Inertial measurement unit-based motion capture to replace camera-based systems for assessing gait in healthy young adults: Proceed with caution

Rekant, Julie; Rothenberger, Scott D.; Chambers, April J.

doi:10.1016/j.measen.2022.100396

Cited by 12 publications

(10 citation statements)

References 34 publications

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“…Outliers and xation errors are possible reasons for the lower measurement validity of the IMU over the AMAS, which may be due to magnetic effects. Biases exist in measurement systems, such as IMU, with an improved agreement with marker-based optical motion capture in the sagittal plane but larger errors in non-sagittal planes [40], and the differences vary among participants [41]. The position, calibration method, and measurement algorithm of common wearable sensors affect accuracy [42].…”

Section: Discussionmentioning

confidence: 99%

A novel evaluation system for arthrokinematic ankle motion using a rotary encoder

Ito

Kodama

Yamaguchi³

2023

Preprint

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Ankle joint control is considered important for walking. The relationship between the kinematic indices and neurophysiological indices that reflect neuromuscular control of the lower extremity by upper motor centres must be evaluated to capture the essence of ankle joint control and construct rehabilitation. Herein, we developed the articulation motion assessments system (AMAS) to measure both these indices synchronously. The aim of this study was to validate the validity and reliability of the AMAS’ ankle kinematics analysis. This study included 14 heathy adults. AMAS, a reflective marker and inertial sensor was attached to their right foot, and they performed different ankle joint tasks, such as dorsiflexion, plantarflexion, inversion, and eversion with subjective maximum and mild angular movements. Each task was performed randomly under two conditions; comfortable and slow speeds. Two-dimensional motion analysis and the AMAS had moderate-to-high correlation. The mean root mean square error (RMSE) was significantly lower than those of inertial sensor. The repeatability of the AMAS was better than 0.76 for intraclass correlation coefficients (3,1) and (3,k). However, the Bland-Altman analysis showed fixed errors. Errors should be considered when comparing measurement methods. Synchronised kinematic and neurophysiological measurements should elucidate ankle joint control mechanisms and improve rehabilitation.

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Section: Discussionmentioning

confidence: 99%

A novel evaluation system for arthrokinematic ankle motion using a rotary encoder

Ito

Kodama

Yamaguchi³

2023

Preprint

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“…The mixed data e.g., quantitative, nominal, time-series, sequences of qualitative data, imprecisely observed data, and textual data were analyzed using the Robust Fuzzy C-Medoids Clustering. In the case of marker-based systems, the optokinetic motion capture is often applied, which is treated as the gold standard for biomechanical and sport analysis Drazan et al (2021); Rekant et al (2022). This type of movement is captured as 3D positions of retroreflective markers using a series of infrared cameras.…”

Section: Survey Datamentioning

confidence: 99%

“…However, they are very accurate and that is why they are often used for biomechanical analysis. The most important results of these systems are both to create a digital model of the skeleton and to calculate joint angles Rekant et al (2022).…”

Section: Survey Datamentioning

confidence: 99%

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Fuzzy C-Means Clustering for Tennis Multivariate Time-Series

Skublewska‐Paszkowska

Karczmarek

Powroźnik

et al. 2023

Preprint

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Creating the proper player profile in training is crucial for athlete development. Although there is a great number of studies concerning this subject, there is no solution that would allow to model it in a convenient way. Applying fuzzy modelling clustering can be useful in this field. Moreover, the application of sophisticated acquisition techniques, like motion capture systems, allow ones to obtain accurate data corresponding to athlete’s movement in the form of a multivariate time series. In this study, the authors undertook the task of clustering the most important at the stage of training tennis strokes such as: Forehand, backhand, and volley. They were represented as trajectories of the tennis racket based on four retro-reflective markers attached to it. The Fuzzy C-Means algorithm, which utilizes the dynamic time warping-based distance to cluster analysis of tennis strokes, has been applied with success to group various kinds of movement of tennis players. The comprehensive analysis included numerous separate tennis moves and their groups. Various analyses depending on their number have been thoroughly carried out. The obtained results allowed creation of the reference stroke model,which can be used for further examination of the tennis players’performance.

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“…IMU systems utilize miniaturized wireless sensors strapped to body segments to determine joint angles and spatiotemporal characteristics of gait through laptop software [ 11 , 12 , 13 ]. IMU-based motion capture systems have been shown to be valid for gait assessment in normal weight populations when specific computational adjustments are implemented [ 14 , 15 , 16 ]. However, IMU-based motion capture has not been compared to OMC in adults with obesity, a group of adults particularly vulnerable to functional decline.…”

Section: Introductionmentioning

confidence: 99%

Obesity-Specific Considerations for Assessing Gait with Inertial Measurement Unit-Based vs. Optokinetic Motion Capture

Rekant,

Rothenberger,

Chambers

2024

Sensors

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Adults with obesity experience high rates of disability and rapid functional decline. Identifying movement dysfunction early can direct intervention and disrupt disability development; however, subtle changes in movement are difficult to detect with the naked eye. This study evaluated how a portable, inertial measurement unit (IMU)-based motion capture system compares to a laboratory-based optokinetic motion capture (OMC) system for evaluating gait kinematics in adults with obesity. Ten adults with obesity performed overground walking while equipped with the OMC and IMU systems. Fifteen gait cycles for each participant were extracted for the 150 total cycles analyzed. Kinematics were compared between OMC and IMU across the gait cycles (coefficient of multiple correlations), at clinically significant time points (interclass correlations), and over clinically relevant ranges (Bland–Altman plots). Sagittal plane kinematics were most similar between systems, especially at the knee. Sagittal plane joint angles at clinically meaningful timepoints were poorly associated except for ankle dorsiflexion at heel strike (ρ = 0.38) and minimum angle (ρ = 0.83). All motions except for ankle dorsiflexion and hip abduction had >5° difference between systems across the range of angles measured. While IMU-based motion capture shows promise for detecting subtle gait changes in adults with obesity, more work is needed before this method can replace traditional OMC. Future work should explore standardization procedures to improve consistency of IMU motion capture performance.

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Inertial measurement unit-based motion capture to replace camera-based systems for assessing gait in healthy young adults: Proceed with caution

Cited by 12 publications

References 34 publications

A novel evaluation system for arthrokinematic ankle motion using a rotary encoder

A novel evaluation system for arthrokinematic ankle motion using a rotary encoder

Fuzzy C-Means Clustering for Tennis Multivariate Time-Series

Obesity-Specific Considerations for Assessing Gait with Inertial Measurement Unit-Based vs. Optokinetic Motion Capture

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