Nonlinear characteristics of gait signals in neurodegenerative diseases
Based on the asymmetric characteristics of left and right movements in patients with neurodegenerative diseases and their inherent coupling relationships, as well as the inevitable internal connection between them according to the principles of mechanical kinematics, and a processing method for the...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1607273/full |
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| author | Yang Yue Na Chang Zonglin Shi |
| author_facet | Yang Yue Na Chang Zonglin Shi |
| author_sort | Yang Yue |
| collection | DOAJ |
| description | Based on the asymmetric characteristics of left and right movements in patients with neurodegenerative diseases and their inherent coupling relationships, as well as the inevitable internal connection between them according to the principles of mechanical kinematics, and a processing method for the ratio of gait signals to left and right limb data is proposed. Using gait time series data collected from left and right limbs via pressure-sensitive insoles, a comparison was conducted among patients with Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS), Huntington's disease (HD), and a healthy control group (Ctrl) in terms of the average, standard deviation, and coefficient of variation of the left and right sequences, as well as the ratios between them. It was discovered that there exists a close correlation between the ratios of left to right sequences and the actual standard deviation and coefficient of variation of these sequences. These ratios can be utilized for identifying the categories of PD, ALS, and HD patients. After using a median filter (n = 3) to filter four sets of stride ratio data (Ctr1, A1s, PD, and HD), it was found that the data before filtering generally showed significant fluctuations, with many peaks and valleys, indicating that the original data may contain a lot of noise or outliers. In contrast, the filtered data showed relatively smaller fluctuations and a smoother curve, indicating that the filtering process effectively reduced noise in the data and enhanced its stability. The raw data distribution for the left and right limbs of patients with PD, ALS, HD, and the Ctrl was relatively large, posing certain difficulties in analyzing the patients' diseases. The use of the ratio of left to right data effectively improves the discreteness of the data. The ranking of CO complexity features from highest to lowest is ALS, HD, PD, and Ctrl. The ranking of sample entropy features from largest to smallest is ALS, HD, PD, and Ctrl. The ranking of wavelet coefficient features from largest to smallest is ALS, PD, HD, and Ctrl. |
| format | Article |
| id | doaj-art-c9c58f3fc5e146b28b160c92b5d29be3 |
| institution | OA Journals |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-c9c58f3fc5e146b28b160c92b5d29be32025-08-20T02:06:39ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-06-011610.3389/fneur.2025.16072731607273Nonlinear characteristics of gait signals in neurodegenerative diseasesYang Yue0Na Chang1Zonglin Shi2Department of Mechanical Engineering, University College London, London, United KingdomDepartment of Neurology, Huaihe Hospital, Henan University, Kaifeng, ChinaDepartment of Neurology, Huaihe Hospital, Henan University, Kaifeng, ChinaBased on the asymmetric characteristics of left and right movements in patients with neurodegenerative diseases and their inherent coupling relationships, as well as the inevitable internal connection between them according to the principles of mechanical kinematics, and a processing method for the ratio of gait signals to left and right limb data is proposed. Using gait time series data collected from left and right limbs via pressure-sensitive insoles, a comparison was conducted among patients with Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS), Huntington's disease (HD), and a healthy control group (Ctrl) in terms of the average, standard deviation, and coefficient of variation of the left and right sequences, as well as the ratios between them. It was discovered that there exists a close correlation between the ratios of left to right sequences and the actual standard deviation and coefficient of variation of these sequences. These ratios can be utilized for identifying the categories of PD, ALS, and HD patients. After using a median filter (n = 3) to filter four sets of stride ratio data (Ctr1, A1s, PD, and HD), it was found that the data before filtering generally showed significant fluctuations, with many peaks and valleys, indicating that the original data may contain a lot of noise or outliers. In contrast, the filtered data showed relatively smaller fluctuations and a smoother curve, indicating that the filtering process effectively reduced noise in the data and enhanced its stability. The raw data distribution for the left and right limbs of patients with PD, ALS, HD, and the Ctrl was relatively large, posing certain difficulties in analyzing the patients' diseases. The use of the ratio of left to right data effectively improves the discreteness of the data. The ranking of CO complexity features from highest to lowest is ALS, HD, PD, and Ctrl. The ranking of sample entropy features from largest to smallest is ALS, HD, PD, and Ctrl. The ranking of wavelet coefficient features from largest to smallest is ALS, PD, HD, and Ctrl.https://www.frontiersin.org/articles/10.3389/fneur.2025.1607273/fullgait signalratio of left to right sequencesmedian filteringnonlinear characteristicscomplexity |
| spellingShingle | Yang Yue Na Chang Zonglin Shi Nonlinear characteristics of gait signals in neurodegenerative diseases Frontiers in Neurology gait signal ratio of left to right sequences median filtering nonlinear characteristics complexity |
| title | Nonlinear characteristics of gait signals in neurodegenerative diseases |
| title_full | Nonlinear characteristics of gait signals in neurodegenerative diseases |
| title_fullStr | Nonlinear characteristics of gait signals in neurodegenerative diseases |
| title_full_unstemmed | Nonlinear characteristics of gait signals in neurodegenerative diseases |
| title_short | Nonlinear characteristics of gait signals in neurodegenerative diseases |
| title_sort | nonlinear characteristics of gait signals in neurodegenerative diseases |
| topic | gait signal ratio of left to right sequences median filtering nonlinear characteristics complexity |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1607273/full |
| work_keys_str_mv | AT yangyue nonlinearcharacteristicsofgaitsignalsinneurodegenerativediseases AT nachang nonlinearcharacteristicsofgaitsignalsinneurodegenerativediseases AT zonglinshi nonlinearcharacteristicsofgaitsignalsinneurodegenerativediseases |