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 Mild Gait Impairment and Its Potential Diagnostic Value in Patients with Early-Stage Parkinson’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuang Wu ◽  
Xu Jiang ◽  
Min Zhong ◽  
Bo Shen ◽  
Jun Zhu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Wearable Sensors ◽  
Diagnostic Value ◽  
Gait Pattern ◽  
Heel Strike ◽  
Phase Time ◽  
Gait Parameters ◽  
Gait Impairments

Background and Purpose. Patients with early-stage Parkinson’s disease (PD) have gait impairments, and gait parameters may act as diagnostic biomarkers. We aimed to (1) comprehensively quantify gait impairments in early-stage PD and (2) evaluate the diagnostic value of gait parameters for early-stage PD. Methods. 32 patients with early-stage PD and 30 healthy control subjects (HC) were enrolled. All participants completed the instrumented stand and walk test, and gait data was collected using wearable sensors. Results. We observed increased variability of stride length (SL) ( P < 0.001 ), stance phase time (StPT) ( P = 0.004 ), and swing phase time (SwPT) ( P = 0.011 ) in PD. There were decreased heel strike (HS) ( P = 0.001 ), range of motion of knee ( P = 0.036 ), and hip joints ( P < 0.001 ) in PD. In symmetry analysis, no difference was found in any of the assessed gait parameters between HC and PD. Only total steps ( AUC = 0.763 , P < 0.001 ), SL ( AUC = 0.701 , P = 0.007 ), SL variability ( AUC = 0.769 , P < 0.001 ), StPT variability ( AUC = 0.712 , P = 0.004 ), and SwPT variability ( AUC = 0.688 , P = 0.011 ) had potential diagnostic value. When these five gait parameters were combined, the predictive power was found to increase, with the highest AUC of 0.802 ( P < 0.001 ). Conclusions. Patients with early-stage PD presented increased variability but still symmetrical gait pattern. Some specific gait parameters can be applied to diagnose early-stage PD which may increase diagnosis accuracy. Our findings are helpful to improve patient’s quality of life.

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

scholarly journals Exploring the effects of deep brain stimulation and vision on tremor in Parkinson’s disease - benefits from objective methods

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Per-Anders Fransson ◽  
Maria H. Nilsson ◽  
Diederick C. Niehorster ◽  
Marcus Nyström ◽  
Stig Rehncrona ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Motion Capture ◽  
Brain Stimulation ◽  
The Body ◽  
Motion Capture System ◽  
3D Motion ◽  
3D Motion Capture ◽  
Deep Brain

Abstract Background Tremor is a cardinal symptom of Parkinson’s disease (PD) that may cause severe disability. As such, objective methods to determine the exact characteristics of the tremor may improve the evaluation of therapy. This methodology study aims to validate the utility of two objective technical methods of recording Parkinsonian tremor and evaluate their ability to determine the effects of Deep Brain Stimulation (DBS) of the subthalamic nucleus and of vision. Methods We studied 10 patients with idiopathic PD, who were responsive to L-Dopa and had more than 1 year use of bilateral subthalamic nucleus stimulation. The patients did not have to display visible tremor to be included in the study. Tremor was recorded with two objective methods, a force platform and a 3 dimensional (3D) motion capture system that tracked movements in four key proximal sections of the body (knee, hip, shoulder and head). They were assessed after an overnight withdrawal of anti-PD medications with DBS ON and OFF and with eyes open and closed during unperturbed and perturbed stance with randomized calf vibration, using a randomized test order design. Results Tremor was detected with the Unified Parkinson’s Disease Rating Scale (UPDRS) in 6 of 10 patients but only distally (hands and feet) with DBS OFF. With the force platform and the 3D motion capture system, tremor was detected in 6 of 10 and 7 of 10 patients respectively, mostly in DBS OFF but also with DBS ON in some patients. The 3D motion capture system revealed that more than one body section was usually affected by tremor and that the tremor amplitude was non-uniform, but the frequency almost identical, across sites. DBS reduced tremor amplitude non-uniformly across the body. Visual input mostly reduced tremor amplitude with DBS ON. Conclusions Technical recording methods offer objective and sensitive detection of tremor that provide detailed characteristics such as peak amplitude, frequency and distribution pattern, and thus, provide information that can guide the optimization of treatments. Both methods detected the effects of DBS and visual input but the 3D motion system was more versatile in that it could detail the presence and properties of tremor at individual body sections.

scholarly journals Wearable sensors objectively measure gait parameters in Parkinson’s disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0183989 ◽  
Author(s):  
Johannes C. M. Schlachetzki ◽  
Jens Barth ◽  
Franz Marxreiter ◽  
Julia Gossler ◽  
Zacharias Kohl ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Gait Parameters

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2020 ◽  
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Gold Standard ◽  
Three Dimensional ◽  
Human Gait ◽  
Large Set ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

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.

Reliability of using foot-worn devices to measure gait parameters in people with Parkinson’s disease

2021 ◽  
pp. 1-8
Author(s):  
Ya-Yun Lee ◽  
Ming-Hao Li ◽  
Jer-Junn Luh ◽  
Chun-Hwei Tai
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Severity ◽  
Wearable Sensors ◽  
Good Reliability ◽  
Retest Reliability ◽  
Early Stages ◽  
Gait Parameters ◽  
Advanced Stages ◽  
Test Retest Reliability

BACKGROUND: Recent advances in technology have warranted the use of wearable sensors to monitor gait and posture. However, the psychometric properties of using wearable devices to measure gait-related outcomes have not been fully established in patients with Parkinson’s disease (PD). OBJECTIVE: This study aimed to investigate the test-retest reliability of body-worn sensors for gait evaluation in people with PD. Additionally, the influence of disease severity on the reliability was determined. METHODS: Twenty individuals with PD were recruited. During the first evaluation, the participants wore inertial sensors on their shoes and walked along a walkway thrice at their comfortable walking speed. The participants were then required to return to the lab after 3–5 days to complete the second evaluation with the same study procedure. Test-retest reliability of gait-related outcomes were calculated. To determine whether the results would be affected by disease severity, reliability was re-calculated by subdividing the participants into early and mid-advanced stages of the disease. RESULTS: The results showed moderate to good reliability (ICC = 0.64–0.87) of the wearable sensors for gait assessment in the general population with PD. Subgroup analysis showed that the reliability was higher among patients at early stages (ICC = 0.71–0.97) compared to those at mid-advanced stages (ICC = 0.65–0.81) of PD. CONCLUSIONS: Wearable sensors could reliably measure gait parameters in people with PD, and the reliability was higher among individuals at early stages of the disease compared to those at mid-advanced stages. Absolute reliability values were calculated to act as references for future studies.

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.

Sign in / Sign up

Export Citation Format

Share Document