of masonry instructors and master masons averaged five times that of the other groups, their productivity was highest, and the loads on their joints were the lowest. Thus, they were deemed experts in this paper. Inertial measurement units (IMU) and video cameras were used to collect kinematic data of the masons, from which pose clusters were identified. A Support Vector Machine (SVM) algorithm was used to classify masons' poses into expert and inexpert classes based on the relative frequency of poses in the motions used to lay each of 945 masonry units. Two classification scenarios were tested. While both scenarios achieved similar levels of accuracy, 91.23% and 92.04% respectively, the processing time for binary classification was only 13 s compared to 523 s for inter-group multiclass SVM. Like characteristic vibration frequencies in machine diagnostics and system identification, the characteristic poses identified provide insight into differing methods between expert and less experienced masons. For example, results show that experts utilize fewer and more ergonomicaly safe poses, while being more productive, which indicates lower energy expenditure (less wasted motions). The classification method and the poses identified contribute knowledge to help develop affordable mason training systems that utilize IMU and video feedback to improve health and productivity of apprentice masons.

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, musculoskel...

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.

Purpose The U.S. Bureau of Labor1 2008 report stated that the number of workers aged 65 or older is expected to rise by 36.6% from 2006 to 2016. This rise is expected to accompany a 6.9% decrease in the number of workers between ages 16 to 24 and a slight increase by 2.4% for workers ages 25 to 54. This indicates that the industry will face an aging workforce problem that can affect capacity and productivity if workers’ health is not properly monitored. An essential problem for the aging workforce is musculoskeletal disorders (MSDs), specifically work-related musculoskeletal disorders (WMSDs). In 2008, shoulder WMSDs constituted 6.9% of all WMSDs in construction, and this percentage was substantially higher for crafts such as electricians, carpenters, and painters. The main risk factor for shoulder WMSDs is prolonged forceful overhead work. This paper is presents a method to monitor shoulder WMSDs risk factor development and to act as an alarm to prevent the disorder from happening. Method An exoskeleton is being developed that will be worn by a worker to wirelessly track upper-arm motion in 3 dimensions using an array of anisotropic magnetoresistive (AMR) sensors, deployed over workers’ upper-arms. The sensory system will be controlled using a microcontroller with the ability to save data on an SD card for post-processing. Results & Discussion Alwasel et al. measured the angle of the upper-arm to trunk using AMR-sensors in an unpublished thesis. The problem with the use of AMR was the assembly itself since the sensor was not held at the center of joint rotation. However the device presented here will place AMRsensors at the center of joint rotation by extending the joint axis of rotation outside the body. This will provide enough data to help assess whether a worker is at risk of developing shoulder injury. This technique will help reduce the number workers that leave the workforce because of injury, and help maintain a good working environment. This technique will also assist in developing and applying health and safety guidelines to overhead work. Thus, it may ultimately help extend the productive age of the construction workforce.

Much of the developed world’s construction workforce is increasing in average age, and yet construction workers typically retire well before they reach the age of sixty. One reason is that their bodies are worn out because of the nature of the work. We therefore face the challenge of both reducing their physical stress and increasing their productive work life, if we wish to avert an economic and social crunch given the demographic trends towards an aging population in most developed countries. In particular, recent statistics from the U.S Department of labor show that 6.9% of all Workrelated Musculoskeletal Disorders (WMSDs) among workers in 2008 affected shoulders; this percentage becomes much larger for electricians, carpenters, and related construction crafts. The cost to our industry and to society is huge, and it is unnecessary. Reduction of certain types of movements and improvements in posture can result in reduced rates of shoulder WMSDs and in extended work lives. This can be done with a combination of robotics, work re-design, and work monitoring. This paper provides the statistical background and economic analysis that supports the scope of the problem, presents background on the kinematics of shoulder movement, and explains the biomechanics and causes of shoulder injuries. Then, preliminary results are presented for a prototype of a simple, low-cost, sensing solution for automatically monitoring undesirable movements and patterns of motion. It is expected that this could be broadly implemented to help reduce Construction Work-related Musculoskeletal Disorders (WMSDs).

Musculoskeletal disorders (MSDs) threaten the wellbeing and livelihood of a large number of construction workers incurring a significant cost to society. We present a new method to monitor and diagnose MSD risks in the workplace. The sensing unit of the system is an optical encoder encompassed within a non-intrusive exoskeleton to measure the joint angle of interest. This sensor can be applied to ball-and-socket and hinge-type joints of the human body, such as the shoulder, elbow, and knee joints. The system is contactless and does not require markers or cameras. Angle measurements are acquired directly without mathematical post-processing, thereby avoiding numerical noise and drift challenges. The system is a simple, robust, and deployable, but it currently lacks resolution of parallel degrees of freedom.