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

Abstract Background It 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. Methods We used data from several studies that assessed load distribution with maximal and submaximal effort. They consisted…


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