Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters

Kluge, Felix; Gaßner, Heiko; Hannink, Julius; Pasluosta, Cristian; Klucken, Jochen; Eskofier, Bjoern M.

doi:10.3390/s17071522

Cited by 119 publications

(137 citation statements)

References 49 publications

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“…Specifically, as segmentation of gait signals into single steps is an important basis for objective gait analysis, we have previously validated our segmentation procedure in PD patients [ 18 ]. We recently assessed the concurrent validity and test-retest reliability of the body worn sensor-based gait analysis system in PD patients [ 22 ]. The wearable sensor-based gait system accurately captured gait parameters as compared to the reference camera-based system.…”

Section: Introductionmentioning

confidence: 99%

Wearable sensors objectively measure gait parameters in Parkinson’s disease

et al. 2017

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Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson’s disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson’s disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson’s disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects’ preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson’s disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson’s disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson’s disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson’s disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

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Section: Introductionmentioning

confidence: 99%

Wearable sensors objectively measure gait parameters in Parkinson’s disease

et al. 2017

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“…There was no golden standard system applied next to the IMU sensor. In experimental settings, optical motion tracking systems are often used as golden standard to test the IMU performance (e.g., Seel et al, 2014;Kluge et al, 2017;Bosch et al, 2018). Here, we used subjective annotation by a person to define the step start and end positions in the IMU profiles knowing that the animals underwent a walking test.…”

Section: Discussionmentioning

confidence: 99%

“…Besides acceleration, they also measure rotational velocity; together, they can indicate orientation. Human locomotion has been well studied using IMUs, where they are considered as a cost-effective alternative to optical motion systems, which are the golden standard in kinematic analysis in laboratory settings (e.g., Seel et al, 2014;Kluge et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Automated Step Detection in Inertial Measurement Unit Data From Turkeys

et al. 2020

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Locomotion is an important welfare and health trait in turkey production. Current breeding values for locomotion are often based on subjective scoring. Sensor technologies could be applied to obtain objective evaluation of turkey gait. Inertial measurement units (IMUs) measure acceleration and rotational velocity, which makes them attractive devices for gait analysis. The aim of this study was to compare three different methods for step detection from IMU data from turkeys. This is an essential step for future feature extraction for the evaluation of turkey locomotion. Data from turkeys walking through a corridor with IMUs attached to each upper leg were annotated manually. We evaluated change point detection, local extrema approach, and gradient boosting machine in terms of step detection and precision of start and end point of the steps. All three methods were successful in step detection, but local extrema approach showed more false detections. In terms of precision of start and end point of steps, change point detection performed poorly due to significant irregular delay, while gradient boosting machine was most precise. For the allowed distance to the annotated steps of 0.2 s, the precision of gradient boosting machine was 0.81 and the recall was 0.84, which is much better in comparison to the other two methods (<0.61). At an allowed distance of 1 s, performance of the three models was similar. Gradient boosting machine was identified as the most accurate for signal segmentation with a final goal to extract information about turkey gait; however, it requires an annotated training dataset.

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“…The reliability of spatiotemporal gait parameters in single and k repeated measurements (k = 3) have been assessed using ICC(2, 1) and ICC(2, 3) for IMU-based sensor and camera systems [138]. The reliability of inertial senors has been quantified using ICC(2, 1) for each condition (varied incline and speed) using RehaGait and instrumented treadmill [139] [140].…”

Section: A Platforms For Validationmentioning

confidence: 99%

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

Jarchi

Pope

Lee

et al. 2018

IEEE Rev. Biomed. Eng.

165

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Gait analysis continues to be an important technique for many clinical applications to diagnose and monitor certain diseases. Many mental and physical abnormalities cause measurable differences in a person's gait. Gait analysis has applications in sport, computer games, physical rehabilitation, clinical assessment, surveillance, human recognition, modeling, and many other fields. There are established methods using various sensors for gait analysis, of which accelerometers are one of the most often employed. Accelerometer sensors are generally more user friendly and less invasive. In this paper, we review research regarding accelerometer sensors used for gait analysis with particular focus on clinical applications. We provide a brief introduction to accelerometer theory followed by other popular sensing technologies. Commonly used gait phases and parameters are enumerated. The details of selecting the papers for review are provided. We also review several gait analysis software. Then we provide an extensive report of accelerometry-based gait analysis systems and applications, with additional emphasis on trunk accelerometry. We conclude this review with future research directions.

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Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters

Cited by 119 publications

References 49 publications

Wearable sensors objectively measure gait parameters in Parkinson’s disease

Wearable sensors objectively measure gait parameters in Parkinson’s disease

Automated Step Detection in Inertial Measurement Unit Data From Turkeys

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

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