Measurement of Lower Limb Joint Kinematics using Inertial Sensors During Stair Ascent and Descent in Healthy Older Adults and Stroke Survivors

Laudanski, Annemarie; Brouwer, Brenda; Li, Qingguo

doi:10.1260/2040-2295.4.4.555

Cited by 33 publications

(28 citation statements)

References 40 publications

(58 reference statements)

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“…Indeed, the peak mean errors obtained in the sagittal plane for a step ascent of 3 • , 5 • , and 5 • for hip, knee, and ankle, respectively, correspond to errors in elevation angles of 5 • , 4 • , and 4 • previously reported for the same joints [30]. Similarly, the peak mean ∆ROM of 6.4 • obtained for the stair ascent and of 4.6 • for the stair descent are comparable to the errors previously reported for healthy subjects (peak error of 4.1 • for a stair ascent and 4.8 • for a stair descent) [31]. Therefore, before implementing inertial sensors in a complex, real-life context, the accuracy should be established in such a context rather than extrapolated from simpler gait movements recorded in controlled lab conditions.…”

Section: Accuracy Across Different Gait Movementssupporting

confidence: 87%

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

Lebleu

Gosseye

Detrembleur

et al. 2020

Sensors

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Inertial measurement unit (IMU) records of human movement can be converted into joint angles using a sensor-to-segment calibration, also called functional calibration. This study aims to compare the accuracy and reproducibility of four functional calibration procedures for the 3D tracking of the lower limb joint angles of young healthy individuals in gait. Three methods based on segment rotations and one on segment accelerations were used to compare IMU records with an optical system for their accuracy and reproducibility. The squat functional calibration movement, offering a low range of motion of the shank, provided the least accurate measurements. A comparable accuracy was obtained in other methods with a root mean square error below 3.6° and an absolute difference in amplitude below 3.4°. The reproducibility was excellent in the sagittal plane (intra-class correlation coefficient (ICC) > 0.91, standard error of measurement (SEM) < 1.1°), good to excellent in the transverse plane (ICC > 0.87, SEM < 1.1°), and good in the frontal plane (ICC > 0.63, SEM < 1.2°). The better accuracy for proximal joints in calibration movements using segment rotations was traded to distal joints in calibration movements using segment accelerations. These results encourage further applications of IMU systems in unconstrained rehabilitative contexts.

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Section: Accuracy Across Different Gait Movementssupporting

confidence: 87%

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

Lebleu

Gosseye

Detrembleur

et al. 2020

Sensors

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“…Several original IMU-based techniques to track lower limb joints motion have been proposed [10, 11, 20, 21], but only a few have been validated using stereophotogrammetry as the gold-standard [19, 22–28], as performed in the present study. As expected, the knee flexion angle was found to be the best to be estimated by the IMU among the three rotations [25]. …”

Section: Discussionsupporting

confidence: 55%

Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: comparison with state-of-the-art gait analysis

Leardini

Lullini

Giannini

et al. 2014

J NeuroEngineering Rehabil

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BackgroundSeveral rehabilitation systems based on inertial measurement units (IMU) are entering the market for the control of exercises and to measure performance progression, particularly for recovery after lower limb orthopaedic treatments. IMU are easy to wear also by the patient alone, but the extent to which IMU’s malpositioning in routine use can affect the accuracy of the measurements is not known. A new such system (Riablo™, CoRehab, Trento, Italy), using audio-visual biofeedback based on videogames, was assessed against state-of-the-art gait analysis as the gold standard.MethodsThe sensitivity of the system to errors in the IMU’s position and orientation was measured in 5 healthy subjects performing two hip joint motion exercises. Root mean square deviation was used to assess differences in the system’s kinematic output between the erroneous and correct IMU position and orientation.In order to estimate the system’s accuracy, thorax and knee joint motion of 17 healthy subjects were tracked during the execution of standard rehabilitation tasks and compared with the corresponding measurements obtained with an established gait protocol using stereophotogrammetry.ResultsA maximum mean error of 3.1 ± 1.8 deg and 1.9 ± 0.8 deg from the angle trajectory with correct IMU position was recorded respectively in the medio-lateral malposition and frontal-plane misalignment tests. Across the standard rehabilitation tasks, the mean distance between the IMU and gait analysis systems was on average smaller than 5°.ConclusionsThese findings showed that the tested IMU based system has the necessary accuracy to be safely utilized in rehabilitation programs after orthopaedic treatments of the lower limb.Electronic supplementary materialThe online version of this article (doi:10.1186/1743-0003-11-136) contains supplementary material, which is available to authorized users.

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“…Typically, accuracy has been quantified by comparing joint angles calculated using IMU systems to optical motion capture through root mean squared error (RMSE) [ 10 , 11 ], correlation coefficients [ 12 , 13 ], and/or Bland-Altman limits of agreement [ 14 , 15 ]. Efforts have largely been focused on IMU system validation for lower limb angles during gait [ 16 , 17 , 18 ]. However, other activities, including stair climbing, kicking, materials handling, and skiing [ 14 , 19 , 20 , 21 ] and upper body angles [ 22 , 23 ] have been examined as well.…”

Section: Introductionmentioning

confidence: 99%

Validation of an IMU Suit for Military-Based Tasks

Mavor

Ross

Clouthier

et al. 2020

Sensors

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Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements.

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Measurement of Lower Limb Joint Kinematics using Inertial Sensors During Stair Ascent and Descent in Healthy Older Adults and Stroke Survivors

Cited by 33 publications

References 40 publications

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations

Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: comparison with state-of-the-art gait analysis

Validation of an IMU Suit for Military-Based Tasks

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