25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches

Picerno, Pietro

doi:10.1016/j.gaitpost.2016.11.008

Cited by 173 publications

(138 citation statements)

References 41 publications

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“…With regards to the treadmill condition, as discussed earlier, caution should be taken when comparing the results with sets of data using different conditions (i.e., overground) or even with different treadmill models. Finally, there is an emerging field of research on wearable sensors to monitor daily life activities including gait that must be acknowledged (Picerno, 2017). Whilst the validity and reliability of this technology are not comparable to the data, particularly for non-sagittal movement, obtained in biomechanics laboratories using motion capture systems and force plates, the use of these sensors enhance the ecological validity of the findings since they allow the individuals to run freely in their natural environment and training conditions.…”

Section: Discussionmentioning

confidence: 99%

A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics

Fukuchi

Duarte

2017

PeerJ

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Background. The goals of this study were (1) to present the set of data evaluating running biomechanics (kinematics and kinetics), including data on running habits, demographics, and levels of muscle strength and flexibility made available at Figshare (DOI: 10.6084/m9.figshare.4543435); and (2) to examine the effect of running speed on selected gait-biomechanics variables related to both running injuries and running economy. Methods. The lower-extremity kinematics and kinetics data of 28 regular runners were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes. Results. A dataset comprising raw and processed kinematics and kinetics signals pertaining to this experiment is available in various file formats. In addition, a file of metadata, including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. Overall, there was an effect of running speed on most of the gait-biomechanics variables selected for this study. However, the foot-strike patterns were not affected by running speed. Discussion. Several applications of this dataset can be anticipated, including testing new methods of data reduction and variable selection; for educational purposes; and answering specific research questions. This last application was exemplified in the study's second objective.

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Section: Discussionmentioning

confidence: 99%

A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics

Fukuchi

Duarte

2017

PeerJ

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“…The recordings in this study were limited in time due to the short time required to execute the investigated movements. The drift resulting from longer records [32] could be limited by using the IMU magnetometer or algorithms that constantly fuse the segment's angular velocity and linear acceleration via known kinematic relations between segments [33].…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

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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“…11 Lower extremity kinematics can be derived from multiple accelerometers, gyroscopes and goniometers, but doing so in daily real-world settings for remote monitoring is not yet feasible. 12 …”

Section: Wearable Sensing Tools: Opportunities and Limitationsmentioning

confidence: 99%

The Evolution of Personalized Behavioral Intervention Technology

Dobkin

Dorsch

2017

Stroke

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25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches

Cited by 173 publications

References 41 publications

A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics

A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics

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

The Evolution of Personalized Behavioral Intervention Technology

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