Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture system

Bidabadi, Shiva Sharif; Murray, Iain; Lee, Gabriel Yin Foo

doi:10.1007/s13534-018-0072-5

Cited by 30 publications

(21 citation statements)

References 25 publications

(29 reference statements)

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“…That study compared the accuracy of the IMU system with the Vicon motion capture system (with an 18 camera setup) [31]. A strong correlation was observed (more than 96.9%) between the IMU system and the Vicon motion capture system [34].…”

Section: Methodsmentioning

confidence: 99%

Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques

Bidabadi

Tan

Murray

et al. 2019

Sensors

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The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression.

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Section: Methodsmentioning

confidence: 99%

Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques

Bidabadi

Tan

Murray

et al. 2019

Sensors

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“…Related works of estimating foot angles using inertial sensing reported comparable range of FPAs, resulting in similar performance as our proposed approach (maximum mean difference of ~ 2.6°). Bidabadi et al used a single foot-worn IMU to estimate the foot pitch angle (ankle flexion/extension) and reported a mean accuracy of ~ 3.8° [ 24 ]. Huang et al presented a single foot-worn IMU (with magnetometer) approach for estimating the FPA with a maximum mean error of ~ 2.5° [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Foot progression angle estimation using a single foot-worn inertial sensor

Wouda

Jaspar

Harlaar

et al. 2021

J NeuroEngineering Rehabil

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Background The foot progression angle is an important measure used to help patients reduce their knee adduction moment. Current measurement systems are either lab-bounded or do not function in all environments (e.g., magnetically distorted). This work proposes a novel approach to estimate foot progression angle using a single foot-worn inertial sensor (accelerometer and gyroscope). Methods The approach uses a dynamic step frame that is recalculated for the stance phase of each step to calculate the foot trajectory relative to that frame, to minimize effects of drift and to eliminate the need for a magnetometer. The foot progression angle (FPA) is then calculated as the angle between walking direction and the dynamic step frame. This approach was validated by gait measurements with five subjects walking with three gait types (normal, toe-in and toe-out). Results The FPA was estimated with a maximum mean error of ~ 2.6° over all gait conditions. Additionally, the proposed inertial approach can significantly differentiate between the three different gait types. Conclusion The proposed approach can effectively estimate differences in FPA without requiring a heading reference (magnetometer). This work enables feedback applications on FPA for patients with gait disorders that function in any environment, i.e. outside of a gait lab or in magnetically distorted environments.

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“…The inertial sensors were located on the body of the study participant to record the accelerations according to the myoMotion protocol, described in the instruction manual ( Figure 1 ). The accuracy and validity of the inertial measurement units (IMU) system in angle determination is unquestioned and was the subject of previous research [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Statistical Parametric Mapping Reveals Subtle Gender Differences in Angular Movements in Table Tennis Topspin Backhand

Bańkosz

Winiarski

2020

IJERPH

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Background: Statistical parametric mapping (SPM) is an innovative method based on the analysis of time series (data series) and is equivalent to statistical methods for numerical (discrete) data series. This study aimed to analyze the patterns of movement in the topspin backhand stroke in table tennis and to use SPM to compare these patterns between advanced female and male players. Methods: The research involved seven advanced male and six advanced female players. The kinematic parameters were measured using an inertial motion analysis system. The SPM was computed using the SPM1D Python package. Results: Our study made it possible to reproduce the pattern of movement in the joints during topspin backhand strokes in the studied athletes. During multiple comparisons, the analysis of variance (ANOVA) SPM test revealed many areas in the studied parameter series with statistically significant differences (p ≤ 0.01). Conclusions: The study presents the movement patterns in the topspin backhand shot and describes the proximal-to-distal sequencing principle during this shot. The SPM study revealed differences between men and women in the contribution of thoracic rotation, external shoulder rotation, dorsal flexion, and supination in the wrist during the hitting phase. These differences may result from the anatomical gender differences or variations in other functionalities of individual body segments between the study groups. Another possible source for these discrepancies may reside in tactical requirements, especially the need for a more vigorous attack in men. The gender differences presented in this study can help in the individualization of the training process in table tennis.

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Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture system

Cited by 30 publications

References 25 publications

Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques

Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques

Foot progression angle estimation using a single foot-worn inertial sensor

Statistical Parametric Mapping Reveals Subtle Gender Differences in Angular Movements in Table Tennis Topspin Backhand

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