Minimal detectable change of kinematic and spatiotemporal parameters in patients with chronic stroke across three sessions of gait analysis

Geiger, Maxime; Supiot, Anthony; Pradon, Didier; Do, Manh-Cuong; Zory, Raphaël; Roche, Nicolás

doi:10.1016/j.humov.2019.01.011

Cited by 33 publications

(35 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, people poststroke typically exhibit less variability on a treadmill than overground, shrinking treadmill-derived MDCs and making them more difficult to resolve. For betweensession assessment, recent work by Geiger et al (2019) found stride length MDCs of around 11.96 cm in poststroke gait which-by the same reasoning-would require standard deviations in ranges lower than 6 cm. Standard deviations of less than 1.3 cm (P) and 2.3 cm (NP) achieved in this work would be sufficiently small to meet these requirements in both cases.…”

Section: E2-12mentioning

confidence: 89%

Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

et al. 2021

View full text Add to dashboard Cite

Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, and task-specific gait training is instrumental for effective gait rehabilitation, characteristics that our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision and frequency to detect subtle changes in intervention efficacy during both conventional and exosuit-assisted gait training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors to reconstruct three clinically meaningful gait metrics related to circumduction, foot clearance, and stride length. Our method corrects sensor drift using instantaneous information from both sides of the body. This approach makes our method robust to irregular walking conditions poststroke as well as usable in real-time applications, such as real-time movement monitoring, exosuit assistance control, and biofeedback. We validate our algorithm in eight people poststroke in comparison to lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) for circumduction, −0.6 cm (−3.5%) for foot clearance, and 3.8 cm (3.6%) for stride length. A single-participant case study shows our technique’s promise in daily-living environments by detecting exosuit-induced changes in gait while walking in a busy outdoor plaza.

show abstract

Section: E2-12mentioning

confidence: 89%

Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Minimal detectable change (MDC) values for these spatiotemporal gait parameters have been measured for a variety of pathological populations, including chronic stroke patients (Geiger et al, 2019), adults with multiple sclerosis (Andreopoulou et al, 2019), post-incomplete spinal chord injury patients (Nair et al, 2012), chronic low back pain patients (Fernandes et al, 2015), and adults with cerebral palsy (Levin et al, 2019), and range from 0.11 m/s to 0.23 m/s for gait speed, 4 cm to 17 cm for step length, 8 cm to 17 cm for stride length, and 2 cm to 3 cm for stride width. Similarly, time-based gait parameter MDC values have been reported as 0.04 s to 0.05 s for step time, 0.09 s for cycle time, 0.03 s to 0.28 s for swing time, 0.06 s to 0.09 s for stance time, and 0.03 s to 0.69 s for double limb support time (Fernandes et al, 2015;Nair et al, 2012;Wittwer et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko

Laende

Strutzenberger

et al. 2021

Journal of Biomechanics

View full text Add to dashboard Cite

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and pressure sensitive gait mats. For the first study, thirty healthy adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heelstrike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems.Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters during treadmill and overground gait.

show abstract

“…In this regard, Marin et al [17] recently summarized the error sources of a gait analysis test in the following groups: participant intrinsic variation, soft tissue movement, relative movement between the device and skin, positioning, instrument accuracy, gait event detection, and anatomical calibration. As shown in other studies [2,[48][49][50], the magnitude of these errors in our experiment could be estimated for each participant using Bland and Altman's limits of agreement [51] (see Equation ( 1)). Using these errors as threshold δ, the MBD method provides the probability that a change had been more than zero.…”

Section: Magnitude-based Decision (Mbd) To Monitor Individuals With Gait Analysismentioning

confidence: 82%

Is My Patient Improving? Individualized Gait Analysis in Rehabilitation

Marín

Blanco

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

In the rehabilitation field, clinicians are continually struggling to assess improvements in patients following interventions. In this paper, we propose an approach to use gait analysis based on inertial motion capture (MoCap) to monitor individuals during rehabilitation. Gait is a cyclical movement that generates a sufficiently large data sample in each capture session to statistically compare two different sessions from a single patient. Using this crucial idea, 21 heterogeneous patients with hemiplegic spasticity were assessed using gait analysis before and after receiving treatment with botulinum toxin injections. Afterwards, the two sessions for each patient were compared using the magnitude-based decision statistical method. Due to the challenge of classifying changes in gait variables such as improvements or impairments, assessing each patient’s progress required an interpretative process. After completing this process, we determined that 10 patients showed overall improvement, five patients showed overall impairment, and six patients did not show any overall change. Finally, the interpretation process was summarized by developing guidelines to aid in future assessments. In this manner, our approach provides graphical information about the patients’ progress to assess improvement following intervention and to support decision-making. This research contributes to integrating MoCap-based gait analysis into rehabilitation.

show abstract

Minimal detectable change of kinematic and spatiotemporal parameters in patients with chronic stroke across three sessions of gait analysis

Cited by 33 publications

References 34 publications

Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Is My Patient Improving? Individualized Gait Analysis in Rehabilitation

Contact Info

Product

Resources

About