A possible parameter for gait clinimetric evaluation in Parkinson’s disease patients

Lescano, Claudia N.; Rodrigo, Silvia E.; Christian, Donna

doi:10.1088/1742-6596/705/1/012019

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…For instance, Barnett et al [ 49 ] showed that vGRF measurements obtained with insole systems were less valid than those obtained with a force platform due to noise reduction thresholds. Other studies noted a delay in data acquisition during specific phases of the gait cycle when using insoles [ 50 ]. Therefore, it is advisable to compare the vGRFs collected with the same instruments.…”

Section: Discussionmentioning

confidence: 99%

Vertical Ground Reaction Forces in Parkinson’s Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

Giardini,

Turcato,

Arcolin

et al. 2023

Sensors

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This study aimed to investigate and compare the vertical Ground Reaction Forces (vGRFs) of patients with Parkinson’s Disease (PwPD) and healthy subjects (HS) when the confounding effect of walking speed was absent. Therefore, eighteen PwPD and eighteen age- and linear walking speed-matched HS were recruited. Using plantar pressure insoles, participants walked along linear and curvilinear paths at self-selected speeds. Interestingly, PwPD exhibited similar walking speed to HS during curvilinear trajectories (p = 0.48) and similar vGRF during both linear and curvilinear paths. In both groups, vGRF at initial contact and terminal stance was higher during linear walking, while vGRF at mid-stance was higher in curvilinear trajectories. Similarly, the time to peak vGRF at each phase showed no significant group differences. The vGRF timing variability was different between the two groups, particularly at terminal stance (p < 0.001). In conclusion, PwPD and HS showed similar modifications in vGRF and a similar reduction in gait speed during curvilinear paths when matched for linear walking speed. This emphasized the importance of considering walking speed when assessing gait dynamics in PwPD. This study also suggests the possibility of the variability of specific temporal measures in differentiating the gait patterns of PwPD versus those of HS, even in the early stages of the disease.

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Section: Discussionmentioning

confidence: 99%

Vertical Ground Reaction Forces in Parkinson’s Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

Giardini,

Turcato,

Arcolin

et al. 2023

Sensors

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show abstract

“…Then, a physiotherapist conducted the functional assessment proposed, and scored the participant again on the HY scale. Stages IV and V were excluded from the study due to the implied severe disability that made it difficult to perform the test independently without the use of assistive products (Giladi et al, 2001;Goetz et al, 2004;Lescano et al, 2016).…”

Section: Participantsmentioning

confidence: 99%

Classification of Parkinson's disease stages with a two-stage deep neural network

Pedrero-Sánchez

Belda-Lois

Serra-Añó

et al. 2023

Front. Aging Neurosci.

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IntroductionParkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test.MethodsThe proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals.ResultsThis stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase.DiscussionThe proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.

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“…These observations suggest that foot force is affected by PD. Lescano et al aimed to analyze gait parameters, stance, swing phase duration, and the magnitude of the vertical component of the ground reaction force for the purpose of assessing whether there are statistically significant differences between PD patients in stages 2 and 2.5 (modified Hoehn and Yahr (HY) scale, see description in Table 1) [7]. Gait information has been developed for movement analysis in healthy control (CO, term defined by PhysioNet [8]) subjects and other subjects with different types of diseases.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease

Setiawan

Lin

2021

Sensors

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Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.

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A possible parameter for gait clinimetric evaluation in Parkinson’s disease patients

Cited by 4 publications

References 16 publications

Vertical Ground Reaction Forces in Parkinson’s Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

Vertical Ground Reaction Forces in Parkinson’s Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

Classification of Parkinson's disease stages with a two-stage deep neural network

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease

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