Accuracy of Markerless Motion Capture Evaluation in Parkinson's Disease After DBS
NCT ID: NCT03607721
Last Updated: 2018-07-31
Study Results
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Basic Information
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COMPLETED
NA
12 participants
INTERVENTIONAL
2017-03-01
2017-07-01
Brief Summary
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The objective of this study was to determine if a markerless 3D motion capture system is a useful instrument to objectively differentiate between Parkinons's Disease (PD) patients with Deep Brain Stimulation (DBS) in On and Off state and controls; and its correlation with the evaluation by means of Unified Parkinson's Disease Rating Scale (UPDRS).
Six PD patients who underwent DBS bilaterally in the subthalamic nucleus were evaluated using BME and UPDRS-III with DBS turned On and Off. BME of 16 different movements in six controls paired by age and sex was compared with PD patients with DBS in On and Off states.
Kinematic data obtained with this markerless system could contribute to the discrimination between PD patients and healthy controls. This emerging technology may help to clinically evaluate PD patients more objectively.
Detailed Description
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Instrument. Actually, there are many markerless motion capture systems in the market, with a broad range of prices, as well as a broad range of reliability. However, DARI system has been proven to be one of the best for numerous reasons. This system requires a quick calibration at the beginning of each day that the technician can complete in less than 10 minutes. It does not have to be repeated until the following day, no matter how many patients are evaluated. The system depends on a computer-based software that acquires the patient's skeleton or avatar using eighteen high-speed cameras (120 Hz) placed around the room to collect whole body data and delivers kinematic analysis almost instantly using sophisticated biomechanical algorithms.
Also, traditional motion labs use cumbersome floor-mounted pressure plates to measure the forces generated by the body. These require frequent calibration and restrict the subject's movement to a limited area. DARI's kinetic capture system does not require force plates and can measure joint torques, ground reaction forces, and other measurements without restricting the subject's natural movement.
Markerless 3D motion capture evaluation of kinematics in the PD patients and controls was performed on a rectangle room that measures 6 x 6 meters and 3 meters in height. The room has a green screen on the floor, and eighteen cameras are strategically placed on the walls, twelve are placed 2.6 meters high and 6 are on a lower level at 30 centimeters from the ground. The room has ample space, which allows for broader movements to be analyzed.
Evaluations. PD patients were asked to arrive in the morning wearing dark close-fitting clothing, to skip their last PD medication, and with DBS in Off state for least 180 minutes. On PD patients, UPDRS-III evaluation was done first. Then, to begin the markerless body motion evaluation (BME), patients and controls' weight and height were entered into the system to help establish the locations of joint centers. Once inside the green room, subjects first stood with feet apart and arms outstretched to the side, while the system created a 3D silhouette of each participant's form and a biometric skeleton was acquired; this took no more than three seconds. For the BME, all subjects performed 16 different movements. This set of movements was especially designed to evaluate PD patients and contains items that are related to three major motor symptoms in this disease: rigidity, bradykinesia and postural instability; tremor is not possible to assess. Once the BME was done, PD patients were asked to turn their DBS to On state and wait 30 minutes before repeating both UPDRS-III and BME. Controls only performed the BME, which took no more than 20 minutes. PD patients were evaluated twice (DBS state On and Off) with a 1-hour wait in between; their evaluation altogether took approximately 1.5 hours. The data files were uploaded to DARI Motion Platform where the biomechanical analysis produced full-body kinematic results and, finally, these data were exported to Excel for statistical analysis.
Analysis. A paired t-test was used to compare mean changes in UPDRS-III between the On and Off state. Mean differences between groups were evaluated with ANOVA or Kruskal-Wallis tests depending of the distribution of the data of each independent variable. Post hoc analyses were made for pairwise comparison in statistically significant results. Bivariate correlations among evaluation modalities were examined. These correlations were examined in the On and Off state between UPDRS-III and BME items. To compare them as accurately as possible, the items on UPDRS-III and BME that were similar were correlated (e.g. rigidity in upper limbs from UPDRS-III was correlated with shoulder flexion, extension, rotation from BME). One of the correlations was hip displacement taken from BME, which analyzes balance by measuring the movement of hips when patients stand during 10 seconds with arms outstretched to the sides and eyes closed; this was correlated with posture stability item from UPDRS-III, which is a quick pull, reactionary intervention test where patient's response is measured. Because not all UPDRS-III items were measurable by DARI, seven out of 18 were correlated; however, all BME items were correlated with UPDRS-III global score. IBM SPSS Statistics 21.0 software was used for data analysis. A p-value of ≤0.05 was considered to indicate statistical significance.
Conditions
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Keywords
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Study Design
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NON_RANDOMIZED
PARALLEL
SUPPORTIVE_CARE
NONE
Study Groups
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DBS Patients Group
Body Motion Evaluation DARI
Body Motion Evaluation DARI
Dynamic Athletic Research Institute (DARI) Software to evaluate motion tridimensionally with a camera system and without the use of body sensors.
Control Group
Body Motion Evaluation DARI
Body Motion Evaluation DARI
Dynamic Athletic Research Institute (DARI) Software to evaluate motion tridimensionally with a camera system and without the use of body sensors.
Interventions
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Body Motion Evaluation DARI
Dynamic Athletic Research Institute (DARI) Software to evaluate motion tridimensionally with a camera system and without the use of body sensors.
Eligibility Criteria
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Inclusion Criteria
* Submitted to subthalamic DBS implantation a minimum of 3 months prior to the evaluation.
Exclusion Criteria
* History of stroke and physical disability
* Another neurological disorder other than PD
* Recent head and limb trauma that limits movement
* Treatment with antipsychotics or recent botulinum toxin treatment.
40 Years
80 Years
ALL
No
Sponsors
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Hospital Zambrano Hellion
OTHER
Responsible Party
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Hector Ramon Martinez
Director of Instituto de Neurologia y Neurocirugia Hospital Zambrano Hellion
Principal Investigators
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Hector R Martinez, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Hospital Zambrano Hellion
Locations
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Instituto de Neurologia y Neurocirugia Hospital Zambrano Hellion
San Pedro Garza García, Nuevo León, Mexico
Countries
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References
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Ceseracciu E, Sawacha Z, Cobelli C. Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: proof of concept. PLoS One. 2014 Mar 4;9(3):e87640. doi: 10.1371/journal.pone.0087640. eCollection 2014.
Rocha AP, Choupina H, Fernandes JM, Rosas MJ, Vaz R, Silva Cunha JP. Parkinson's disease assessment based on gait analysis using an innovative RGB-D camera system. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3126-9. doi: 10.1109/EMBC.2014.6944285.
Fry AC, Herda TJ, Sterczala AJ, Cooper MA, Andre MJ. Validation of a motion capture system for deriving accurate ground reaction forces without a force plate. Big Data Anal. 2016;1(1):11. doi:10.1186/s41044-016-0008-y.
Moodie P. Validation : Reviewing 3D Motion Capture Technology Types and What the Gold Standard Should Be for Human Movement . Lenexa, Kansas
Rosengarden S, Docking S, Wassom D, Moodie N. The long term repeatability of a 3D markerless motion capture system and the implications it has on healthcare. J Appl Hum Mov. 2015;1(1):21-25.
Wassom D, Fry A, Moodie N. Repeatability of 3D markerless motion capture and how it could affect between-session variability. J Appl Hum Mov. 2015;1(1):21-25.
Mündermann L, Anguelov D, Corazza S, Chaudhari AM, Andriacchi TP. Validation of a markerless motion capture system for the calculation of lower extremity kinematics.; 2005.
Chen SW, Lin SH, Liao LD, Lai HY, Pei YC, Kuo TS, Lin CT, Chang JY, Chen YY, Lo YC, Chen SY, Wu R, Tsang S. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. Biomed Eng Online. 2011 Nov 10;10:99. doi: 10.1186/1475-925X-10-99.
Perrott MA, Pizzari T, Cook J, McClelland JA. Comparison of lower limb and trunk kinematics between markerless and marker-based motion capture systems. Gait Posture. 2017 Feb;52:57-61. doi: 10.1016/j.gaitpost.2016.10.020. Epub 2016 Oct 31.
Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Gait Posture. 2014 Apr;39(4):1062-8. doi: 10.1016/j.gaitpost.2014.01.008. Epub 2014 Jan 22.
Bovonsunthonchai S, Vachalathiti R, Pisarnpong A, Khobhun F, Hiengkaew V. Spatiotemporal gait parameters for patients with Parkinson's disease compared with normal individuals. Physiother Res Int. 2014 Sep;19(3):158-65. doi: 10.1002/pri.1579. Epub 2013 Dec 23.
Ferrarin M, Rizzone M, Bergamasco B, Lanotte M, Recalcati M, Pedotti A, Lopiano L. Effects of bilateral subthalamic stimulation on gait kinematics and kinetics in Parkinson's disease. Exp Brain Res. 2005 Jan;160(4):517-27. doi: 10.1007/s00221-004-2036-5. Epub 2004 Oct 22.
Dewey DC, Miocinovic S, Bernstein I, Khemani P, Dewey RB 3rd, Querry R, Chitnis S, Dewey RB Jr. Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease. J Neurol Sci. 2014 Oct 15;345(1-2):131-8. doi: 10.1016/j.jns.2014.07.026. Epub 2014 Jul 19.
Espy DD, Yang F, Bhatt T, Pai YC. Independent influence of gait speed and step length on stability and fall risk. Gait Posture. 2010 Jul;32(3):378-82. doi: 10.1016/j.gaitpost.2010.06.013. Epub 2010 Jul 23.
Other Identifiers
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DARI-DBS
Identifier Type: -
Identifier Source: org_study_id