Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

2020 ◽  
Vol 30 (3) ◽  
pp. 362-370 ◽  
Author(s):  
Jason Fong ◽  
Renz Ocampo ◽  
Douglas P. Gross ◽  
Mahdi Tavakoli
1998 ◽  
Vol 3 (2) ◽  
pp. 4-5
Author(s):  
Glenn Pransky

Abstract According to the AMA Guides to the Evaluation of Permanent Impairment, a functional capacity evaluation (FCE) measures an individual's physical abilities via a set of activities in a structured setting and provides objective data about the relationship between an impairment and maximal ability to perform work activities. A key distinction between FCEs and self-reported activities of daily living is that the former involve direct observation by professional evaluators. Numerous devices can quantify the physical function of a specific part of the musculoskeletal system but do not address the performance of whole body tasks in the workplace, and these devices have not been shown to predict accurately the ability to perform all but the simplest job tasks. Information about reliability has been proposed as a way to identify magnification and malingering, but variability due to pain and poor comprehension of instructions may cause variations in assessments. Structured work capacity evaluations involve a set of activities but likely underestimate the individual's ability to do jobs that involve complex or varying activities. Job simulations involve direct observation of an individual performing actual job tasks, require a skilled and experienced evaluator, and raise questions about expense, time, objectivity and validity of results, and interpretation of results in terms of the ability to perform specific jobs. To understand the barriers to return to work, examiners must supplement FCEs with information regarding workplace environment, accommodations, and demotivators.


2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.


2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.


2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  

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