Gait Posture Estimation by Wearable Acceleration and Gyro Sensor

Takeda, Ryo; Tadano, Shigeru; Natorigawa, Akiko; Todoh, Masahiro; Yoshinari, Satoshi

doi:10.1007/978-3-642-03889-1_30

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Furthermore, as some perturbations of the body segment orientation may affect joint angle accuracy [39], the effects of each calibration process on the knee joint angle accuracy were tested during a pedaling motion. Concerning the flexion/extension angle, the present results reported RMS errors in a range between 3.74 ± 2.99° (cycling method) and 4.79 ± 3.03° (static method), which was in agreement with most studies in the literature that reported errors within a range of 5–10° during gait [56,57]. Present results were demonstrated to be lower than those previously reported in cycling, with a mean error of 6.41° [41].…”

Section: Discussionsupporting

confidence: 92%

Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Cordillet

Bideau

et al. 2019

Sensors

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This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

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Section: Discussionsupporting

confidence: 92%

Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Cordillet

Bideau

et al. 2019

Sensors

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“…Gait analysis data collected with H-Gait system were evaluated in all the three planes. In particular trajectory of the center joints were considered in the sagittal, frontal, and transverse planes [10], [18], [19]. Similarly to what was presented in [16], the joint center trajectory in the transverse plane was calculated for both knee and ankle joint, bilaterally.…”

Section: Transverse Plane Measurementsmentioning

confidence: 99%

Gait measurements in the transverse plane using a wearable system: An experimental study of test-retest reliability

Rosso

Gastaldi

Agostini

et al. 2017

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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3D gait analysis comprises the study of kinematics in the sagittal, coronal, and transverse planes. The transverse plane measurements are usually less used and generally show the lowest reliability. Nevertheless, the knee and ankle joint center trajectories, in the transverse plane, provide new parameters that may be important in clinical gait analysis. The aim of this study is to analyze the test-retest variability of these parameters. Gait measurements were performed using H-Gait, a wearable system based on magnetic and inertial sensors. A normal weight and an overweight subject were recruited and were asked to walk at their preferred speed for 6 trials. For both of them, the angle between the right and left knee and ankle joint center trajectories were analyzed. Overall, results showed a standard deviation across trials always lower than 2°. This small standard deviation was found also in the overweight subject, for whom it is usually challenging to obtain reliable gait measurements. In addition, a greater knee angle between the right and left joint center trajectories was found in the overweight subject compared to the normal weight. The promising results of this study suggest that the new parameters introduced might be suitable to assess gait of subjects with different anthropometric characteristics.

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“…Takeda et al calculated translational acceleration using angular velocity signals. Translational acceleration was then removed from acceleration data, producing gravitational acceleration that represents body segment inclination [21]. Liu et al computed orientation by comparing data of two accelerometers attached to the same segment.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Ambulatory Assessment of 3D Knee Angle Using Novel Inertial Sensor-Based Technique

et al. 2021

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Three-dimensional (3D) knee angle measurement is one of the key measures in human gait analysis. Inertial sensor capable of measuring joint motion under unconstrained conditions is a practical tool for clinical evaluation and rehabilitation. An inertial measurement unit (IMU) consisting of accelerometer and gyroscope allows orientation measurement in 3D with an additional sensor (i.e., magnetometer). However, ferromagnetic interference negatively affects the performance of magnetometer and thus reduces measurement accuracy. In this study, a technique based on nonlinear autoregressive neural network with exogenous inputs (NARX) is presented to measure 3D segmental orientation during gait without the use of magnetometer. With IMUs attached to the thigh and shank, 3D knee angles in long-distance treadmill walking were computed and validated against an optical motion analysis system as the gold standard. Pseudo-integrator (PI) was also compared to the reference system for benchmarking. The learning capability of NARX was further assessed with the comparison of complementary filter (CF) to the reference system. The proposed NARX model was shown to outperform PI with biases between-3.5°and-0.2°, and root mean square errors between 4.5°and 2.5°. Results demonstrated the capability of NARX in providing accurate estimates of 3D knee joint angle while avoiding interference as encountered in systems incorporating magnetometer, suggesting that NARX is feasible to computing long-term ambulatory measurements of body segment orientation and 3D joint angles. INDEX TERMS 3D knee angle, gait analysis, NARX network, pseudo-integration, segmental orientation.

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Gait Posture Estimation by Wearable Acceleration and Gyro Sensor

Cited by 10 publications

References 5 publications

Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Gait measurements in the transverse plane using a wearable system: An experimental study of test-retest reliability

Biomechanical Ambulatory Assessment of 3D Knee Angle Using Novel Inertial Sensor-Based Technique

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