Early detection of Parkinson’s diseases by using the relationship between time response and movement characteristics of human arms
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Abstract
Parkinson’s and stroke diseases are brain problems of many elderly people. This study was done to investigate an early detection method for Parkinson’s disease using the relationship between the brain time response and arm movement characteristics. 120 healthy people were examined and classified into four age groups (<20, 20-40, 41-60, >60 years old). The relationship between the two parameters was examined using a fabricated electronic device with an accelerometer on a hammer and a star-pattern generator a with a 9-position lighted keypad. Several simple and complex light patterns were designed to test brain function. The experimental treatments were developed using a 4×2 factorial experiment in a completely randomized design (CRD). The results showed that the time response of the >60 age group was the longest compared to other groups (p<0.01). Based on the experiments using a pattern-position approach, all age groups completed the simple pattern faster than the complex pattern (p<0.01). The accelerometer signal patterns in 20-40 years old and +60 years old were found to have polynomial and linear signal patterns, respectively. The relationship between the time response and the accelerometer signal were found to be negatively monotonic ( 0.835, p < 0.01). Therefore, this finding could identify healthy people without Parkinson’s disease with an accuracy of 99.58 %. The results suggest that tests such as these can be developed for early detection of Parkinson’s and related diseases.
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