scholarly journals Influence of Maximal or Submaximal Effort on the Load Distribution of the Hand Analyzed by Manugraphy

2018 ◽  
Vol 43 (10) ◽  
pp. 948.e1-948.e9 ◽  
Author(s):  
Marion Mühldorfer-Fodor ◽  
Eren Cenik ◽  
Peter Hahn ◽  
Thomas Mittlmeier ◽  
Jörg van Schoonhoven ◽  
...  
Keyword(s):  
Load Distribution ◽  
Submaximal Effort

scholarly journals Machine learning as a tool for predicting insincere effort in power grips

2016 ◽  
Author(s):  
Peter Hahn ◽  
Eren Cenik ◽  
Karl-Josef Prommersberger ◽  
Marion Mühldorfer-Fodor
Keyword(s):  
Machine Learning ◽  
Grip Strength ◽  
Load Distribution ◽  
Point Of View ◽  
Distribution Analysis ◽  
Additional Information ◽  
Submaximal Effort ◽  
Low Sensitivity ◽  
Grip Effort ◽  
The Given

AbstractBackgroundIt was not possible to detect the common problem of insincere grip effort in grip strength evaluation until now. The usually used JAMAR dynamometer has low sensitivity and specificity in distinguishing between maximal and submaximal effort. The manugraphy system may give additional information to the dynamometer measurements used to assess grip force, as it also measures the load distribution of the hand while it grips a cylinder. Until now, the data of load distribution evaluation were analyzed by comparing discrete variables (e.g., load values of a defined area). From another point of view, the results of manugraphy measurements form a pattern. Analyzing patterns is a typical domain of machine learning.MethodsWe used data from several studies that assessed load distribution with maximal and submaximal effort. They consisted of 2016 total observations, including 324 patterns of submaximal effort. The rest were from grips with maximal effort. After preparation and feature selection, XGBoost machine learning was used for classification of the patterns.FindingsAfter applying machine learning to the given data, we were able to predict submaximal grip effort based on the inherent pattern with a sensitivity of 94% and a specificity of 100%.InterpretationUsing techniques from applied predictive modeling, submaximal effort in grip strength testing could be detected with high accuracy through load distribution analysis. Machine learning is a suitable method for recognizing altered grip patterns.

Clinical Update: Functional Capacity Evaluations—An Update

1999 ◽  
Vol 4 (5) ◽  
pp. 4-7 ◽  
Author(s):  
Laura Welch
Keyword(s):  
Job Performance ◽  
Functional Capacity ◽  
Injury Risk ◽  
Disability Evaluation ◽  
Time Period ◽  
Submaximal Effort ◽  
Lack Of Sleep ◽  
Overall Performance ◽  
Time Required ◽  
Specific Limitation

Abstract Functional capacity evaluations (FCEs) have become an important component of disability evaluation during the past 10 years to assess an individual's ability to perform the essential or specific functions of a job, both preplacement and during rehabilitation. Evaluating both job performance and physical ability is a complex assessment, and some practitioners are not yet certain that an FCE can achieve these goals. An FCE is useful only if it predicts job performance, and factors that should be assessed include overall performance; consistency of performance across similar areas of the FCE; consistency between observed behaviors during the FCE and limitations or abilities reported by the worker; objective changes (eg, blood pressure and pulse) that are appropriate relative to performance; external factors (illness, lack of sleep, or medication); and a coefficient of variation that can be measured and assessed. FCEs can identify specific movement patterns or weaknesses; measure improvement during rehabilitation; identify a specific limitation that is amenable to accommodation; and identify a worker who appears to be providing a submaximal effort. FCEs are less reliable at predicting injury risk; they cannot tell us much about endurance over a time period longer than the time required for the FCE; and the FCE may measure simple muscular functions when the job requires more complex ones.

A Modular Race Tire Model Concerning Thermal and Transient Behavior using a Simple Contact Patch Description

2013 ◽  
Vol 41 (4) ◽  
pp. 232-246
Author(s):  
Timo Völkl ◽  
Robert Lukesch ◽  
Martin Mühlmeier ◽  
Michael Graf ◽  
Hermann Winner
Keyword(s):  
Load Distribution ◽  
Thermal Condition ◽  
Transient Behavior ◽  
Wheel Load ◽  
Contact Patch ◽  
Fundamental Parameters ◽  
Dry Conditions ◽  
Simple Contact ◽  
Tire Forces ◽  
Descriptive Set

ABSTRACT The potential of a race tire strongly depends on its thermal condition, the load distribution in its contact patch, and the variation of wheel load. The approach described in this paper uses a modular structure consisting of elementary blocks for thermodynamics, transient excitation, and load distribution in the contact patch. The model provides conclusive tire characteristics by adopting the fundamental parameters of a simple mathematical force description. This then allows an isolated parameterization and examination of each block in order to subsequently analyze particular influences on the full model. For the characterization of the load distribution in the contact patch depending on inflation pressure, camber, and the present force state, a mathematical description of measured pressure distribution is used. This affects the tire's grip as well as the heat input to its surface and its casing. In order to determine the thermal condition, one-dimensional partial differential equations at discrete rings over the tire width solve the balance of energy. The resulting surface and rubber temperatures are used to determine the friction coefficient and stiffness of the rubber. The tire's transient behavior is modeled by a state selective filtering, which distinguishes between the dynamics of wheel load and slip. Simulation results for the range of occurring states at dry conditions show a sufficient correlation between the tire model's output and measured tire forces while requiring only a simplified and descriptive set of parameters.

Dynamic Service Placement and Load Distribution in Edge Computing

2020 ◽  
Author(s):  
Adyson Magalhaes Maia ◽  
Yacine Ghamri-Doudane ◽  
Dario Vieira ◽  
Miguel Franklin de Castro
Keyword(s):  
Load Distribution ◽  
Edge Computing ◽  
Service Placement

Comparative assessment of rubber steel cables for vertical mine hoists

2020 ◽  
pp. 85-93
Author(s):  
V. E. Perekutnev ◽  
V. V. Zotov
Keyword(s):  
Feasibility Study ◽  
Carrying Capacity ◽  
Load Distribution ◽  
Comparative Assessment ◽  
Life Extension ◽  
Steel Cable ◽  
Motion Stability ◽  
Cable Length ◽  
Steel Cables ◽  
Mine Hoist

Operation of inhaul rubber steel cables in vertical mine hoisting is discussed. The research in the field of mine hoisting is reviewed, and the further R&D directions are identified. Some studies concern life extension of hoisting ropes. One of the promising trends seems to be application of belt pullers as inhaul cables, which can essentially enhance mine hoist efficiency. In the meanwhile, capabilities of rubber steel cables suffer from deficient attention. The performance capabilities of rubber steel cables of top manufacturers (Promkanat and SAG) are compared, and application ranges in vertical mine hoisting are determined for such cables. It is found that the Polish manufacturer’s rubber steel cables offer a wider range of application. The analysis shows that rubber steel cables can be used as inhaul cables of vertical mine hoisters. Rubber steel cables possess suitable characteristics and are capable to elevate considerable loads to various hoisting heights. In particular, the existing rubber steel cables ensure carrying capacity of hoists up to 20-25 t at the hoisting heights to 400-500 m and sometimes can elevate skips with tonnage of 10 t to a height up to 1000 m and more. The further feasibility study of operation of inhaul steel rubber cables in hoisting units should address motion stability of a puller on a driving drum, load distribution in ropes of base of rubber steel cables, validation of hoister design, adjustability of rubber steel cable length during its operation, etc.

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