Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds

Werner, Charlotte; Easthope, Chris; Curt, Armin; Demkó, László

doi:10.3390/s21217381

Cited by 14 publications

(16 citation statements)

References 42 publications

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“…A data-driven selection of relevant sensor-derived gait parameters for a comprehensive characterization of walking after a spinal cord injury using an extensive dataset is still missing. So far, studies using IMUs to characterize walking in individuals with SCI focused mainly on the validity of the sensor-derived metrics [ 18 , 19 ], the test-retest reliability [ 20 ], or sensor-derived metrics were manually selected to compare different walking conditions [ 21 – 23 ].…”

Section: Introductionmentioning

confidence: 99%

Data-driven characterization of walking after a spinal cord injury using inertial sensors

Werner

Gönel

Lerch

et al. 2023

J NeuroEngineering Rehabil

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Background An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson’s disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity. Methods The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters. Results Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient’s walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors. Conclusions In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.

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Section: Introductionmentioning

confidence: 99%

Data-driven characterization of walking after a spinal cord injury using inertial sensors

Werner

Gönel

Lerch

et al. 2023

J NeuroEngineering Rehabil

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“…Detailed analysis of walking motion in individuals with stroke and spinal cord injury using a single sensor resulted in considerable measurement errors of walking distance and step count when using wrist or arm-mounted accelerometers ( Jayaraman et al, 2018 ; Compagnat et al, 2019 ). Measurement errors were smaller when locating the sensor at the hips, at the ankles, or when applying multiple sensors ( Werner et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…In healthy individuals, a minimal sensor setup for basic activities such as sitting, standing, and walking requires two sensors, ideally placed on the waist and ankle ( Allahbakhshi et al, 2019 ). However, additional sensors are desirable to increase the number of detectable activities and provide a basis for movement intensity and quality, such as real-world gait analysis ( Renggli et al, 2020 ; Werner et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

et al. 2022

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Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population.Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

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“…These time points were determined from the video recordings. The sensor-based walking speed was estimated from the ankle-worn sensors, by applying an algorithm originally developed for the spinal cord injured population [ 14 ]. In brief, the algorithm derives an adaptive threshold from the main frequency component of the gyroscope data, which corresponds to the cadence of walking.…”

Section: Methodsmentioning

confidence: 99%

Accuracy and comparison of sensor-based gait speed estimations under standardized and daily life conditions in children undergoing rehabilitation

Rast

Aschwanden

Werner

et al. 2022

J NeuroEngineering Rehabil

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Background Gait speed is a widely used outcome measure to assess the walking abilities of children undergoing rehabilitation. It is routinely determined during a walking test under standardized conditions, but it remains unclear whether these outcomes reflect the children's performance in daily life. An ankle-worn inertial sensor provides a usable opportunity to measure gait speed in the children's habitual environment. However, sensor-based gait speed estimations need to be accurate to allow for comparison of the children's gait speed between a test situation and daily life. Hence, the first aim of this study was to determine the measurement error of a novel algorithm that estimates gait speed based on data of a single ankle-worn inertial sensor in children undergoing rehabilitation. The second aim of this study was to compare the children’s gait speed between standardized and daily life conditions. Methods Twenty-four children with walking impairments completed four walking tests at different speeds (standardized condition) and were monitored for one hour during leisure or school time (daily life condition). We determined accuracy by comparing sensor-based gait speed estimations with a reference method in both conditions. Eventually, we compared individual gait speeds between the two conditions. Results The measurement error was 0.01 ± 0.07 m/s under the standardized and 0.04 ± 0.06 m/s under the daily life condition. Besides, the majority of children did not use the same speed during the test situation as in daily life. Conclusion This study demonstrates an accurate method to measure children's gait speed during standardized walking tests and in the children's habitual environment after rehabilitation. It only requires a single ankle sensor, which potentially increases wearing time and data quality of measurements in daily life. We recommend placing the sensor on the less affected side, unless the child wears one orthosis. In this latter case, the sensor should be placed on the side with the orthosis. Moreover, this study showed that most children did not use the same speed in the two conditions, which encourages the use of wearable inertial sensors to assess the children's walking performance in their habitual environment following rehabilitation.

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Towards a Mobile Gait Analysis for Patients with a Spinal Cord Injury: A Robust Algorithm Validated for Slow Walking Speeds

Cited by 14 publications

References 42 publications

Data-driven characterization of walking after a spinal cord injury using inertial sensors

Data-driven characterization of walking after a spinal cord injury using inertial sensors

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Accuracy and comparison of sensor-based gait speed estimations under standardized and daily life conditions in children undergoing rehabilitation

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