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Article

Using a Video Device and a Deep Learning-Based Pose Estimator to Assess Gait Impairment in Neurodegenerative Related Disorders: A Pilot Study

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ENEA-Robotics and Artificial Intelligence Lab, “Casaccia” Research Centre, 00123 Rome, Italy
2
Department of Systems Medicine, Tor Vergata University of Rome, 00133 Rome, Italy
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IRCCS Santa Lucia Foundation, 00179 Rome, Italy
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Department for Life Quality Studies, University of Bologna, 47037 Rimini, Italy
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Centre of Space Bio-Medicine, Tor Vergata University of Rome, 00133 Rome, Italy
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Department of Industrial Engineering, Tor Vergata University of Rome, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Kelvin K.L. Wong
Appl. Sci. 2022, 12(9), 4642; https://doi.org/10.3390/app12094642
Received: 2 April 2022 / Revised: 23 April 2022 / Accepted: 27 April 2022 / Published: 5 May 2022
(This article belongs to the Special Issue Artificial Intelligence in Medicine and Healthcare)
As the world’s population is living longer, age-related neurodegenerative diseases are becoming a more significant global issue. Neurodegenerative diseases cause worsening motor, cognitive and autonomic dysfunction over time and reduce functional abilities required for daily living. Compromised motor performance is one of the first and most evident manifestations. In the case of Parkinson’s disease, these impairments are currently evaluated by experts through the use of rating scales. Although this method is widely used by experts worldwide, it includes subjective and error-prone motor examinations that also fail in the characterization of symptoms’ fluctuations. The aim of this study is to evaluate whether artificial intelligence techniques can be used to objectively assess gait impairment in subjects with Parkinson’s disease. This paper presents the results of a cohort of ten subjects, five with a Parkinson’s disease diagnosis at different degrees of severity. We experimentally demonstrate good effectiveness of the proposed system in extracting the main features concerning people’s gait during the standard tests that clinicians use to assess the burden of disease. This system can offer neurologists, through accurate and objective data, a second opinion or a suggestion to reconsider score assignment. Thanks to its simplicity, tactful and non-intrusive approach and clinical-grade accuracy, it can be adopted on an ongoing basis even in environments where people usually live and work. View Full-Text
Keywords: deep learning; artificial stereo vision; neurodegenerative disease; gait analysis deep learning; artificial stereo vision; neurodegenerative disease; gait analysis
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MDPI and ACS Style

Zanela, A.; Schirinzi, T.; Mercuri, N.B.; Stefani, A.; Romagnoli, C.; Annino, G.; Bonaiuto, V.; Cerroni, R. Using a Video Device and a Deep Learning-Based Pose Estimator to Assess Gait Impairment in Neurodegenerative Related Disorders: A Pilot Study. Appl. Sci. 2022, 12, 4642. https://doi.org/10.3390/app12094642

AMA Style

Zanela A, Schirinzi T, Mercuri NB, Stefani A, Romagnoli C, Annino G, Bonaiuto V, Cerroni R. Using a Video Device and a Deep Learning-Based Pose Estimator to Assess Gait Impairment in Neurodegenerative Related Disorders: A Pilot Study. Applied Sciences. 2022; 12(9):4642. https://doi.org/10.3390/app12094642

Chicago/Turabian Style

Zanela, Andrea, Tommaso Schirinzi, Nicola Biagio Mercuri, Alessandro Stefani, Cristian Romagnoli, Giuseppe Annino, Vincenzo Bonaiuto, and Rocco Cerroni. 2022. "Using a Video Device and a Deep Learning-Based Pose Estimator to Assess Gait Impairment in Neurodegenerative Related Disorders: A Pilot Study" Applied Sciences 12, no. 9: 4642. https://doi.org/10.3390/app12094642

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