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 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 Quantitative Gait Analysis in Parkinson’s Disease: Comparison With a Healthy Control Group

2005 ◽  
Vol 86 (5) ◽  
pp. 1007-1013 ◽  
Author(s):  
Olumide Sofuwa ◽  
Alice Nieuwboer ◽  
Kaat Desloovere ◽  
Anne-Marie Willems ◽  
Fabienne Chavret ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Healthy Control Group ◽  
Control Group ◽  
Healthy Control ◽  
Quantitative Gait Analysis

scholarly journals Hidden Markov Model based Stride Segmentation on Unsupervised Free-living Gait Data in Parkinson’s Disease Patients

2021 ◽  
Author(s):  
Nils Roth ◽  
Arne Küderle ◽  
Martin Ullrich ◽  
Till Gladow ◽  
Franz Marxreiter ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gait Analysis ◽  
Large Scale ◽  
Hidden Markov ◽  
Inertial Sensor ◽  
Training Data ◽  
Free Living

Abstract Background: To objectively assess a patient's gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method : To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson's Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results: The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts (<30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts (>50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion: The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications

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.

scholarly journals An Integrated Multi-Sensor Approach for the Remote Monitoring of Parkinson’s Disease

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4764 ◽  
Author(s):  
Giovanni Albani ◽  
Claudia Ferraris ◽  
Roberto Nerino ◽  
Antonio Chimienti ◽  
Giuseppe Pettiti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Remote Monitoring ◽  
Rating Scale ◽  
Population Aging ◽  
Neurological Diseases ◽  
Low Cost ◽  
Wearable Sensors ◽  
Cost Effective ◽  
The Body

The increment of the prevalence of neurological diseases due to the trend in population aging demands for new strategies in disease management. In Parkinson’s disease (PD), these strategies should aim at improving diagnosis accuracy and frequency of the clinical follow-up by means of decentralized cost-effective solutions. In this context, a system suitable for the remote monitoring of PD subjects is presented. It consists of the integration of two approaches investigated in our previous works, each one appropriate for the movement analysis of specific parts of the body: low-cost optical devices for the upper limbs and wearable sensors for the lower ones. The system performs the automated assessments of six motor tasks of the unified Parkinson’s disease rating scale, and it is equipped with a gesture-based human machine interface designed to facilitate the user interaction and the system management. The usability of the system has been evaluated by means of standard questionnaires, and the accuracy of the automated assessment has been verified experimentally. The results demonstrate that the proposed solution represents a substantial improvement in PD assessment respect to the former two approaches treated separately, and a new example of an accurate, feasible and cost-effective mean for the decentralized management of PD.

scholarly journals Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Nils Roth ◽  
Arne Küderle ◽  
Martin Ullrich ◽  
Till Gladow ◽  
Franz Marxreiter ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gait Analysis ◽  
Large Scale ◽  
Hidden Markov ◽  
Inertial Sensor ◽  
Training Data ◽  
Free Living

Abstract Background To objectively assess a patient’s gait, a robust identification of stride borders is one of the first steps in inertial sensor-based mobile gait analysis pipelines. While many different methods for stride segmentation have been presented in the literature, an out-of-lab evaluation of respective algorithms on free-living gait is still missing. Method To address this issue, we present a comprehensive free-living evaluation dataset, including 146.574 semi-automatic labeled strides of 28 Parkinson’s Disease patients. This dataset was used to evaluate the segmentation performance of a new Hidden Markov Model (HMM) based stride segmentation approach compared to an available dynamic time warping (DTW) based method. Results The proposed HMM achieved a mean F1-score of 92.1% and outperformed the DTW approach significantly. Further analysis revealed a dependency of segmentation performance to the number of strides within respective walking bouts. Shorter bouts ($$< 30$$ < 30 strides) resulted in worse performance, which could be related to more heterogeneous gait and an increased diversity of different stride types in short free-living walking bouts. In contrast, the HMM reached F1-scores of more than 96.2% for longer bouts ($$> 50$$ > 50 strides). Furthermore, we showed that an HMM, which was trained on at-lab data only, could be transferred to a free-living context with a negligible decrease in performance. Conclusion The generalizability of the proposed HMM is a promising feature, as fully labeled free-living training data might not be available for many applications. To the best of our knowledge, this is the first evaluation of stride segmentation performance on a large scale free-living dataset. Our proposed HMM-based approach was able to address the increased complexity of free-living gait data, and thus will help to enable a robust assessment of stride parameters in future free-living gait analysis applications.

scholarly journals Effects of motor imagery training of Parkinson's disease: a protocol for a randomized clinical trial

Trials ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Isaíra Almeida Pereira da Silva Nascimento ◽  
Lorenna Marques de Melo Santiago ◽  
Aline Alves de Souza ◽  
Camila de Lima Pegado ◽  
Tatiana Souza Ribeiro ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Imagery ◽  
Low Cost ◽  
Mental Image ◽  
Training Session ◽  
Easy Access ◽  
Control Group ◽  
Imagery Training ◽  
Electroencephalographic Activity

Abstract Background Gait disorders in individuals with Parkinson’s disease (PD) may be associated with alterations in the motor control system and aggravated by psychoemotional and cognitive issues. Therapeutic strategies aimed at self-perception and motor regulation seem to be promising. Motor imagery (MI) has been shown to be one of these strategies, but there is still no clear evidence of its applicability in this population. The aim of this trial is to determine the effects of motor-imagery training on the gait and electroencephalographic activity of individuals with PD. Methods/design The sample will consist of 40 individuals, aged between 45 and 75 years, in the mild and moderate phase of the disease, with the ability to generate voluntary mental images. They will be assessed for cognitive level, degree of physical disability, mental-image clarity, kinematic gait variables, electroencephalographic activity and mobility. Next, subjects will be randomly assigned to an experimental group (EG) and a control group (CG). The EG will perform motor imagery and gait, while the CG will only engage in gait exercises. Twelve training sessions will be conducted lasting up to 90 min each, three times a week, for 4 weeks. The subjects will be reassessed on the kinematic variables of gait, electroencephalographic activity and mobility at 1, 7 and 30 days after the final training session. Discussion The results may provide an important advance in neurological rehabilitation where an easy-access and low-cost intervention may help to improve gait, electroencephalographic activity and mobility in individuals with PD. Trial registration Clinicaltrials.gov, ID: NCT03439800. Registered on 15 November 2017.

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