Gait Study of Parkinson's Disease Subjects using Haptic Cues with A Motorized Walker

Zhang, Minhua; Artan, N. Sertac; Gu, Huanying; Dong, Ziqian; Ganatra, Lyudmila Burina; Shermon, Suzanna; Rabin, Ely

doi:10.20944/preprints201809.0211.v1

Cited by 8 publications

(18 citation statements)

References 30 publications

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“…For spatiotemporal gait parameters, patients with earlystage PD exhibit a shorter SL and a more variable gait pattern. 21,23 Our research extends previous studies wherein the MPS group exhibit a shorter SL and a more variable gait pattern than HC. ST means the time it takes to walk one stride length.…”

Section: Gait Analysis Between Hc and Meds-off Subgroupssupporting

confidence: 86%

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Gait Analysis of Old Individuals with Mild Parkinsonian Signs and Those Individuals’ Gait Performance Benefits Little from Levodopa

Xu²,

Zhu

et al. 2021

RMHP

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Background and Purpose: Gait analysis and the effects of levodopa on the gait characteristics in Mild parkinsonian signs (MPS) are rarely published. The present research aimed to (1) analyze the gait characteristics in MPS; (2) explore the effects of levodopa on the gait performance of MPS. Methods: We enrolled 22 inpatients with MPS and 20 healthy control subjects (HC) from Nanjing Brain Hospital. The Unified Parkinson's Disease Rating Scale was used to evaluate motor symptoms. Acute levodopa challenge test was performed to explore the effects of levodopa on the gait performance of MPS. The instrumented stand and walk test was conducted for each participant and the JiBuEn gait analysis system was used to collect gait data. Results: For spatiotemporal parameters: Compared with HC, the state before taking levodopa/benserazide in MPS group (meds-off) demonstrated a decrease in stride length (SL) (p≤0.001), an increase in SL variability (p≤0.001), and swing phase time variability (p=0.016). Compared with meds-off, the state after 1 hour of taking levodopa/benserazide in MPS group (meds-on) exhibited an increase in SL (p≤0.001), a decrease in SL variability (p≤0.001). For kinematic parameters: Compared with HC, meds-off demonstrated a decrease in heel strike angle (p=0.008), range of motion (ROM) of knee joint (p=0.011) and ROM of hip joint (p=0.007). Compared with meds-off, meds-on exhibited an increase in HS (p≤0.001). Bradykinesia and rigidity scores were significantly correlated with gait parameters. Conclusion: Although the clinical symptoms of the MPS group are mild, their gait damage is obvious and they exhibited a decreased SL and joints movement, and a more variable gait pattern. Levodopa had little effect on the gait performance of those individuals.

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Section: Gait Analysis Between Hc and Meds-off Subgroupssupporting

confidence: 86%

“…19 The asymmetry index (AI) was used to assess the symmetry of gait parameters (Formula (3)). [20][21][22] Formula (1): CV separate = SD ÷ mean value…”

Section: Resultsmentioning

confidence: 99%

Gait Analysis of Old Individuals with Mild Parkinsonian Signs and Those Individuals’ Gait Performance Benefits Little from Levodopa

Xu²,

Zhu

et al. 2021

RMHP

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“…In another study, Zhang et al [44] developed a framework to analyze, extract and compare the gait features of 6 PD patients while walking under three different conditions-without any support, roller walker pushing and holding a powered walker in different speeds. The study made use of nine Vicon Mocap system camera and a set of reflective markers to determine the location of the body landmarks.…”

Section: A: Marker-based (Model-build)mentioning

confidence: 99%

“…Also, the combination of Butterworth filters can be formed to create the one with higher-order but sometimes it results in declined performance due to the larger size. Another type of low pass filter named as Savitzky-Golay filter (SGF) [44], [51] has been adopted for smoothing of noisy data and is specifically preferred in biomedical applications. These filters are based on the idea of convolution and suppress the least square error that occurs while a polynomial is fitted at every frame of noisy data.…”

Section: ) Filteringmentioning

confidence: 99%

Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A Survey

2019

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Parkinson's Disease (PD) being the second most hazardous neurological disorder has developed its roots in damaging people's quality of life (QOL). The ineffectiveness of clinical rating scales makes the PD diagnosis a very complicated task. Thus, more efficient systems are required to perform an automated evaluation of PD for its earlier detection and to enhance life expectancy rate. Gait based clinical diagnosis can provide useful indications regarding the presence of PD. From recent years, computer vision-based (VB) analysis is in great demand and seems to be highly effective in PD inspection. The objective of this article is to systematically analyze the applications of computer vision in PD evaluation through gait. This paper surveys the VB PD gait acquisition modalities as well as provides a concise overview of preprocessing techniques. The study presents a description of PD related gait features, extraction and selection methods used for PD analysis. A number of machine learning techniques for classification of PD and healthy gait are also discussed. This article extensively surveys PD gait datasets considering data from 1997 to 2018. Also, several research gaps in existing studies have identified that need to be addressed in the future. At last, an outline of the proposed idea is given that can cope up with the related issues and can lead to quality VB PD gait investigation.

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“…For this reason, we created a mobile system that provides objective measurement data to the physician to be able to evaluate motor disease quantitatively. Gait deviations such as asymmetry are one of the characteristic symptoms of patients with PD that contribute to the risk of falls [3] . For the continuous measurement of motion in daily life, we propose the use of wearable microcontroller-based systems.…”

Section: Introductionmentioning

confidence: 99%

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

Steinmetzer¹,

Wilberg²,

Bönninger³

et al. 2020

Microprocessors and Microsystems

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Gait deviations such as asymmetry are one of the characteristic symptoms of motor dysfunctions that contribute to the risk of falls. Our objective is to measure gait abnormalities such as asymmetry of the lower limbs in order to evaluate the diagnosis more objectively. For the measurement we use inertial measurement unit (IMU) sensors and force sensors, which are integrated in wristbands and insoles. To extend the battery life of wearable devices, we only save data of the activity gait within the wearables. Therefore we perform activity recognition with a smartphone. Using convolutional neural network (CNN) we achieved an accuracy of 94.7 % of the activity gait recognition. Before recording we synchronize the wearable sensors and reach a maximum latencies of 3 ms. Before the analysis of the symmetry we detect the strides by using a CNN with an accuracy of 98.8 %. For the symmetry evaluation we used dynamic time warping (DTW). The DTW enables us to calculate symmetry of the complete time series of human gait.

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Gait Study of Parkinson's Disease Subjects using Haptic Cues with A Motorized Walker

Cited by 8 publications

References 30 publications

Gait Analysis of Old Individuals with Mild Parkinsonian Signs and Those Individuals’ Gait Performance Benefits Little from Levodopa

Gait Analysis of Old Individuals with Mild Parkinsonian Signs and Those Individuals’ Gait Performance Benefits Little from Levodopa

Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A Survey

Analyzing gait symmetry with automatically synchronized wearable sensors in daily life

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