The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.
In this study, an insole-type ground reaction force (GRF) measurement system using a load cell was manufactured and configured as a system that can measure joint angle and GRF, when walking in conjunction with a commercialized inertial sensor. The data acquisition device was used to acquire synchronized data, between the inertial measurement unit (IMU) sensor and the load cell insole. A three-dimensional motion analysis system comprising six infrared cameras and two ground reaction forces, was used to check the accuracy of the gait measurement system, comprising an inertial sensor and a load cell insole. The motion and force data were acquired while performing five times six-meter walking test by five young adult male subjects (Age: 26.0±1.8, Height: 171.4±6.8 cm, Weight: 62.2±10.8 kg). It was measured and as a result of comparing the calculated sagittal joint angle with the vertical GRF, the sagittal lower extremity joint angle correlation coefficient (Pearson’s r) was 0.40 to 0.94, and the vertical GRF to be 0.98 to 0.99. It is necessary to upgrade the joint angle calculation algorithm through future research. Additionally, the possibility of clinical application for actual stroke patients will be reviewed.
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.
Citations
Citations to this article as recorded by
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 ㎜.
Citations
Citations to this article as recorded by
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
Ergonomic guidelines for the design interfaces of additive modules for manual wheelchairs: sagittal plane Bartosz Wieczorek, Mateusz Kukla, Łukasz Warguła, Marcin Giedrowicz Scientific Reports.2023;[Epub] CrossRef
Effects of the Selection of Deformation-related Variables on Accuracy in Relative Position Estimation via Time-varying Segment-to-Joint Vectors Chang June Lee, Jung Keun Lee JOURNAL OF SENSOR SCIENCE AND TECHNOLOGY.2022; 31(3): 156. CrossRef
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.
Previous studies on joint angle estimation have been restricted to slow-speed level walking conditions, even though slope walking and running elicit unique biomechanical characteristics. Measurements were mostly based on an optical motion capture system despite in-the-lab limitation of measurement technique. The contribution of this study is twofold: (i) to propose a joint angle estimation method by applying a state-of-the-art parallel Kalman filter based on an inertial measurement unit (IMU) that can overcome in-the-lab limitation, and (ii) to demonstrate its application to level walking condition as well as slope walking and running conditions to fill a gap in joint kinematics literature. In particular, this study focuses on knee flexion/extension and ankle dorsiflexion/plantarflexion angles at various speed variations. The parallel Kalman filter applied in the proposed method can compensate external acceleration through Markov-chain-based acceleration modeling, that may enhance joint estimation performance in high speed walking conditions. To validate the proposed estimation method, an optical motion capture system was used as reference. In addition, patterns for each condition were investigated to identify and evaluate presence of classifying features.
Citations
Citations to this article as recorded by
Evaluating Sparse Inertial Measurement Unit Configurations for Inferring Treadmill Running Motion Mackenzie N. Pitts, Megan R. Ebers, Cristine E. Agresta, Katherine M. Steele Sensors.2025; 25(7): 2105. CrossRef
Consistency Is Key: A Secondary Analysis of Wearable Motion Sensor Accuracy Measuring Knee Angles Across Activities of Daily Living Before and After Knee Arthroplasty Robert C. Marchand, Kelly B. Taylor, Emily C. Kaczynski, Skye Richards, Jayson B. Hutchinson, Shayan Khodabakhsh, Ryan M. Chapman Sensors.2025; 25(13): 3942. CrossRef
달리기 속도에 따른 관성 측정 장비(IMU)의 신뢰도 분석 지혁 강, 재원 강, 석훈 윤 The Korean Journal of Physical Education.2024; 63(5): 493. CrossRef
Comparison of Concurrent and Asynchronous Running Kinematics and Kinetics From Marker-Based and Markerless Motion Capture Under Varying Clothing Conditions Robert M. Kanko, Jereme B. Outerleys, Elise K. Laende, W. Scott Selbie, Kevin J. Deluzio Journal of Applied Biomechanics.2024; 40(2): 129. CrossRef
Development of Gait Measurement System Combined with IMU and Loadcell Insole: A Pilot Study Jeong-Woo Seo, Junggil Kim, Seulgi Lee, Gyerae Tack, Jin-Seung Choi Journal of the Korean Society for Precision Engineering.2022; 39(9): 657. CrossRef
Validity and repeatability of a new inertial measurement unit system for gait analysis on kinematic parameters: Comparison with an optoelectronic system Elodie Piche, Marine Guilbot, Frédéric Chorin, Olivier Guerin, Raphaël Zory, Pauline Gerus Measurement.2022; 198: 111442. CrossRef
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
Recreating the Motion Trajectory of a System of Articulated Rigid Bodies on the Basis of Incomplete Measurement Information and Unsupervised Learning Bartłomiej Nalepa, Magdalena Pawlyta, Mateusz Janiak, Agnieszka Szczęsna, Aleksander Gwiazda, Konrad Wojciechowski Sensors.2022; 22(6): 2198. CrossRef
Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation Túlio Fernandes de Almeida, Edgard Morya, Abner Cardoso Rodrigues, André Felipe Oliveira de Azevedo Dantas Sensors.2021; 21(19): 6477. CrossRef
Drift Reduction in IMU-based Joint Angle Estimation for Dynamic Motion-Involved Sports Applications Jung Keun Lee, Chang June Lee Journal of the Korean Society for Precision Engineering.2020; 37(7): 539. CrossRef
Relative Position Estimation using Kalman Filter Based on Inertial Sensor Signals Considering Soft Tissue Artifacts of Human Body Segments Chang June Lee, Jung Keun Lee JOURNAL OF SENSOR SCIENCE AND TECHNOLOGY.2020; 29(4): 237. CrossRef
Kinematic Constraint-Projected Kalman Filter to Minimize Yaw Estimation Errors Induced by Magnetic Distortions Tae Hyeong Jeon, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(7): 659. CrossRef
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.
Citations
Citations to this article as recorded by
Indoor Localization of a Mobile Robot based on Unscented Kalman Filter Using Sonar Sensors Soo Hee Seo, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2021; 38(4): 245. CrossRef
Unscented Kalman Filter Based 3D Localization of Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2020; 37(5): 331. CrossRef
Estimation of Vertical Displacement based on Inertial Sensor Signals Combined with Joint Constraint Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(3): 233. CrossRef
Kinematic Constraint-Projected Kalman Filter to Minimize Yaw Estimation Errors Induced by Magnetic Distortions Tae Hyeong Jeon, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2019; 36(7): 659. CrossRef
Extended Kalman Filter Based 3D Localization Method for Outdoor Mobile Robots Woo Seok Lee, Min Ho Choi, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(9): 851. CrossRef
Unscented Kalman Filter based Outdoor Localization of a Mobile Robot Woo Seok Lee, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2019; 36(2): 183. CrossRef
Dynamic Accuracy Improvement of a MEMS AHRS for Small UAVs Min-Shik Roh, Beom-Soo Kang International Journal of Precision Engineering and Manufacturing.2018; 19(10): 1457. CrossRef
Study on Robust Lateral Controller for Differential GPS-Based Autonomous Vehicles Hyung-Gyu Park, Kyoung-Kwan Ahn, Myeong-Kwan Park, Seok-Hee Lee International Journal of Precision Engineering and Manufacturing.2018; 19(3): 367. CrossRef
GPS-Based Human Tracking Methods for Outdoor Robots Woo Seok Lee, In Ho Cho, Jong Hwan Lim Journal of the Korean Society for Precision Engineering.2018; 35(4): 413. CrossRef
First
Prev
