Evaluation of the effects on stride-to-stride variability and gait asymmetry in children with Cerebral Palsy wearing the WAKE-up ankle module

Mileti, Ilaria; Taborri, Juri; Rossi, Stefano; Petrarca, Maurizio; Patanè, Fabrizio; Cappa, Paolo

doi:10.1109/memea.2016.7533748

Cited by 24 publications

(26 citation statements)

References 20 publications

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“…This implies that the maximum value of impedance in children is not in correspondence of the DP direction and can be ascribed to the incomplete development of muscles. This finding indicates that children are not able to provide maximum impedance during movements that mainly involve the dorsi-plantarflexion of the ankle, such as walking; thus, it could be one of the main reasons for the well-known higher stride-to-stride variability found for children [33].…”

Section: Age-related Differences Of Ankle Impedancementioning

confidence: 94%

“…In addition, our outcomes are in accordance with [44], confirming that ankle stiffness increases with age and stature. The lowest values of ankle impedance found for children can be also considered one of the main causes of the dynamic instability of children when walking [33,42]. In fact, a complete development of lower limb joints guarantees a natural interaction between limbs and the environment, and a proper value of ankle impedance allows correct regulating and controlling movements [45].…”

Section: Age-related Differences Of Ankle Impedancementioning

confidence: 99%

“…Currently, the design of robotic devices for the rehabilitation of children is an increasingly appealing and challenging field [16,[31][32][33][34]. However, properly scaling robotic devices designed for adults to match the characteristics of children, remains an existing challenge, and is generally recognized as an important goal to achieve in the robotic field [35].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

et al. 2019

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A deep analysis of ankle mechanical properties is a fundamental step in the design of an exoskeleton, especially if it is to be suitable for both adults and children. This study aims at assessing age-related differences of ankle properties using pediAnklebot. To achieve this aim, we enrolled 16 young adults and 10 children in an experimental protocol that consisted of the evaluation of ankle mechanical impedance and kinematic performance. Ankle impedance was measured by imposing stochastic torque perturbations in dorsi-plantarflexion and inversion-eversion directions. Kinematic performance was assessed by asking participants to perform a goaldirected task. Magnitude and anisotropy of impedance were computed using a multipleinput multiple-output system. Kinematic performance was quantified by computing indices of accuracy, smoothness, and timing. Adults showed greater magnitude of ankle impedance in both directions and for all frequencies, while the anisotropy was higher in children. By analyzing kinematics, children performed movements with lower accuracy and higher smoothness, while no differences were found for the duration of the movement. In addition, adults showed a greater ability to stop the movement when hitting the target. These findings can be useful to a proper development of robotic devices, as well as for implementation of specific training programs.

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Section: Age-related Differences Of Ankle Impedancementioning

confidence: 94%

Section: Age-related Differences Of Ankle Impedancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

et al. 2019

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“…In the last decade, increasing attention has been paid to novel methodologies for the real-time gait phase detection [ 1 , 2 ]. Assessment of gait temporal parameters has the great potential to objectively evaluate patient recovery during rehabilitation treatments, to discriminate between normal and pathological gait, and to reliably quantify typical hallmarks among gait impairments, such as motor fluctuations in Parkinson’s Disease (PD) [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition

Mileti

Germanotta

Sipio

et al. 2018

Sensors

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Monitoring gait quality in daily activities through wearable sensors has the potential to improve medical assessment in Parkinson’s Disease (PD). In this study, four gait partitioning methods, two based on thresholds and two based on a machine learning approach, considering the four-phase model, were compared. The methods were tested on 26 PD patients, both in OFF and ON levodopa conditions, and 11 healthy subjects, during walking tasks. All subjects were equipped with inertial sensors placed on feet. Force resistive sensors were used to assess reference time sequence of gait phases. Goodness Index (G) was evaluated to assess accuracy in gait phases estimation. A novel synthetic index called Gait Phase Quality Index (GPQI) was proposed for gait quality assessment. Results revealed optimum performance (G < 0.25) for three tested methods and good performance (0.25 < G < 0.70) for one threshold method. The GPQI resulted significantly higher in PD patients than in healthy subjects, showing a moderate correlation with clinical scales score. Furthermore, in patients with severe gait impairment, GPQI was found higher in OFF than in ON state. Our results unveil the possibility of monitoring gait quality in PD through real-time gait partitioning based on wearable sensors.

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“…Following the methodology proposed in (Røislien et al 2012), groups of five curves (k j (t) with j is the number of by sample amplitude variations from the averaged pattern, which is typical for each joint and representative for the within-subject variability (Winter 1984); (c) the offset among curves, mostly depending on marker repositioning (Leardini et al 1999); (d) the time shift due to physiological and/or pathological gait phases variability (Mileti et al 2016); and (e) the different curve shapes among joints and planes (Røislien et al 2012). The effects of each confusing-factor on the RI have not been tested, and a comparative analysis that aims to clearly interpret the relationship between RI and the confusing-factors is still lacking in literature.…”

Section: Sine-curve Datamentioning

confidence: 99%

How to choose and interpret similarity indices to quantify the variability in gait joint kinematics

Marco

Scalona

Pacilli

et al. 2018

International Biomechanics

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Repeatability and reproducibility indices are often used in gait analysis to validate models and assess patients in their follow-up. When comparing joint kinematics, their interpretation can be ambiguous due to a lack of understanding of the exact sources of their variations. This paper studied four indices (Root Mean Square Deviation, Mean Absolute Variability, Coefficient of Multiple Correlation, and Linear Fit Method) in relation to five confusing-factors: joints' range of motion, sample-by-sample amplitude variability, offset, time shift and curve shape. A first simulation was conducted to test the mathematics behind each index. A second simulation tested the influence of the curve shape on the indices using a Fourier's decomposition. The Coefficient of Multiple Correlation and the Linear Fit method Coefficients were independent from the range of motion. Different Coefficients of Multiple Correlation were found among different joints, leading to misinterpretation of the results. The Linear Fit Method coefficients should not be adopted when time shift increases. Root Mean Square Deviation and Mean Absolute Variability were sensitive to all the confusing-factors. The Linear Fit Method coefficients seemed to be the most suitable to assess gait data variability, complemented with Root Mean Square Deviation or Mean Absolute Variability as measurements of data dispersion.

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Evaluation of the effects on stride-to-stride variability and gait asymmetry in children with Cerebral Palsy wearing the WAKE-up ankle module

Cited by 24 publications

References 20 publications

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

Quantifying Age-Related Differences of Ankle Mechanical Properties Using a Robotic Device

Measuring Gait Quality in Parkinson’s Disease through Real-Time Gait Phase Recognition

How to choose and interpret similarity indices to quantify the variability in gait joint kinematics

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