Motion-based remote control device for interaction with multimedia content

This dissertation describes the development and implementation of techniques to enhance the accuracy of low-complexity lters, making them suitable for remote control devices in consumer electronics. The evolution veri ed in the last years, on multimedia contents, available for consumers in Smart TVs and set-top-boxes, is not raising the expected interest from users, and one of the pointed reasons for this nding is the user interface. Although most current pointing devices rely on relative rotation increments, absolute orientation allows for a more intuitive use and interaction. This possibility is explored in this work as well as the interaction with multimedia contents through gestures. Classical accurate fusion algorithms are computationally intensive, therefore their implementation in low-energy consumption devices is a challenging task. To tackle this problem, a performance study was carried, comparing a relevant set of professional commercial of-the-shelf units, with the developed low-complexity lters in state-of-the-art Magnetic, Angular Rate, Gravity (MARG) sensors. Part of the performance evaluation tests are carried out under harsh conditions to observe the algorithms response in a nontrivial environment. The results demonstrate that the implementation of low-complexity lters using low-cost sensors, can provide an acceptable accuracy in comparison with the more complex units/ lters. These results pave the way for faster adoption of absolute orientation-based pointing devices in interactive multimedia applications, which includes hand-held, battery-operated devices

Robust foot clearance estimation based on the integration of foot-mounted IMU acceleration data

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject's foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions

A methodology for the performance evaluation of inertial measurement units

This paper presents a methodology for a reliable comparison among Inertial Measurement Units or attitude estimation devices in a Vicon environment. The misalignment among the reference systems and the lack of synchronization among the devices are the main problems for the correct performance evaluation using Vicon as reference measurement system. We propose a genetic algorithm coupled with Dynamic Time Warping (DTW) to solve these issues. To validate the efficacy of the methodology, a performance comparison is implemented between the WB-3 ultra-miniaturized Inertial Measurement Unit (IMU), developed by our group, with the commercial IMU InertiaCube3™ by InterSense

Augmenting Inertial Motion Capture with SLAM Using EKF and SRUKF Data Fusion Algorithms

Inertial motion capture systems widely use low-cost IMUs to obtain the orientation of human body segments, but these sensors alone are unable to estimate link positions. Therefore, this research used a SLAM method in conjunction with inertial data fusion to estimate link positions. SLAM is a method that tracks a target in a reconstructed map of the environment using a camera. This paper proposes quaternion-based extended and square-root unscented Kalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filters use measurements based on SLAM position data, multi-link biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the fusion algorithm is capable of estimating link geometries, allowing the imposing of biomechanical constraints without a priori knowledge of sensor positions. An optical tracking system is used as a reference of ground-truth to experimentally evaluate the performance of the proposed algorithm in various scenarios of human arm movements. The proposed algorithms achieve up to 5.87 (cm) and 1.1 (deg) accuracy in position and attitude estimation. Compared to the EKF, the SRUKF algorithm presents a smoother and higher convergence rate but is 2.4 times more computationally demanding. After convergence, the SRUKF is up to 17% less and 36% more accurate than the EKF in position and attitude estimation, respectively. Using an absolute position measurement method instead of SLAM produced 80% and 40%, in the case of EKF, and 60% and 6%, in the case of SRUKF, less error in position and attitude estimation, respectively.Comment: 8 pages, 8 figures, 4 tables, 21 reference

Gait analysis in a box: A system based on magnetometer-free IMUs or clusters of optical markers with automatic event detection

Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it is necessary to overcome certain limitations, such as the need for magnetically controlled environments, which affect IMU systems, or the need for additional instrumentation to detect gait events, which affects IMUs and optical systems. We present a MoCap gait analysis system called Move Human Sensors (MH), which incorporates proposals to overcome both limitations and can be configured via magnetometer-free IMUs (MH-IMU) or clusters of optical markers (MH-OPT). Using a test–retest reliability experiment with thirty-three healthy subjects (20 men and 13 women, 21.7 ± 2.9 years), we determined the reproducibility of both configurations. The assessment confirmed that the proposals performed adequately and allowed us to establish usage considerations. This study aims to enhance gait analysis in daily clinical practice

Wearable inertial sensor system towards daily human kinematic gait analysis: benchmarking analysis to MVN BIOMECH

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941