Ergonomics

A marker-less 2D video algorithm measured hand kinematics (location, velocity and acceleration) in a paced repetitive laboratory task for varying hand activity levels (HAL). The decision tree (DT) algorithm identified the trajectory of the hand using spatiotemporal relationships during the exertion and rest states. The feature vector training (FVT) method utilised the k-nearest neighbourhood classifier, trained using a set of samples or the first cycle. The average duty cycle (DC) error using the DT algorithm was 2.7%. The FVT algorithm had an average 3.3% error when trained using the first cycle sample of each repetitive task, and had a 2.8% average error when trained using several representative repetitive cycles. Error for HAL was 0.1 for both algorithms, which was considered negligible. Elemental time, stratified by task and subject, were not statistically different from ground truth (p\ua0<\ua00.05). Both algorithms performed well for automatically measuring elapsed time, DC and HAL. Practitioner Summary: A completely automated approach for measuring elapsed time and DC was developed using marker-less video tracking and the tracked kinematic record. Such an approach is automatic, repeatable, objective and unobtrusive, and is suitable for evaluating repetitive exertions, muscle fatigue and manual tasks.R21 EB014583/EB/NIBIB NIH HHS/United StatesR21 OH010221/OH/NIOSH CDC HHS/United StatesT42 OH008455/OH/NIOSH CDC HHS/United States2017-11-01T00:00:00Z26848051PMC522607

Ergonomics

ObjectiveAn equation was developed for estimating hand activity level (HAL) directly from tracked RMS hand speed (S) and duty cycle (D).BackgroundTable lookup, equation, or marker-less video tracking can estimate HAL from motion/exertion frequency (F) and D. Since automatically estimating F is sometimes complex, HAL may be more readily assessed using S.MethodsHands from 33 videos originally used for the HAL rating were tracked to estimate S, scaled relative to hand breadth (HB), and single-frame analysis was used to measure D. Since HBs were unknown, a Monte Carlo method was employed for iteratively estimating the regression coefficients from US Army anthropometry survey data.ResultsThe equation: =10[e\ue2\u2c6\u201915.87+0.02D+2.25lnS1+e\ue2\u2c6\u201915.87+0.02D+2.25lnS], R2 = 0.97, had a residual range \uc2\ub10.5 HAL.ConclusionsThe S equation superiorly fit the Latko (1997) data and predicted independently observed HAL values (Harris, 2011) better (MSE=0.16) than the F equation (MSE=1.28).Practitioner SummaryAn equation was developed for estimating the HAL rating for the ACGIH Threshold Limit Value\uc2\uae based on hand RMS speed and duty cycle. Speed is more readily evaluated from videos using semi-automatic markerless tracking, than frequency. The speed equation predicted observed HAL values much better than the F equation.R01 OH007914/OH/NIOSH CDC HHS/United StatesR01OH007914/OH/NIOSH CDC HHS/United StatesR21 EB014583/EB/NIBIB NIH HHS/United StatesR21 OH010221/OH/NIOSH CDC HHS/United StatesR21OH010221/OH/NIOSH CDC HHS/United States2015-12-01T00:00:00Z25343278PMC466488

Ergonomics

ObjectiveA new equation for predicting the hand activity level (HAL) used in the ACGIH threshold limit value\uc2\uae (TLV\uc2\uae), was based on exertion frequency (F) and percentage duty cycle (D).BackgroundThe TLV\uc2\uae includes a table for estimating HAL from F and D originating from data in Latko et al. (1997) and post-hoc adjustments that includes extrapolations outside of the data range.MethodsMultimedia video task analysis determined D for two additional jobs from Latko\ue2\u20ac\u2122s study not in the original data set, and a new non-linear regression equation was developed to better fit the data and create a more accurate table.ResultsThe equation, HAL=6.56lnD[F1.311+3.18F1.31], generally matches the TLV\uc2\uae HAL lookup table, and is a substantial improvement over the linear model, particularly for F > 1.25 Hz and D > 60% jobs.ConclusionThe equation more closely fits the data and applies the TLV\uc2\uae using a continuous function.Practitioner SummaryThe original HAL lookup table is limited in resolution, omits values, and extrapolates values outside of the range of data. A new equation and table was developed to address these issues.R01OH007914/OH/NIOSH CDC HHS/United StatesR21OH010221/OH/NIOSH CDC HHS/United StatesUL1 TR000427/TR/NCATS NIH HHS/United States2015-02-01T00:00:00Z25343340PMC430273

Advanced extravehicular activity systems requirements definition study. Phase 2: Extravehicular activity at a lunar base

The focus is on Extravehicular Activity (EVA) systems requirements definition for an advanced space mission: remote-from-main base EVA on the Moon. The lunar environment, biomedical considerations, appropriate hardware design criteria, hardware and interface requirements, and key technical issues for advanced lunar EVA were examined. Six remote EVA scenarios (three nominal operations and three contingency situations) were developed in considerable detail

Ergonomics

Observer, manual single-frame video, and automated computer vision measures of the Hand Activity Level (HAL) were compared. HAL can be measured three ways: (1) observer rating (HAL|), (2) calculated from single-frame multimedia video task analysis for measuring frequency (F) and duty cycle (D) (HAL|), or (3) from automated computer vision (HAL|). This study analysed videos collected from three prospective cohort studies to ascertain HAL|, HAL|, and HAL| for 419 industrial videos. Although the differences for the three methods were relatively small on average (<1), they were statistically significant (| < .001). A difference between the HAL| and HAL| ratings within \ub11 point on the HAL scale was the most consistent, where more than two thirds (68%) of all the cases were within that range and had a linear regression through the mean coefficient of 1.03 (| = 0.89). The results suggest that the computer vision methodology yields comparable results as single-frame video analysis.| The ACGIH Hand Activity Level (HAL) was obtained for 419 industrial tasks using three methods: observation, calculated using single-frame video analysis and computer vision. The computer vision methodology produced results that were comparable to single-frame video analysis.CC999999/ImCDC/Intramural CDC HHSUnited States/T42 OH008429/OH/NIOSH CDC HHSUnited States

A case study evaluating the ergonomic and productivity impacts of partial automation strategies in the electronics industry

A case study is presented that evaluates the impact of partial automation strategies on productivity and ergonomics. A company partly automated its assembly and transportation functions while moving from a parallel-batch to a serial line-based production system. Data obtained from company records and key informants were combined with detailed video analysis, biomechanical modelling data and field observations of the system. The new line system was observed to have 51% higher production volumes with 21% less per product labour input and lower work-in-process levels than the old batch-cart system. Partial automation of assembly operations was seen to reduce the total repetitive assembly work at the system level by 34%. Automation of transportation reduced transport labour by 63%. The strategic decision to implement line-transportation was found to increase movement repetitiveness for operators at manual assembly stations, even though workstations were constructed with consideration to ergonomics. Average shoulder elevation at these stations increased 30% and average shoulder moment increased 14%. It is concluded that strategic decisions made by designers and managers early in the production system design phase have considerable impact on ergonomic conditions in the resulting system. Automation of transport and assembly both lead to increased productivity, but only elements related to the automatic line system also increased mechanical loads on operators and hence increased the risk for work-related disorders. Suggestions for integrating the consideration of ergonomics into production system design are made

An ergonomic assessment of the airline baggage handler

A material or baggage handler is responsible for loading and unloading baggage and materials from inbound/outbound aircraft flights and transferring the materials to and from the baggage holding and sorting areas and back to the passengers or output source. Baggage handlers work in all types of inclement weather, all over the airport, and in-and-around the aircraft. The baggage handler\u27s job entails repeated lifting pulling, pushing, squatting, twisting, kneeling, and stretching of the arms and back, which makes the baggage handler\u27s job one of the more challenging material handling jobs to ergonomically assess and make corrections for. The aim of the present study is to evaluate the current literature available pertaining to baggage handlers and ergonomics, as well as examine all aspects of the baggage handlers\u27 job in an effort to develop ergonomic solutions. This thesis is based on the literature review of a core set of articles that thoroughly cover the major aspects of the baggage handlers\u27 job, work environment, and ergonomic afflictions pertinent to the baggage handlers using ergonomic evaluation techniques. It was shown that typical solutions to ergonomic problems of baggage handlers, such as wearing back support belts, are not conclusively effective in reducing the back injury rate amongst airline baggage handlers. The redesign of workstations and aircraft holds, although thought to be the most effective idea due to success where already applied, was not the most practical or readily available solution financially. The future of ergonomic advancements in the field of airline material handling will rely on future research. Such a research will need to develop a benefit analysis to quantify the dollars spent on back-related injuries against the cost of remodeling aircrafts and workstations

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS), phase 2. Volume 1: Telepresence technology base development

The field of telepresence is defined, and overviews of those capabilities that are now available, and those that will be required to support a NASA telepresence effort are provided. Investigation of NASA's plans and goals with regard to telepresence, extensive literature search for materials relating to relevant technologies, a description of these technologies and their state of the art, and projections for advances in these technologies over the next decade are included. Several space projects are examined in detail to determine what capabilities are required of a telepresence system in order to accomplish various tasks, such as servicing and assembly. The key operational and technological areas are identified, conclusions and recommendations are made for further research, and an example developmental program is presented, leading to an operational telepresence servicer

Ames life science telescience testbed evaluation

Eight surrogate spaceflight mission specialists participated in a real-time evaluation of remote coaching using the Ames Life Science Telescience Testbed facility. This facility consisted of three remotely located nodes: (1) a prototype Space Station glovebox; (2) a ground control station; and (3) a principal investigator's (PI) work area. The major objective of this project was to evaluate the effectiveness of telescience techniques and hardware to support three realistic remote coaching science procedures: plant seed germinator charging, plant sample acquisition and preservation, and remote plant observation with ground coaching. Each scenario was performed by a subject acting as flight mission specialist, interacting with a payload operations manager and a principal investigator expert. All three groups were physically isolated from each other yet linked by duplex audio and color video communication channels and networked computer workstations. Workload ratings were made by the flight and ground crewpersons immediately after completing their assigned tasks. Time to complete each scientific procedural step was recorded automatically. Two expert observers also made performance ratings and various error assessments. The results are presented and discussed