Objective: To create a tool for raters certified in the MDS-UPDRS) [1] to blindly score online signal transforms of the output of the wearable sensors to record movements of the extremities impaired in Parkinson’s disease (PD) [2].

Background: The assessment of PD relies on the history of the present illness, the clinical interview, the physical examination, and the MDS-UPDRS [1] and other structured instruments. Methodology is needed for experts to remotely assess signals and transforms of a simple, low-cost, and accessible assessment system [2].

Method: An international team of trained raters certified in the MDS-UPDRS [1] (N=35) blindly rated virtual images of signals and fast Fourier transforms (FFT) and continuous wavelet transforms (CWT) of signals generated by a low-cost procedure to obtain continuous quantitative measurements of movements of people [1,2] administered to participants with PD (N=20) and age- and sex-matched healthy controls (N=8) [3]. Images were randomly and blindly presented to raters separately for (A) output signals and FFTs and (B) CWTs of the five repetitive items (finger tapping, hand movements, pronation-supination movement, toe tapping, foot agility) [1,2].

Raters were presented separate images for (A) output signals along with FFTs [4] and (B) CWTs [5] for each of the five repetitive movements on left and right to be rated for test and retest. The ratings of ten participants with PD with single ratings were presented as six images (x, y, z for finger and wrist or toe and ankle) in one panel. The ratings for two sessions of ten participants with PD and eight healthy controls were presented as two images (average of x, y, and z for finger and wrist or toe and ankle) [3,6-8].

Results: A parent regression exponential model (Y=-0.00291e1.13124+0.44694) exhibited optimal fit at alpha=0.05 with scores between 0 and 2 are harder to accurately rate as compared to a 3 or 4 [9-10].

Conclusion: The development of an online procedure for the independent, blind rating of signals and transforms of signals of a low-cost data acquisition and display system provides the tools for objective remote analysis by trained raters certified in the MDS-UPDRS [1] located around the world and correlation with visual observation of the movements, allowing clinicians to better identify the underlying health conditions and make better informed clinical decisions for appropriate and beneficial treatment interventions [6].

References: [1] C.G. Goetz, B.C. Tilley, S.R. Shaftman, G.T. Stebbins, S. Fahn, P. Martinez-Martin, W. Poewe, C. Sampaio, M.B. Stern, R. Dodel, B. Dubois, R. Holloway, J. Jankovic, J. Kulisevsky, A.E. Lang, A. Lees, S. Leurgans, P.A. LeWitt, D. Nyenhuis, C.W. Olanow, O. Rascol, A. Schrag, J.A. Teresi, J.J. van Hilten, N. LaPelle, for the Movement Disorder Society UPDRS Revision Task Force, Movement disorder society-sponsored revision of the unified parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results, Mov. Disord. 23 (2008) 2129–2170.
[2] G.N. McKay, T.P. Harrigan, J.R. Brašić, A low-cost quantitative continuous measurement of movements in the extremities of people with Parkinson’s disease, MethodsX 6 (2019) 169–189. https://doi.org/10.1016/j.mex.2018.12.017.
[3] J. Brasic, T. Harrigan, B. Hwang, K. Mills, A. Pantelyat, J. Bang, A. Syed, P. Vyas, S. Martin, A. Jamal, R. Panaparambil, A. Gaite, L. Ziegelman, M. Hernandez. Quantitative extremity movement measurement. Mov Disord 2020; 35 (suppl 1): S576-S578. [abstract] https://onlinelibrary.wiley.com/doi/epdf/10.1002/mds.28268
[4] H. Hakim, T. Kosuri, L. Zhao, L. Ziegelman, K. Mills, A. Pantelyat, J. Bang, M.E. Hernandez, J.R. Brasic. Use of accelerometer signals to improve classification of structured motor assessments of Parkinson’s Disease. Society for Neuroscience Annual Meeting, 2021. [abstract] https://www.abstractsonline.com/pp8/#!/10485/presentation/24079.
[5] T. Kosuri, H. Hakim, L. Ziegelman, K.A. Mills, A. Pantelyat, J. Bang, M.E. Hernandez, J.R. Brasic. Continuous wavelet transforms to improve the accuracy of motor assessments of Parkinson’s disease. Society for Neuroscience Annual Meeting, 2021. [sbstract] https://www.abstractsonline.com/pp8/#!/10485/presentation/24078.
[6] J.R. Brasic, L. Ziegelman, K.A. Mills, M.E. Hernandez. Feasibility of a system for human visual rating of signals from accelerometers on the extremities of people with Parkinson’s disease. Society for Neuroscience Annual Meeting, 2021. [abstract] https://www.abstractsonline.com/pp8/#!/10485/presentation/24083
[7] T.P. Harrigan, B.J. Hwang, A.K. Mathur, K.A. Mills, A.Y. Pantelyat, J.A. Bang, A.B. Syed, P. Vyas, S.D. Martin, A. Jamal, L. Ziegelman, M.E. Hernandez, D.F. Wong, J.R. Brašić. Dataset of quantitative structured office measurements of movements in the extremities. Data Brief. 2020, 31, 105876. https://doi.org/10.1016/ j.dib.2020.105876
[8] L. Ziegelman, M. Hernandez, T. Kosuri, S. Martin, M. Shneyderman, A. Suresh, A. Gorny, A. Syed, A. Qureshi, A. Mahmoud, A. Dutta, A. Jamal, A. Bhatnagar, C. Udoji, C. Cook, H. Youssef, H. Hakim, I. Brookshier, K. Kumari, K. Tang, L. Zhao, M. Shankar, M. Doheim, M. Mansour, M. Shehata, M. Eltony, N. Cho, N. Loza, R. Thakkar, R. Panaparambil, S. Bertucci, T. Elshourbagy, Y. Hu, T. Harrigan, J. Brasic. Signal processing of quantitative continuous measurement of movements in the extremities. Mendeley Data. V8. 2020. https://doi.org/10.17632/4dp4v7968z.8
[9] L. Ziegelman, T. Harrigan, J.R. Brasic, M.E. Hernandez. Characterization of motor impairment rating error of transformed IMU data. Society for Neuroscience Annual Meeting, 2021. [abstract] https://www.abstractsonline.com/pp8/#!/10485/presentation/24081.
[10] A.O. Sadaney. An affordable tool to detect and quantify movement impairment for the application of precision medicine to persons who may have Parkinson’s disease. Podium presentation at movement disorders session. Cairo International Neurology Conference, The Egyptian Society of Neurology, Psychiatry, and Neurosurgery, Cairo, Egypt. February 10, 2022.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/classification-of-extremity-movements-by-visual-observation-of-signal-transforms/