Recently, the estimation of joint kinetics such as joint force and moment using wearable inertial sensors has received great attention in biomechanics. Generally, the joint force and moment are calculated though inverse dynamics using segment kinematic data, ground reaction force, and moment. However, this approach has problems such as estimation error of kinematic data and soft tissue artifacts, which can lead to inaccuracy of joint forces and moments in inverse dynamics. This study aimed to apply a recurrent neural network (RNN) instead of inverse dynamics to joint force and moment estimation. The proposed RNN could receive signals from inertial sensors and force plate as input vector and output lower extremity joints forces and moments. As the proposed method does not depend on inverse dynamics, it is independent of the inaccuracy problem of the conventional method. Experimental results showed that the estimation performance of hip joint moment of the proposed RNN was improved by 66.4% compared to that of the inverse dynamics-based method.
Relative position estimation between body segments is one essential process for inertial sensor-based human motion analysis. Conventionally, the relative position was calculated through a constant segment to joint (S2J) vector and the orientation of the segment, assuming that the segment was rigid. However, the S2J vector is deformed by soft tissue artifact (STA) of the segment. In a previous study, in order to handle the above problem, Lee and Lee proposed the relative position estimation method using time-varying S2J vectors based on inertial sensor signals. Here, time-varying S2J vectors were determined through the joint flexion angle using regression. However, it was not appropriate to consider only the flexion angle as a deformation-related variable. In addition, regression has limitations in considering complex joint motion. This paper proposed artificial neural network models to compensate for the STA by considering all three-axis motion of the joint. A verification test was conducted for lower body segments. Experimental results showed that the proposed method was superior to the previous method. For pelvis-to-foot relative position estimation, averaged root mean squared error of the previous method was 17.38 mm, while that of the proposed method was 12.71 mm.
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Wearable Inertial Sensors-based Joint Kinetics Estimation of Lower Extremity Using a Recurrent Neural Network Ji Seok Choi, Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2023; 40(8): 655. CrossRef
Estimation of the relative position between the body segments is an important task in inertial sensor-based human motion tracking. Conventionally, the relative position is determined using orientations and constant segment vectors that connect from segment to joint center, based on the assumption that the segments are rigid. However, the human body segments are non-rigid, which leads to an inaccurate relative position estimation. This paper proposes a relative position estimation method based on inertial sensor signals, considering the non-rigidity of the human bodies. Considering that the effects of non-rigidity are highly correlated with a specific variable, the proposed method uses time-varying segment vectors determined by the specific physical variable, instead of using constant segment vectors. Verification test results for an upper-body model demonstrates the superiority of the proposed method over the conventional method: The averaged root mean square error of the sternum-to-forearm estimation from the conventional method was 34.19 ㎜, while the value from the proposed method was 16.67 ㎜.
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Wearable Inertial Sensors-based Joint Kinetics Estimation of Lower Extremity Using a Recurrent Neural Network Ji Seok Choi, Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2023; 40(8): 655. CrossRef
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Application of Artificial Neural Network for Compensation of Soft Tissue Artifacts in Inertial Sensor-Based Relative Position Estimation Ji Seok Choi, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2022; 39(3): 233. CrossRef
Displacement estimation based on inertial sensor signals is usually performed in aid of global positioning systems or barometers. However, due to low accuracy estimation capabilities of such aiding sensors, inertial sensor-based displacement estimation is difficult to achieve high accuracy. This paper will show that it is possible to determine the vertical displacement of a link connected by a joint with higher accuracy while only using the inertial sensor. The proposed method utilizes a predetermined position vector from the joint center to the sensor and link orientation. By combining the joint constraint, accuracy of the orientation estimation is ensured even in highly dynamic conditions, and thus, the vertical displacement estimation with high accuracy can be achieved. Experimental results show that the proposed method outperformed the method by fusing inertial sensor and barometer signals as well as the method using inertial sensor signals only without constraint combination.
This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.
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This paper introduces a new outdoor localization method for practical application to guide robots. This method uses only encoder data from the robot’s wheels and non-inertial sensors, such as GPS and a digital compass, to guarantee ease of use and economy in real world usage without cumulative error. Position and orientation information from DGPS (Differential Global Positioning System) and a digital compass are combined with encoder data from the robot’s wheels to more accurately estimate robot position using an extended Kalman filter. Conventional robot guidance methods use different types of fusion that rely on DGPS. We use a very simple and consistent method that ensures localization stability by using the validation gate to evaluate DGPS reliability and digital compass data that can be easily degraded by various noise sources. Experimental results of the localization are presented that show the feasibility and effectiveness of the methods using a real robot in real world conditions.
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