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IMU-based Suit for Strength Exercises: Design, Calibration and Tracking

  • Wearable systems have been applied in various studies as a convenient and efficient solution for monitoring health and fitness. There is a large number of commercial products in the growing market of wearable systems that can be worn as wristbands, clasps, or in the form of clothing. However, these systems only provide general information about the intensity and possibly the type of user activity, which is not sufficient for monitoring strength and conditioning exercises. To achieve optimal muscular development and reduce the risk of exercise-related injury, a wearable system should provide reliable biomechanical details of body movements as well as real-time feedback during training. In addition, it should be an affordable, comfortable, and easy-to-use platform for different types of users with different levels of movement intensity and work autonomously over long periods of time. These requirements impose many challenges on the design of such systems. This study presents most of these challenges and proposes solutions. In this work, a low-cost and light-weight tracking suit is designed and developed, which integrates multiple Inertial measurement units (IMUs). A novel data acquisition approach is proposed to improve the energy efficiency of the system without the use of additional devices. Given a valid calibration, IMUs, comprising inertial sensors and magnetometers, can provide accurate orientation in three dimensions (3D). Unlike the inertial sensors, magnetometer measurements are easily disturbed by ferromagnetic materials in the vicinity of the sensor, either inside the IMU casing or in the final mounting position. Therefore, this work proposes a practical method for in-field magnetometer calibration and alignment to the coordinate system of an IMU. This method is verified experimentally in terms of magnitude deviation, heading error, plane projections, and repeatability. The results show a higher accuracy compared to the related works. Sensor to body calibration is a critical requirement for capturing accurate body movements. Therefore, a theoretical analysis of an existing method is carried out, showing its limited applicability for hip and knee joints. On this basis, by applying geometric constraints, a method is proposed for estimating the positions of three IMUs (mounted on the pelvis, upper leg, and lower leg) simultaneously. The result of experiments with different types of movements and arbitrary intensity shows that the proposed method outperforms the previous method. Moreover, two real-time tracking algorithms based on the extended Kalman filter (EKF) are proposed for lower body motion estimation. The first approach provides an estimate of the pelvis orientation. The second approach estimates the position of IMUs and the joint angles with respect to the pelvis by incorporating the result of body-IMU calibration. The modeling of the biomechanical constraint compensates for lack of a reliable horizontal reference, e.g. Earth’s magnetic field. Experiments to track strength exercises such as squat and hip abduction/adduction show promising results. In order to finally provide a monitoring application in which users can follow the exercises according to the instructions and taking into account their health status, this work proposes an approach for the identification of exercises based on an online template matching algorithm, which detects the correct performance using a previously recorded exercise in the presence of a supervisor. Therefore, unlike most identification algorithms, no large datasets are required for training. The algorithm is optimized here to reduce execution time while maintaining the accuracy. Experiments show that for the specific application of this study, i.e. squat exercise monitoring, the proposed method outperforms the related works in optimization of online template matching.

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Author:Sarvenaz Salehi Mourkani
Advisor:Didier Stricker
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2021/07/15
Date of Publication:2021/07/15
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2021/04/16
Date of the Publication (Server):2021/07/15
Tag:body-IMU calibration; human body motion tracking; inertial sensors; magnetometer calibration; sensor fusion; wearable systems
Number of page:IX, 163
Faculties / Organisational entities:Fachbereich Informatik
CCS-Classification (computer science):A. General Literature
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Creative Commons 4.0 - Namensnennung, nicht kommerziell, keine Bearbeitung (CC BY-NC-ND 4.0)