scholarly journals Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells

2021 ◽  
Vol 11 (23) ◽  
pp. 11521
Author(s):  
Yaojung Shiao ◽  
Thang Hoang ◽  
Po-Yao Chang
Keyword(s):  
Angular Velocity ◽  
Inertial Measurement Unit ◽  
Weighting Function ◽  
Measurement Unit ◽  
Identification Algorithm ◽  
Accurate Identification ◽  
Inertial Measurement ◽  
Exercise Mode ◽  
Mode Identification ◽  
Axial Acceleration

Exercise is good for health, quality of life, and maintenance of human muscles. Dumbbells are popular indoor exercise equipment with several benefits such as low cost, high flexibility in space and time, easy operation, and suitability for people of all ages. Facilitated by advances in the Internet of Things, smart dumbbells that provide automatic counting and motion monitoring functions have been developed. To perform these tasks, the key process is identification of exercise mode. This study proposes a method to identify essential muscle groups’ (biceps, triceps, and deltoids) exercise modes of a dumbbell using an inertial measurement unit to provide three-axis angular velocities and accelerations. The motion angles were estimated from the axial acceleration and angular velocity. Phase diagrams and time plots of the axial angle, angular velocity, and acceleration were used to extract significant features of each exercise. Machine Learning and weighting functions were developed to combine these features into an identification index value for accurate identification and classification of the exercise modes. An algorithm was developed to verify the exercise mode identification. The results show that the proposed method and weighting function can successfully identify the six exercise modes. The identification algorithm was 99.5% accurate. The exercise mode identification of the dumbbell is confirmed.

scholarly journals Angular velocity fusion of the microelectromechanical system inertial measurement unit array based on extended Kalman filter with correlated system noises

2020 ◽  
pp. 002029402091770
Author(s):  
Li Xing ◽  
Xiaowei Tu ◽  
Weixing Qian ◽  
Yang Jin ◽  
Pei Qi
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Extended Kalman Filter ◽  
Inertial Measurement Unit ◽  
Angular Rate ◽  
The State ◽  
Measurement Unit ◽  
Fusion Method ◽  
Microelectromechanical System ◽  
Inertial Measurement

The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.

scholarly journals Trunk Inclination Estimate During the Sprint Start Using an Inertial Measurement Unit: A Validation Study

2013 ◽  
Vol 29 (5) ◽  
pp. 622-627 ◽  
Author(s):  
Elena Bergamini ◽  
Pélagie Guillon ◽  
Valentina Camomilla ◽  
Hélène Pillet ◽  
Wafa Skalli ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Inertial Measurement Unit ◽  
Sagittal Plane ◽  
Measurement Unit ◽  
Mean Values ◽  
Inertial Measurement ◽  
Trunk Inclination ◽  
Time Histories ◽  
Mean Square Difference ◽  
Curve Similarity

The proper execution of the sprint start is crucial in determining the performance during a sprint race. In this respect, when moving from the crouch to the upright position, trunk kinematics is a key element. The purpose of this study was to validate the use of a trunk-mounted inertial measurement unit (IMU) in estimating the trunk inclination and angular velocity in the sagittal plane during the sprint start. In-laboratory sprint starts were performed by five sprinters. The local acceleration and angular velocity components provided by the IMU were processed using an adaptive Kalman filter. The accuracy of the IMU inclination estimate and its consistency with trunk inclination were assessed using reference stereophotogrammetric measurements. A Bland-Altman analysis, carried out using parameters (minimum, maximum, and mean values) extracted from the time histories of the estimated variables, and curve similarity analysis (correlation coefficient > 0.99, root mean square difference < 7 deg) indicated the agreement between reference and IMU estimates, opening a promising scenario for an accurate in-field use of IMUs for sprint start performance assessment.

scholarly journals Use of ant colony optimization and the Kalman filter to deduce thigh dip angle via acceleration and angular velocity sensing

2018 ◽  
Vol 51 (9-10) ◽  
pp. 488-497
Author(s):  
Zheng Fang ◽  
Tao Yu ◽  
Qian Wang ◽  
Chao Wang ◽  
Siyuan Chen
Keyword(s):  
Kalman Filter ◽  
Angular Velocity ◽  
Conventional Method ◽  
Inertial Measurement Unit ◽  
Optimal Parameter ◽  
Ground Truth ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Parameter Combination ◽  
Dip Angle

Background: Acceleration and angular velocity sensors are commonly used in the measurement of gait parameters. A representative application calculates the limb segment dip angle. The rotation angle is typically deduced by a conventional Kalman filter, which includes the use of two empirically derived parameters. We improved this conventional method by introducing colony algorithm to find the optimal parameter combination instead of empirically assignment. Method: To achieve optimal results, a servo motor with an inertial measurement unit was used to simulate human limb segment motion according to programmed rotation angles that was employed as the ground truth. To minimize the bias between the ground truth and the calculated result, the ant colony algorithm was employed to obtain the optimal Kalman filter parameter combination in two-dimensional space. Results: By the motor experiment, the sum of the angle squared error was only 1.9305 rad2, much better than the 6.7723 rad2 error by the conventional method. The optimal parameter combination obtained was then used in a human experiment involving a basketball player. The frames from video of a whole gait cycle period were all showed with a corresponding deduced thigh dip angle curve diagram. Conclusion: The colony algorithm for parameters optimization results in less angle errors deduced by Kalman filter using the data from inertial measurement unit. The subject experiment verified the feasibility and performance of this method.

scholarly journals Quaternion-Based Local Frame Alignment between an Inertial Measurement Unit and a Motion Capture System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4003 ◽  
Author(s):  
Jung Keun Lee ◽  
Woo Chang Jung
Keyword(s):  
Angular Velocity ◽  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Least Squares Method ◽  
Measurement Unit ◽  
Motion Capture System ◽  
Inertial Measurement ◽  
Local Frame ◽  
Velocity Transformation ◽  
Frame Alignment

Local frame alignment between an inertial measurement unit (IMU) system and an optical motion capture system (MCS) is necessary to combine the two systems for motion analysis and to validate the accuracy of IMU-based motion data by using references obtained through the MCS. In this study, we propose a new quaternion-based local frame alignment method where equations of angular velocity transformation are used to determine the frame alignment orientation in the form of quaternion. The performance of the proposed method was compared with those of three other methods by using data with different angular velocities, noises, and alignment orientations. Furthermore, the effects of the following three factors on the estimation performance were investigated for the first time: (i) transformation concept, i.e., angular velocity transformation vs. angle transformation; (ii) orientation representations, i.e., quaternion vs. direction cosine matrix (DCM); and (iii) applied solvers, i.e., nonlinear least squares method vs. least squares method through pseudoinverse. Within our limited test data, we obtained the following results: (i) the methods using angular velocity transformation were better than the method using angle transformation; (ii) the quaternion is more suitable than the DCM; and (iii) the applied solvers were not critical in general. The proposed method performed the best among the four methods. We surmise that the fewer number of components and constraints of the quaternion in the proposed method compared to the number of components and constraints of the DCM-based methods may result in better accuracy. Owing to the high accuracy and easy setup, the proposed method can be effectively used for local frame alignment between an IMU and a motion capture system.

The Application of Inertial Measurement Unit in Inertia Parameter Identification

2011 ◽  
Vol 201-203 ◽  
pp. 974-981 ◽  
Author(s):  
Bo Wang ◽  
Zhong Xi Hou ◽  
Xian Zhong Gao ◽  
Shang Qiu Shan
Keyword(s):  
Angular Velocity ◽  
Parameter Identification ◽  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Oscillation Period ◽  
Experimental Manipulation ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Periodic Movement ◽  
Inertia Parameter

This paper presents a simple but effective method for inertial parameter identification with symmetrical trifilar pendulum and inertial measurement unit (IMU). An improvement upon conventional pendulum method is described by introducing IMU to identify the orientation of specimen by self-alignment, and then complicated equipment and experimental manipulation are not needed any more. Based on the excellent capacity of motion tracking, the IMU is also used for recording the characteristic of periodic movement by collected the information about acceleration and angular velocity. The main sources of identification errors are discussed from a set of examples. Then an experiment is carried out, and Fourier analysis is used to gain the oscillation period, which made the measurement much more convenient and accurate. The identification results are also presented by comparing with reference values computed from geometrical considerations, which proves the effectiveness of such method.

scholarly journals IMU Action Recognition Based on Machine Learning

2020 ◽  
Vol 3 (6) ◽  
Author(s):  
Yongzhe Zhang ◽  
Jiachen Zheng
Keyword(s):  
Angular Velocity ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
The Public ◽  
6 Degrees Of Freedom ◽  
Three Dimensional Space

This paper adopts IMU motion recognition technology based on mechanical learning. IMU, inertial measurement unit, is a device that uses accelerometer and gyroscope to measure the three-axis attitude Angle (or angular velocity) and acceleration of an object. In a narrow sense, an IMU is equipped with gyroscope and accelerometer on three orthogonal axes, with a total of 6 degrees of freedom, to measure the angular velocity and acceleration of an object in three-dimensional space, which is known as "6-axis IMU". Broadly speaking, the IMU can add magnetometer to accelerometer and gyroscope to form the "9-axis IMU" which is now known to the public.

scholarly journals Development of a Wireless Inertial Measurement System for Pitching Motion Analysis

2011 ◽  
Vol 2-3 ◽  
pp. 452-457 ◽  
Author(s):  
Seiji Kitamura ◽  
Koichi Sagawa ◽  
Toshiaki Tsukamoto ◽  
Yasuyuki Ishibashi
Keyword(s):  
Angular Velocity ◽  
Measurement System ◽  
Flash Memory ◽  
Inertial Measurement Unit ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Measurement Procedure ◽  
Joint Torque ◽  
Measurement Unit ◽  
Inertial Measurement

This paper presents a wireless inertial measurement system to analyze three- dimensional (3D) pitching movement of baseball. Developed wireless inertial measurement unit (WIMU) sizes 43.7×45.2×25.7 [mm], weighs 48 [g] including Lithium-Ion battery, and consists of two types of 3D accelerometers, two types of 3D gyroscopes, release sensor, a microcontroller (MCU), flash memory, and an RF module. Synchronization of start and completion of measurement procedure for plural WIMUs are wirelessly controlled by a host computer. Three-dimensional pitching form of upper limb and trunk is produced by the numerical integration of the acceleration and angular velocity. The experimental results show that 3D posture, trajectory and joint torque in overhand and sidearm throwing are successfully estimated using the proposed system.

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