Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Karatsidis, Angelos; Bellusci, Giovanni; Schepers, H. Martin; Zee, Mark de; Andersen, Michael Skipper; Veltink, Petrus H.

doi:10.3390/s17010075

Cited by 187 publications

(205 citation statements)

References 40 publications

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“…We are investigating whether the 3D GRF could be well formulated using 3D CoM kinematics as a form of simple mechanics, as was the case for 2D. Despite the lack of quantitative formulation, there is considerable experimental evidence to show that full usage of the IMU data could predict the unmeasured GRFs, metabolic cost, and joint angles [9,33,34]. Three dimensional acceleration and angular rate measured from the 17 IMUs attached to the whole body have been used to successfully predict 3D GRF data [9].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Lim

Kim

Park

2019

Sensors

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Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

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Section: Discussionmentioning

confidence: 99%

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Lim

Kim

Park

2019

Sensors

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“…Conversely, Yanai et al [56] employed the reverse approach by embedding force plates directly into the baseball pitching mound. Alternatively, two main types of wearable sensor technologies have also been used to estimate GRF/M, first, in-shoe pressure sensors [6,31], and more recently, body mounted inertial sensors [27,41,54]. Unfortunately, the accuracy of these methods is restricted to simple gait motion (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the accuracy of these methods is restricted to simple gait motion (e.g. walking), whereby they estimate only a single force component (primarily vertical F z ), or the sensor itself (location or added mass) adversely affects performance [6,27,31,41,54]. The current generation of wearable sensors are limited by low-fidelity, low resolution, or uni-dimensional data analysis (e.g.…”

Section: Introductionmentioning

confidence: 99%

On-field player workload exposure and knee injury risk monitoring via deep learning

Johnson

Mian

Lloyd

et al. 2019

Journal of Biomechanics

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In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload exposure and knee injury risk. Traditionally, this analysis has relied on captive laboratory force plates and associated downstream biomechanical modeling, and many researchers have approached the problem of portability by extrapolating models built on linear statistics. An alternative approach would be to capitalize on recent advances in deep learning. In this study, using the pre-trained CaffeNet convolutional neural network (CNN) model, multivariate regression of marker-based motion capture to 3D KJM for three sports-related movement types were compared. The strongest overall mean correlation to source modeling of 0.8895 was achieved over the initial 33 % of stance phase for sidestepping. The accuracy of these mean predictions of the three critical KJM associated with anterior cruciate ligament (ACL) injury demonstrate the feasibility of on-field knee injury assessment using deep learning in lieu of laboratory embedded force plates. This multidisciplinary research approach significantly advances machine representation of real-world physical models with practical application for both community and professional level athletes.

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“…Applying the same model to patients may not work due to altered movement patterns. Other studies considered similar problems, such as estimating the daily cumulative joint loading (Robbins et al, 2009) and ground reaction forces (Guo et al, 2017;Karatsidis et al, 2017;Wouda et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU

Brabandere

Emmerzaal

Timmermans

et al. 2020

Front. Bioeng. Biotechnol.

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Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.

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Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Cited by 187 publications

References 40 publications

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

On-field player workload exposure and knee injury risk monitoring via deep learning

A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU

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