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Ziegelman L, Kosuri T, Hakim H, Zhao L, Elshourbagy A, Mills KA, Harrigan TP, Hernandez ME, Brašić JR. Dataset of quality assurance measurements of rhythmic movements. Data Brief 2023; 50:109556. [PMID: 37753262 PMCID: PMC10518676 DOI: 10.1016/j.dib.2023.109556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 07/30/2023] [Accepted: 09/04/2023] [Indexed: 09/28/2023] Open
Abstract
A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed on participants with Parkinson's disease and multiple system atrophy as well as age- and sex-matched healthy participants with typical development. Participants underwent twelve videotaped procedures rated by a trained examiner while connected to four accelerometers [1,2] generating a trace of the three location dimensions expressed as spreadsheets [3,4]. The signals of the five repetitive motion items (3.4 Finger tapping, 3.5 Hand movements, 3.6 Pronation-supination movements of hands, 3.7 Toe tapping, and 3.8 Leg agility) [1] underwent processing to fast Fourier [5] and amor and bump continuous wavelet transforms [6], [7], [8], [9], [10], [11], [12], [13]. Images of the signals and their transforms [4], [5], [6] of the five repetitive tasks of each participant were randomly expressed as panels on an electronic framework for rating by 35 trained examiners who did not know the source of the original output [14]. The team of international raters completed ratings of the signals and their transforms independently using criteria like the scoring systems for live assessments of movements in human participants [1,2]. The raters scored signals and transforms for deficits in the sustained performance of rhythmic movements (interruptions, slowing, and amplitude decrements) often observed in people with Parkinson's disease [15], [16], [17], [18], [19], [20]. Raters were first presented the images of the signals and transforms of a man with multiple system atrophy as a test and a retest in a different random order. After the raters completed the assessments of the man with multiple system atrophy, they were presented random test and retest panels of the images of signals and transforms of ten participants with Parkinson's disease who completed a single rating session. After the raters completed the assessments of the participants with Parkinson's disease who completed one set of ratings, they were presented random test and retest panels of the images of signals and transforms of (A) ten participants with Parkinson's disease and (B) eight age- and sex-match healthy participants with typical development who completed two rating session separated by a month or more [15], [16], [17], [18], [19], [20]. The data provide a framework for further analysis of the acquired information. Additionally, the data provide a template for the construction of electronic frameworks for the remote analysis by trained raters of signals and transforms of rhythmic processes to verify that the systems are operating smoothly without interruptions or changes in frequency and amplitude. Thus, the data provide the foundations to construct electronic frameworks for the virtual quality assurance of a vast spectrum of rhythmic processes. The dataset is a suitable template for solving unsupervised and supervised machine learning algorithms. Readers may utilize this procedure to assure the quality of rhythmic processes by confirming the absence of deviations in rate and rhythm. Thus, this procedure provides the means to confirm the quality of the vast spectrum of rhythmic processes.
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Affiliation(s)
- Liran Ziegelman
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 60801, United States
| | - Tanvi Kosuri
- Department of Public Health Studies, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States
| | - Husain Hakim
- Section of High-Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, The Johns Hopkins University, Baltimore, MD 21205, United States
| | - Luqi Zhao
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 60801, United States
| | - Abdelwahab Elshourbagy
- Misr University for Science and Technology, Al Motamayez District-6th of October, Giza Governorate 3236101, Egypt
| | - Kelly Alexander Mills
- Department of Neurology, School of Medicine, The Johns Hopkins University, Baltimore, MD 21205, United States
| | - Timothy Patrick Harrigan
- Research and Exploratory Development, Applied Physics Laboratory, The Johns Hopkins University, Laurel, MD 20723, United States
| | - Manuel Enrique Hernandez
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 60801, United States
| | - James Robert Brašić
- Section of High-Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, The Johns Hopkins University, Baltimore, MD 21205, United States
- Department of Behavioral Health, New York City Health + Hospitals/Bellevue, 462 First Avenue, New York, NY 10016, United States
- Department of Psychiatry, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, United States
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Hu Y, He M, Zhao J, Bishnoi A, Ziegelman L, Hsiao-Wecksler E, Hernandez ME. Effect Of Tai Chi On Resting State Alpha Power And Functional Connectivity In Older Women. Med Sci Sports Exerc 2022. [DOI: 10.1249/01.mss.0000882544.58374.a3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Hernandez ME, Ziegelman L, Kosuri T, Hakim H, Zhao L, Mills KA, Brašić JR. Classification of extremity movements by visual observation of signals and their transforms. MethodsX 2022; 9:101739. [PMID: 35677844 PMCID: PMC9168139 DOI: 10.1016/j.mex.2022.101739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/21/2022] [Indexed: 11/19/2022] Open
Abstract
A low-cost quantitative continuous measurement of movements utilizes accelerometers to generate signal outputs to precisely record the positions of extremities during the performance of movements. This procedure can readily be accomplished with inexpensive materials constructed indivisuals throughout the world. The proposed protocol provides the framework for trained raters to assess the signal outputs by visual observation to generate objective measurements like the measurements of the actual movements. Expert raters can then remotely give quantitative suggestions for providers in underserved regions to utilize precision medicine to develop optimal treatment plans tailored to the specific needs of each individual. The proposed protocol lays the foundations for experts located in tertiary centers to provide optimal assessments of signal outputs generated remotely in underserved regions. This protocol provides the means to address gaps in current research including the dearth of objective measurements of movements utilizing automatic intelligence and machine learning to accurately and precisely analyze movement assessments. Future research will include the development of robotic tools to perform assessments and analyses of the movements of human beings to enhance the conduct of movement evaluations of people with Parkinson's disease and related conditions to apply precision medicine for optimal diagnostic and therapeutic interventions.
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Affiliation(s)
- Manuel Enrique Hernandez
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Liran Ziegelman
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tanvi Kosuri
- Department of Public Health Studies, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States
| | - Husain Hakim
- Department of Neuroscience, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States
| | - Luqi Zhao
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Kelly Alexander Mills
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - James Robert Brašić
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Corresponding author.
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Ziegelman L, Alkurdi A, Hu Y, Bishnoi A, Kaur R, Sowers R, Hsiao-Wecksler ET, Hernandez ME. Feasibility of VR Technology in Eliciting State Anxiety Changes While Walking in Older Women. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:583-586. [PMID: 34891361 DOI: 10.1109/embc46164.2021.9630542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Virtual reality (VR) technology offers an exciting way to emulate real-life walking conditions that may better elicit changes in emotional state. We aimed to determine whether VR technology is a feasible way to elicit changes in state anxiety during walking. Electrocardiogram data were collected for 18 older adult women while they navigated a baseline walking task, a dual walking task, and four walking VR environments. Using heart rate variability (HRV) analysis, we found that all four of the VR environments successfully elicited a significantly higher level of state anxiety as compared to the walking baseline, with 84% of participants eliciting a significantly lower HRV in each of the four VR conditions as compared to baseline walking. VR was also found to be a more reliable tool for increasing state anxiety as compared to a dual task, where only 47% of participants demonstrated a significantly lower HRV as compared to baseline walking. VR, therefore, could be promising as a tool to elicit changes in state anxiety and less limited in its ability to elicit changes as compared to a traditional dual task condition.
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Harrigan TP, Hwang BJ, Mathur AK, Mills KA, Pantelyat AY, Bang JA, Syed AB, Vyas P, Martin SD, Jamal A, Ziegelman L, Hernandez ME, Wong DF, Brašić JR. Dataset of quantitative structured office measurements of movements in the extremities. Data Brief 2020; 31:105876. [PMID: 32642510 PMCID: PMC7334383 DOI: 10.1016/j.dib.2020.105876] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/05/2020] [Accepted: 06/10/2020] [Indexed: 12/01/2022] Open
Abstract
A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed on people with Parkinson's disease, multiple system atrophy, and age-matched healthy volunteers. Participants underwent twelve videotaped procedures rated by a trained examiner while connected to four accelerometers [1,2] generating a trace of the three location dimensions expressed as spreadsheets [3,4]. The signals of the five repetitive motion items [1,2] underwent processing to fast Fourier [5] and continuous wavelet transforms [6]. The dataset [7] includes the coding form with scores of the live ratings [1,2], the raw files [3], the converted spreadsheets [4], and the fast Fourier [5] and continuous wavelet transforms [6]. All files are unfiltered. The data also provide findings suitable to compare and contrast with data obtained by investigators applying the same procedure to other populations. Since this is an inexpensive procedure to quantitatively measure motions in Parkinson's disease and other movement disorders, this will be a valuable resource to colleagues, particularly in underdeveloped regions with limited budgets. The dataset will serve as a template for other investigations to develop novel techniques to facilitate the diagnosis, monitoring, and treatment of Parkinson's disease, other movement disorders, and other nervous and mental conditions. The procedure will provide the basis to obtain objective quantitative measurements of participants in clinical trials of new agents.
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Affiliation(s)
- Timothy P. Harrigan
- Research and Exploratory Development, Applied Physics Laboratory, The Johns Hopkins University, Laurel, MD, United States
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Brian J. Hwang
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anil K. Mathur
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kelly A. Mills
- Neuromodulation and Advanced Therapies Clinic, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Alexander Y. Pantelyat
- Atypical Parkinsonism Center, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jee A. Bang
- Johns Hopkins Huntington Center of Excellence, Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Alveena B. Syed
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Pankhuri Vyas
- Department of Otolaryngology-Head and Neck Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Samuel D. Martin
- Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD, United States
| | - Armaan Jamal
- Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD, United States
| | - Liran Ziegelman
- Neuroscience Program, College of Liberal Arts and Sciences, University of Illinois at Champaign-Urbana, Champaign-Urbana, IL, United States
| | - Manuel E. Hernandez
- Neuroscience Program, College of Liberal Arts and Sciences, University of Illinois at Champaign-Urbana, Champaign-Urbana, IL, United States
- Department of Kinesiology and Community Health, College of Applied Health Sciences, University of Illinois at Champaign-Urbana, Champaign-Urbana, IL, United States
| | - Dean F. Wong
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - James Robert Brašić
- Section of High Resolution Brain Positron Emission Tomography Imaging, Division of Nuclear Medicine and Molecular Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Kaur R, Sun R, Ziegelman L, Sowers R, Hernandez ME. Using Virtual Reality to Examine the Neural and Physiological Responses to Height and Perturbations in Quiet Standing. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:5233-5236. [PMID: 31947038 DOI: 10.1109/embc.2019.8857647] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
We describe an experimental setup, which uses virtual reality to understand neural responses to height and perturbations in human postural control. This system could help clinicians develop better methods to alleviate symptoms from a significant fear of heights, especially in the elderly and those with movement disorders, such as Parkinson's disease. In our design, EEG and EKG systems monitor the participants' neural responses and heart activities respectively, while they try to maintain balance on a force plate in an induced virtual world, experiencing randomized height changes and perturbations. These responses are then analyzed to understand the participants' anxiety caused by height and postural challenges.
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