Classification of extremity movements by visual observation of signals and their transforms
Manuel Enrique Hernandez,
Liran Ziegelman,
Tanvi Kosuri,
Husain Hakim,
Luqi Zhao,
Kelly Alexander Mills,
James Robert Brašić
Affiliations
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 Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Public Health Studies, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neuroscience, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, 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 21205, 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 Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Public Health Studies, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neuroscience, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, 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 21205, United States
James Robert Brašić
Corresponding author.; Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Public Health Studies, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neuroscience, Krieger School of Arts and Sciences, The Johns Hopkins University, Baltimore, MD 21218, United States; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, 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 21205, United States
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.
