Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning

Leandro Donisi; Giuseppe Cesarelli; Pietro Balbi; Vincenzo Provitera; Bernardo Lanzillo; Armando Coccia; Giovanni D'Addio; Leandro Donisi; Giuseppe Cesarelli; Pietro Balbi; Vincenzo Provitera; Bernardo Lanzillo; Armando Coccia; Giovanni D'Addio

doi:10.3934/mbe.2021348

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 6995-7009. doi: 10.3934/mbe.2021348

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Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning

1.
Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Campania, Italy
2.
Department of Bioengineering, Institute of Care and Scientific Research ICS Maugeri, Telese Terme, Campania, Italy
3.
Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Naples, Campania, Italy
4.
Department of Neurorehabilitation, Institute of Care and Scientific Research ICS Maugeri, Telese Terme, Campania, Italy
5.
Department of Information Technology and Electrical Engineering, University of Naples Federico II, Naples, Campania, Italy

Academic Editor：Carlo Ricciardi

Received: 30 April 2021 Accepted: 17 August 2021 Published: 23 August 2021

Parkinson's disease is the second most common neurodegenerative disorder in the world. Assumed that gait dysfunctions represent a major motor symptom for the pathology, gait analysis can provide clinicians quantitative information about the rehabilitation outcome of patients. In this scenario, wearable inertial systems for gait analysis can be a valid tool to assess the functional recovery of patients in an automatic and quantitative way, helping clinicians in decision making. Aim of the study is to evaluate the impact of the short-term rehabilitation on gait and balance of patients with Parkinson's disease. A cohort of 12 patients with Idiopathic Parkinson's disease performed a gait analysis session instrumented by a wearable inertial system for gait analysis: Opal System, by APDM Inc., with spatial and temporal parameters being analyzed through a statistic and machine learning approach. Six out of fourteen motion parameters exhibited a statistically significant difference between the measurements at admission and at discharge of the patients, while the machine learning analysis confirmed the separability of the two phases in terms of Accuracy and Area under the Receiving Operating Characteristic Curve. The rehabilitation treatment especially improved the motion parameters related to the gait. The study shows the positive impact on the gait of a short-term rehabilitation in patients with Parkinson's disease and the feasibility of the wearable inertial devices, that are increasingly spreading in clinical practice, to quantitatively assess the gait improvement.
- gait analysis,
- machine learning,
- Parkinson's disease,
- short-term rehabilitation,
- wearable inertial device
Citation: Leandro Donisi, Giuseppe Cesarelli, Pietro Balbi, Vincenzo Provitera, Bernardo Lanzillo, Armando Coccia, Giovanni D'Addio. Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6995-7009. doi: 10.3934/mbe.2021348

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Abstract

Parkinson's disease is the second most common neurodegenerative disorder in the world. Assumed that gait dysfunctions represent a major motor symptom for the pathology, gait analysis can provide clinicians quantitative information about the rehabilitation outcome of patients. In this scenario, wearable inertial systems for gait analysis can be a valid tool to assess the functional recovery of patients in an automatic and quantitative way, helping clinicians in decision making. Aim of the study is to evaluate the impact of the short-term rehabilitation on gait and balance of patients with Parkinson's disease. A cohort of 12 patients with Idiopathic Parkinson's disease performed a gait analysis session instrumented by a wearable inertial system for gait analysis: Opal System, by APDM Inc., with spatial and temporal parameters being analyzed through a statistic and machine learning approach. Six out of fourteen motion parameters exhibited a statistically significant difference between the measurements at admission and at discharge of the patients, while the machine learning analysis confirmed the separability of the two phases in terms of Accuracy and Area under the Receiving Operating Characteristic Curve. The rehabilitation treatment especially improved the motion parameters related to the gait. The study shows the positive impact on the gait of a short-term rehabilitation in patients with Parkinson's disease and the feasibility of the wearable inertial devices, that are increasingly spreading in clinical practice, to quantitatively assess the gait improvement.

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