Movement Speed Estimation Based on Foot Acceleration Patterns

Grad, Stefan; Zrenner, Markus; Schuldhaus, Dominik; Wirth, Markus; Cegielny, Tomek; Zwick, Constantin; Eskofier, Bjoern M.

doi:10.1109/embc.2018.8513042

Cited by 4 publications

(10 citation statements)

References 10 publications

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“…The Acceleration method introduced in [6] uses only acceleration data for step segmentation and the computation of stride length and stride velocity. The method correlates the velocity of the foot (and thus, the subject) with the acceleration during the swing phase of the foot.…”

Section: Methodsmentioning

confidence: 99%

“…We used the step segmentation algorithm from the cited publication due its applicability to movements other than running [6]. The inputs to the processing pipeline were the sampled triaxial acceleration signals from the foot sensor ax, ay, and az.…”

Section: Methodsmentioning

confidence: 99%

“…Individual strides were determined in the smoothed dorsoventral acceleration signal sy using a peak detection process in combination with two knowledge-based parameter thresholds. The goal was to detect and isolate the high impact response of initial ground contact in the smoothed signal [6]. Each time a valid stride was detected by the stride segmentation algorithm, the average velocity per stride vstride was determined based on a second degree polynomial regression function:vstride=A+B·ι[nIC]+C·ι[nIC]2, where the constants A,B, and C are derived during a regression model training phase where known reference velocity observations are matched to velocity integration values using parametric regression analysis.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Gradl et al [6] proposed an algorithm that uses quadratic regression to compute the velocity of movements. The velocity was evaluated during running, as well as other movements and showed a relative error of 6.9±5.5%.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units

Zrenner

Gradl

Jensen

et al. 2018

Sensors

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Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units

Zrenner

Gradl

Jensen

et al. 2018

Sensors

Self Cite

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“…Shiang et al [ 11 ], Falbriard et al [ 12 ], and Strohrmann et al [ 2 ] mounted the IMU sensors on the instep of the foot on top of the shoelaces, whereas Lederer et al [ 13 ] and Koska et al [ 14 ] mounted the sensors on the heel. Other sensor positions presented in literature are on the lateral side of the running shoe below the ankle [ 17 ] and inside the sole of the running shoe [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Does the Position of Foot-Mounted IMU Sensors Influence the Accuracy of Spatio-Temporal Parameters in Endurance Running?

Zrenner

Küderle

Roth

et al. 2020

Sensors

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Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners’ performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists due to the possibility to monitor biomechanically relevant spatio-temporal parameters outside the lab in real-world environments. Researchers developed and investigated algorithms to extract various features using IMU data of different sensor positions on the foot. In this work, we evaluate whether the sensor position of IMUs mounted to running shoes has an impact on the accuracy of different spatio-temporal parameters. We compare both the raw data of the IMUs at different sensor positions as well as the accuracy of six endurance running-related parameters. We contribute a study with 29 subjects wearing running shoes equipped with four IMUs on both the left and the right shoes and a motion capture system as ground truth. The results show that the IMUs measure different raw data depending on their position on the foot and that the accuracy of the spatio-temporal parameters depends on the sensor position. We recommend to integrate IMU sensors in a cavity in the sole of a running shoe under the foot’s arch, because the raw data of this sensor position is best suitable for the reconstruction of the foot trajectory during a stride.

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Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

Zhang

Guo

Zanotto

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Movement Speed Estimation Based on Foot Acceleration Patterns

Cited by 4 publications

References 10 publications

Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units

Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units

Does the Position of Foot-Mounted IMU Sensors Influence the Accuracy of Spatio-Temporal Parameters in Endurance Running?

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

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