Early detection of Parkinson’s diseases by using the relation between time response and movement characteristics of human’s arms

Abstract

Parkinson’s and stroke diseases are closely linked to the brain of the elderly. This study was to investigate the early detecting method of Parkinson’s disease by using the relation between the brain time response and the arm movement characteristics. 120 Healthy people were examined and classified into 4 groups of ages (60 years old).The relationship between the two parameters were conducted by using the self-made electronics set which had an accelerometer attached on the hammer; and pattern generator using star-pattern with 9-position lighted keypad. Several simple and complex light patterns were designed to test the brain function of the elderly. The experimental treatments were subjected to 4×2 Factorial Experiment in Completely Randomized Design (CRD). The results showed that the time response of the group of+60’s years old was the longest compared with other group with P<0.01. Based on the experiments on pattern-position approach, those selected samples with 4 groups of age completed the experiment with a sample pattern faster than the complex pattern in all 4 groups of age with P<0.01. The acceleration signal’s patterns in 20-40 years old and +60 years old were found polynomial and linear signal patterns, respectively. The relationship between the time response and acceleration signal were found negative monotonic correlated (𝑟𝑠𝑟 = 0.835, P < 0.01). Therefore, this finding could identify the healthy people without Parkinson’s disease with accuracy of 99.58 %. The results could be concluded that relationship between the time response and the acceleration signal could predict Parkinson’s disease and related diseases in the future.

Keywords

• Brain defect
• Accelerometer
• Keypad light pattern
• Detection
• Relationship
• Polynomial
• Linear regression