Relative Position Estimation using Kalman Filter Based on Inertial Sensor Signals Considering Soft Tissue Artifacts of Human Body Segments

Lee, Chang June; Lee, Jung Keun

doi:10.46670/jsst.2020.29.4.237

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…In order to ensure the integrity of data in wireless transmission and improve the efficiency of data transmission, the system defines the star topology structure with base station as the center and realizes the network protocol based on time division multiplexing. Because the components of each sensor node and base station are difficult to be strictly consistent, and there is clock deviation between different nodes, clock synchronization is required [8,9].…”

Section: Inertial Sensorsmentioning

confidence: 99%

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Zhang

2021

Mathematical Problems in Engineering

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Aerobics is one of the main contents of physical education, which has a positive role in promoting the health of young people. This paper mainly studies the parallel processing method of inertial aerobics multisensor data fusion. In this paper, an aerobics exercise system is designed, which uses digital filter to remove the noise generated in the process of exercise. In this paper, Kalman filter is used to filter the pulse error of accelerometer, and the data structure of unidirectional link is used to achieve the effect of sliding window, which can reduce the memory cost to the greatest extent. In this paper, the region of moving object is determined by horizontal and vertical projection of binary symmetric difference image. At the same time, the optimal feature combination is selected from the reduced features by feature subset selection, and the classification algorithm is used as the evaluation function in the optimization process. Finally, the collected data are tested, analyzed, and sorted out. The experimental data show that, after calibrating the sensor data, the static x-axis and y-axis data are about 0 g, and the z-axis data are about 1 g, which is closer to the real value. The results show that the attitude data collected by the inertial sensor can be stably transmitted to the software of the computer wirelessly for attitude reconstruction, and the recognition of each attitude and parameter has reached a high accuracy.

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Section: Inertial Sensorsmentioning

confidence: 99%

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Zhang

2021

Mathematical Problems in Engineering

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“…However, they are useful in that they quantify the variation in the joint center due to STAs. The authors of this paper, Lee and Lee [ 33 ], proposed a KF that estimates the S2J vectors for each segment as time-varying vectors to consider the variation in the joint center when estimating the relative position between two body segments. Although the method proposed in [ 33 ] compensated for the effects of STAs by applying a constraint of the joint center acceleration, uncertainty existed because the constraint is based on a mechanical spherical joint.…”

Section: Introductionmentioning

confidence: 99%

Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors

Lee

2022

Sensors

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In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences.

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Intelligent Detection and Analysis of Wearable Devices in Wushu Training

2022

Security and Communication Networks

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With the integration and development of artificial intelligence and medical technology, wearable intelligent technology has become an important health testing equipment in people’s lives, constantly testing physical function and health. However, according to the movement standards, body indicators, and constantly changing detection indexes of athletes in the exercise process, it can effectively guide athletes to use them correctly and efficiently, which has become an important task of wearable intelligent technology. In the design mode of wearable smart devices, wearable bracelets are designed with acceleration sensors, martial arts training data are measured, and machine learning technology is used to analyze and evaluate the data. When a user uses the method, a sensor is used for collecting the data, the data are transmitted to a processing platform through low-power Bluetooth, the data are analyzed through a program, the accuracy of each action is output, and finally, a standard measurement result of a section of the boxing method is combined. This paper collects and analyzes the data of body characteristics and movement characteristics of wearable intelligent devices in Wushu training. Sensor technology and filtering technology are used to collect and filter the collected information, and better analysis data are obtained. Finally, the filtered data of Wushu are analyzed, and then, the efficiency and performance of different algorithms in Wushu training are compared. Wearable intelligent equipment collects Wushu action training data and then uses fixed threshold classification to recognize Wushu action. The results show that the method used has high accuracy.

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Relative Position Estimation using Kalman Filter Based on Inertial Sensor Signals Considering Soft Tissue Artifacts of Human Body Segments

Cited by 5 publications

References 20 publications

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Parallel Processing Method of Inertial Aerobics Multisensor Data Fusion

Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors

Intelligent Detection and Analysis of Wearable Devices in Wushu Training

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