A Multi Criteria Decision Modelling Approach for Gait Analysis of Parkinson’s Disease Using Wearable Sensors to Compare the Classification Performance Based on the Different Feature Selection Methods

2019 ◽  
pp. 528-534
Author(s):  
Satyabrata Aich ◽  
Kamalakanta Muduli ◽  
Hee-Cheol Kim
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Gait Analysis ◽  
Wearable Sensors ◽  
Classification Performance ◽  
Selection Methods ◽  
Decision Modelling ◽  
Modelling Approach

scholarly journals The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2821
Author(s):  
Chariklia Chatzaki ◽  
Vasileios Skaramagkas ◽  
Nikolaos Tachos ◽  
Georgios Christodoulakis ◽  
Evangelia Maniadi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Rating Scale ◽  
Wearable Sensors ◽  
The Elderly ◽  
Detection Algorithm ◽  
Timed Up And Go ◽  
Computer Scientists ◽  
Smart Insole

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease.

scholarly journals Validation of quantitative gait analysis systems for Parkinson’s disease for use in supervised and unsupervised environments

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sara Alberto ◽  
Sílvia Cabral ◽  
João Proença ◽  
Filipa Pona-Ferreira ◽  
Mariana Leitão ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Inertial Sensor ◽  
Low Cost ◽  
Wearable Sensors ◽  
Smartphone Application ◽  
Control Group ◽  
Wearable Systems ◽  
Full Body

Abstract Background Gait impairments are among the most common and impactful symptoms of Parkinson’s disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. Objective We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. Methods Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. Results Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. Conclusions We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

scholarly journals Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7680
Author(s):  
Verena Jakob ◽  
Arne Küderle ◽  
Felix Kluge ◽  
Jochen Klucken ◽  
Bjoern M. Eskofier ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Motion Capture ◽  
Gold Standard ◽  
Wearable Sensors ◽  
Heel Strike ◽  
Motion Capture System ◽  
Gait Parameters ◽  
Analysis System

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

scholarly journals Assessing Gait in Parkinson’s Disease Using Wearable Motion Sensors: A Systematic Review

Diseases ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 18 ◽  
Author(s):  
Lorenzo Brognara ◽  
Pierpaolo Palumbo ◽  
Bernd Grimm ◽  
Luca Palmerini
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Inertial Sensors ◽  
Neurodegenerative Disorder ◽  
Wearable Sensors ◽  
Motion Sensors ◽  
Temporal Parameters ◽  
Spatio Temporal ◽  
Wearable Motion Sensors

: Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Gait impairments are common among people with PD. Wearable sensor systems can be used for gait analysis by providing spatio-temporal parameters useful to investigate the progression of gait problems in Parkinson disease. However, various methods and tools with very high variability have been developed. The aim of this study is to review published articles of the last 10 years (from 2008 to 2018) concerning the application of wearable sensors to assess spatio-temporal parameters of gait in patients with PD. We focus on inertial sensors used for gait analysis in the clinical environment (i.e., we do not cover the use of inertial sensors to monitor walking or general activities at home, in unsupervised environments). Materials and Methods: Relevant articles were searched in the Medline database using Pubmed. Results and Discussion: Two hundred ninety-four articles were initially identified while searching the scientific literature regarding this topic. Thirty-six articles were selected and included in this review. Conclusion: Wearable motion sensors are useful, non-invasive, low-cost, and objective tools that are being extensively used to perform gait analysis on PD patients. Being able to diagnose and monitor the progression of PD patients makes wearable sensors very useful to evaluate clinical efficacy before and after therapeutic interventions. However, there is no uniformity in the use of wearable sensors in terms of: number of sensors, positioning, chosen parameters, and other characteristics. Future research should focus on standardizing the measurement setup and selecting which spatio-temporal parameters are the most informative to analyze gait in PD. These parameters should be provided as standard assessments in all studies to increase replicability and comparability of results.

An intelligent system for Parkinson’s diagnosis using hybrid feature selection approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Intelligent System ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Feature Reduction ◽  
Selection Methods ◽  
Selection Approach ◽  
Feature Selection Approach

Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

scholarly journals Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

2022 ◽  
Author(s):  
Aishwarya Balakrishnan ◽  
◽  
Jeevan Medikonda ◽  
Pramod Kesavan Namboothiri ◽  
Manikandan Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Wearable Sensors ◽  
Disease Assessment ◽  
Learning Approaches

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

Effects of Onabotulinum Toxin A on Gait in Parkinson’s Disease Patients with Foot Dystonia

2021 ◽  
pp. 1-6
Author(s):  
Pei Huang ◽  
Yuan-Yuan Li ◽  
Jung E. Park ◽  
Ping Huang ◽  
Qin Xiao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Botulinum Toxin ◽  
Gait Analysis ◽  
Rating Scale ◽  
Analog Scale ◽  
Toxin A ◽  
Timed Up And Go ◽  
Balance Test ◽  
Onabotulinum Toxin A

ABSTRACT: We investigated the effects of botulinum toxin on gait in Parkinson’s disease (PD) patients with foot dystonia. Six patients underwent onabotulinum toxin A injection and were assessed by Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), visual analog scale (VAS) of pain, Timed Up and Go (TUG), Berg Balance Test (BBT), and 3D gait analysis at baseline, 1 month, and 3 months. BFMDRS (p = 0.002), VAS (p = 0.024), TUG (p = 0.028), and BBT (p = 0.034) were improved. Foot pressures at Toe 1 (p = 0.028) and Midfoot (p = 0.018) were reduced, indicating botulinum toxin’s effects in alleviating the dystonia severity and pain and improving foot pressures during walking in PD.

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