Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Abstract

Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

“…All studies included gait tasks of varying methods: Timed Up and Go (TUG), 29 , 30 , 34 , 35 , 45 , 47 6MWT, 32 instrumented stand and walk task 40 and many more. The discriminating factor for TUG is that it includes the ‘turning phase’.…”

“…Multiple layers can be conducted in which new filters are used each time and then feedforwarded into ANN. Steinmetzer et al 45 adopted single-layer and multi-layer CNN for gait analysis. The final results show a promising result of 93.4% accuracy using wavelet transformation and a three-layer CNN algorithm.…”

“…The final results show a promising result of 93.4% accuracy using wavelet transformation and a three-layer CNN algorithm. 45 Despite the complexity of NN mapping multi-dimensional images, the computational requirements of CNN are attainable from consumer-based products. For example, Steinmetzer et al 45 applied a three-layer model CNN with consumer-grade Intel Core i7-6700HQ 2.6 GHz four cores and 16 GB RAM.…”

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

“…This device was proposed as a rapid response tool to prevent sudden deaths before the wearer could reach the hospital. Arm-worn smart devices can also be used to monitor arm movements that are an important feature of the walking gait of humans [ 33 ]. An analysis of the arm swinging motion can help in the detection of motor dysfunctions such as those found among patients of Parkinson’s disease.…”

An Energy-Efficient, Parallel Neighborhood and Adaptation Functions for Hardware Implemented Self-Organizing Maps Applied in Smart Grid

“…The key to building such systems is a proper development of ANNs, which includes not only the algorithmic part but also efficient ways of the implementation of such networks. ANNs gained a large popularity over the past decades [7][8][9][10]. One of the main branches in development of such systems is the family of neural networks (NNs) trained in an unsupervised way [11,12].…”