Huntington's Disease Assessment Using Tri Axis Accelerometers

Abstract

Huntington’s disease (HD) is a progressive inherited neurodegenerative disorder, causing involuntary movement and
\ncognitive problems, severely affecting the quality of life. Controlling upper limb function is a core feature of daily activity
\nand can prove problematic for people with HD. The Money Box Test (MBT) has been developed with a purpose of
\nquantifying the involuntary movement frequently seen in people with HD. In this research, wearable and highly sensitive
\naccelerometers are used to collect the acceleration of the hands and chest during the performance of the MBT. Using this
\ndata, a new approach is proposed to automatically classify the participants into two classes, healthy and HD, on the basis of
\nthe time series accelerometer data. A set of 90 time domain features is extracted from the accelerometer data, a feature
\nselection technique is used to analyse the feature significance and to reduce the dimensionality of the dataset, and finally an
\nSVM classifier is used to classify subjects into healthy and HD classes. The data of seven healthy controls and 15 HD
\npatients are used in this study. The highest accuracy with the most significant eight features is 86.36% with the sensitivity
\nand the specificity values being 87.50%, and 83.33% respectively.