A functional calibration protocol for ankle plantar-dorsiflexion estimate using magnetic and inertial measurement units: Repeatability and reliability assessment

Mascia, Guido; Brasiliano, Paolo; Feo, Paolo Di; Cereatti, Andrea; Camomilla, Valentina

doi:10.1016/j.jbiomech.2022.111202

Cited by 5 publications

(6 citation statements)

References 62 publications

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“…Such discrepancies are unlikely to affect the results of this study since differences between marker-based and MIMU-based events were in the range 14–21 ms. Second, the analysis of how an anticipated or delayed identification of FSs would influence the characterization of the heel and forefoot rocker must be interpreted with caution. In the current study, to set apart the errors entailed in estimating ankle kinematics with MIMUs [ 24 ] from those relative to gait event identification, ankle kinematics was estimated using an optoelectronic system. Therefore, the characterization of the heel and forefoot rocker may change when using MIMUs to estimate the ankle kinematics.…”

Section: Discussionmentioning

confidence: 99%

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Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

et al. 2023

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Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.

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Section: Discussionmentioning

confidence: 99%

“…(ii) The anatomical calibration procedure. The relevant accuracy and reliability, validated in healthy individuals [ 21 , 24 , 25 ], should be tested in this population, and the most accurate and reliable should be chosen;…”

Section: Introductionmentioning

confidence: 99%

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

et al. 2023

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“…All of them were extracted from

, including the raw estimate of the height,

. Nineteen jump-related variables (features from A to s) were inspired by Dowling and Vamos ( 5 ); three of them (

, and

), enlarging the description of power related variables were presented in Mascia and Camomilla ( 22 ); finally, the last three features are the central frequencies obtained processing

via VMD ( 45 ), subdividing the signal into three intrinsic mode functions, each having a frequency spectrum centered around each of them. In particular, the high- and mid-central frequencies (

and

) were assumed to be associated with the inertial effects due to wobbling masses, whereas the low-central frequency (

) was thought to be associated with the jump itself ( Figure 4 ).…”

Section: Methodsmentioning

confidence: 99%

“…Smartphone IMUs have already been used to characterize jump activity: to detect it among other activities ( 18 ), to find possible correlations between jump mat variables and kinematic features in both CMJ and SJ ( 19 ), and to analyze drop jumps ( 20 , 21 ). Estimating jump height using SP-embedded IMU direct measures has not been attempted, to the best of our knowledge, aside from preliminary investigations forerunner of the current one ( 22 , 23 ). SP-focused studies were all based on video camera-based approaches ( 24 , 25 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning aided jump height estimate democratization through smartphone measures

Mascia

Lazzari²,

Camomilla³

2023

Front. Sports Act. Living

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IntroductionThe peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors.MethodsFor this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps – 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps – 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error.ResultsThe multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features.DiscussionThe study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.

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“…Often, both approaches are combined, and one axis is derived from a static pose and one from a functional motion. Those hybrid approaches have been demonstrated for the upper body [27,28] and lower body [29][30][31]. For thorax and lumbar joint angles, however, a recent study by Cottam et al [32] found that calibration via functional motions did not improve accuracy in comparison to relying on manual sensor placement.…”

Section: Calibration From Arbitrary Motions (Model-based Alignment)mentioning

confidence: 99%

Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints

Laidig¹,

Weygers²,

Seel³

2022

Preprint

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Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, marker-based optical motion capture, but at much lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientation in which the sensor is attached to the body segments. This knowledge is commonly obtained via an anatomical calibration procedure based on precisely defined poses or motions, which is time-consuming and error-prone. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

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A functional calibration protocol for ankle plantar-dorsiflexion estimate using magnetic and inertial measurement units: Repeatability and reliability assessment

Cited by 5 publications

References 62 publications

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

Machine learning aided jump height estimate democratization through smartphone measures

Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints

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