Publications by authors named "Lora A Cavuoto"

Wet bulb globe temperature (WBGT) is a commonly used measure to predict heat strain in workers. Different combinations of environmental conditions can create equivalent WBGT, yet it remains unknown whether biophysical, physiological, and perceptual responses vary when working in different but equivalent hot conditions. The purpose of the study was to compare body heat storage and physiological and perceptual strain during walking in hot-dry and warm-wet conditions of the same WBGT.

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OCCUPATIONAL APPLICATIONSMusculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue.

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The goal of this study was to evaluate the relationship between ratings of perceived exertion (RPE) and relative strength with respect to baseline for a fatiguing free dynamic task targeting the upper extremity, namely simulated order picking, and determine whether the relationship remains the same for different conditions (i.e., pace and weight) and with fatigue.

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Community-dwelling individuals with chronic stroke used a novel, portable rehabilitation system, mRehab, that uses a smartphone app coupled with 3D printed objects resembling daily use items. The objectives of this study include evaluating participant's approach and nature of engagement with mRehab and identifying factors that influenced the users' engagement with mRehab. An explanatory mixed-method approach was used.

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Repetitive task performance is a leading cause of musculoskeletal injuries among order-picking workers in warehouses. The repetition of lifting tasks increases the risk of back and shoulder injuries among these workers. While lifting in this industry is composed of loaded and unloaded picking and placing, the existing literature does not address the separate analysis of the biomechanics of the back and shoulder for these events.

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Existing ergonomic risk assessment tools require monitoring of multiple risk factors. To eliminate the direct observation, we investigated the effectiveness of an end-to-end framework that works with the data from a single wearable sensor. The framework is used to identify the performed task as the major contextual risk factor, and then estimate the task duration and number of repetitions as two main indicators of task intensity.

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Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance.

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Background: Smart technology use in rehabilitation is growing and can be used remotely to assist clients in self-monitoring their performance. With written home exercise programs being the commonly prescribed form of rehabilitation after discharge, mobile health technology coupled with task-oriented programs can enhance self-management of upper extremity training. In the current study, a rehabilitation system, namely mRehab, was designed that included a smartphone app and 3D-printed household items such as mug, bowl, key, and doorknob embedded with a smartphone.

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Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM).

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Article Synopsis
  • The study aimed to create an AI method using image processing to distinguish between CD117(+) oncocytoma and chromophobe renal cell carcinoma (ChRCC) by analyzing peak early-phase enhancement ratios from CT images.
  • A convolutional neural network (CNN) was trained on data from 192 patients to accurately identify kidney and tumor areas and evaluate PEER measurements, which were compared to traditional expert opinions for performance assessment.
  • Results showed high accuracy for tumor classification (95%), with the CNN model providing reliable segmentation and effective discrimination between the two tumor types through deep learning techniques.
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Background: Residual sensorimotor deficits are common following stroke. While it has been demonstrated that targeted practice can result in improvements in functional mobility years post stroke, there is little to support rehabilitation across the lifespan. The use of technology in home rehabilitation provides an avenue to better support self-management of recovery across the lifespan.

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Objective: We present a literature review on workplace physical fatigue interventions, focusing on evaluating the methodological quality and strength of evidence.

Background: Physical fatigue is a recognized workplace problem, with negative effects on performance and health-related complaints. Although many studies have focused on the mechanisms and consequences of fatigue, few have considered the effectiveness of interventions to mitigate fatigue.

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The main goal of this study was to investigate the reliability of muscle strength across different levels of obesity. A sample of 142 healthy subjects performed maximum voluntary isometric contractions for shoulder flexion and trunk extension on each of four days. Subjects were recruited into one of three groups, non-obese, overweight, or obese, based on body mass index (BMI).

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Purpose: To develop and validate an automated assessment of surgical performance (AASP) system for objective and computerized assessment of pelvic lymph node dissection (PLND) as an integral part of robot-assisted radical cystectomy (RARC) using console-feed videos recorded during live surgery.

Methods: Video recordings of 20 PLNDs were included. The quality of lymph node clearance was assessed based on the features derived from the computer vision process which include: the number and cleared area of the vessels/nerve (N-Vs); image median color map; and mean entropy (measures the level of disorganization) in the video frame.

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Background: This study systematically examined obesity research in occupational safety and health regarding the use of various obesity measurement and classification methods.

Methods: A systematic search of the PubMed database on English language publications from 2000 to 2015 using related keywords and search of citations resulted in selection of 126 studies. They were categorized into two groups based on their main research question: 1) general physical or mental work-related functioning; and 2) task or body part specific functioning.

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The purpose of this study was to examine task and sex differences in forearm muscle oxygenation, measured using near infrared spectroscopy, during sustained submaximal handgrip exercises. Forty-eight adults (50% males) performed fatiguing handgrip exercises at 20, 40, 60 and 80% of their maximum handgrip strength. While males and females exhibited similar levels of relative fatigability, forearm oxygenation was found to be task (i.

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Limitations following stroke make it one of the leading causes of disability. The current medical pathway provides intensive care in the acute stages, but rehabilitation services are commonly discontinued after one year. While written home exercise programs are regularly prescribed at the time of discharge, compliancy is an issue.

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This study explored concurrent effects of six work psychosocial factors on current participation and the self-reported likelihood of future participation in workplace wellness programs using a cross-sectional survey, an ad hoc focus group, and structured interviews. Classification and regression tree analysis was used to analyze survey responses from 343 employees (194 nonparticipants, 95 participants, and 54 engaged participants). A thematic analysis of focus group ( = 7) and interview ( = 5) narratives was also undertaken.

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The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction ($1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm.

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Objective: To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals; (b) potential benefits of using such technologies in the workplace; and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry.

Background: Wearable sensors are increasingly being promoted as a means to improve employee health and well-being, and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies.

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Advanced manufacturing has resulted in significant changes on the shop-floor, influencing work demands and the working environment. The corresponding safety-related effects, including fatigue, have not been captured on an industry-wide scale. This paper presents results of a survey of U.

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Background: Fatigue increases the likelihood of developing work-related musculoskeletal disorders and injury. Due to the physiological and neuromuscular changes that accompany obesity, it may alter the fatigue development mechanism and exacerbate injury risk. The upper extremities have the highest incidence rates for work-related musculoskeletal disorders.

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Objective: The objective of the current study was to determine the effect of body mass index (BMI) on fatigability of three different muscle groups at four different work intensities.

Methods: Forty-nine normal-weight, 50 overweight, and 43 obese adults (32.1 ± 9.

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This study evaluates whether the existing force-endurance relationship models are predictive of endurance time for overweight and obese individuals, and if not, provide revised models that can be applied for ergonomics practice. Data was collected from 141 participants (49 normal weight, 50 overweight, 42 obese) who each performed isometric endurance tasks of hand grip, shoulder flexion, and trunk extension at four levels of relative workload. Subject-specific fatigue rates and a general model of the force-endurance relationship were determined and compared to two fatigue models from the literature.

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Objective: To investigate the cognitive and physical workload experienced by each operating room team member for different types of urologic procedures.

Methods: Surgeons, anesthesiologists, surgical fellows, bedside assistants, circulating nurses, and scrub nurses completed the National Aeronautics and Space Administration Task Load Index questionnaire for various urologic robot-assisted surgery procedures. A total of 338 questionnaires from 55 unique individuals were collected.

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