Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Localization and velocity tracking of human via 3 IMU sensors

Abstract: In sports training and exercises like walking and jogging, the velocity and position of the exercise people is very crucial for motion evaluation. A simple wearable system and corresponding method for velocity monitoring using minimal sensors can be very useful for daily use. In this work, a velocity tracking and localization method using only three IMU sensors is introduced. The three sensors are located at the right shank, right thigh and the pelvis to measure the kinematics of the lower limbs. In the method, a reference root point on the pelvis is chosen to represent the velocity and location of the person. Through acceleration fine tuning algorithm, the acceleration data is refined and combined with the velocity calculated from body kinematics to get a drift-free and accurate 3D velocity result. The location of the person is tracked based on this velocity estimation and the limb kinematic subsequently. The benchmark study with the commercial optical reference shows that the error in velocity tracking is within 0.1 m/s and localization accuracy is within 2% in both normal walking, jogging and jumping. Due to the conveniences of the small-size wearable IMU sensors, this proposed velocity tracking and localization method is very useful in everyday exercises both indoor and outdoor.

Biosleeve human-machine interface

Abstract: Systems and methods for sensing human muscle action and gestures in order to control machines or robotic devices are disclosed. One exemplary system employs a tight fitting sleeve worn on a user arm and including a plurality of electromyography (EMG) sensors and at least one inertial measurement unit (IMU). Power, signal processing, and communications electronics may be built into the sleeve and control data may be transmitted wirelessly to the controlled machine or robotic device.

Design and implementation of an inertial measurement unit for control of artificial limbs: Application on leg orthoses

Abstract: This paper presents the design and implementation of a cost-effective and small inertial measurement unit (IMU) for application on leg smart orthotics and prosthetics IMU design based on biomechanical considerations for lower leg devices is presented Methods for calculation of a number of biomechanical parameters related to gait based on the unit are discussed, including calibration and offset correction procedures An approach for electronic knee joint control of orthosis during cyclic walking based on IMU signals is discussed Finally, experiments are conducted for a subject walking on a flat surface wearing a mechanically driven orthosis with the proposed sensor Experimental results demonstrate the IMU suitability and feasibility for real-time embedded control of wearable assistive devices for walking restoration and monitoring

Multisensor data fusion for robust pose estimation of a six-legged walking robot

Abstract: For autonomous navigation tasks it is important that the robot always has a good estimate of its current pose with respect to its starting position and - in terms of orientation - with respect to the gravity vector. For this, the robot should make use of all available information and be robust against the failure of single sensors. In this paper a multisensor data fusion algorithm for the six-legged walking robot DLR Crawler is presented. The algorithm is based on an indirect feedback information filter that fuses measurements from an inertial measurement unit (IMU) with relative 3D leg odometry measurements and relative 3D visual odometry measurements from a stereo camera. Errors of the visual odometry are computed and considered in the filtering process in order to achieve accurate pose estimates which are robust against visual odometry failure. The algorithm was successfully tested and results are presented.

On-Device Mobile Visual Location Recognition by Integrating Vision and Inertial Sensors

Abstract: This paper deals with the problem of city scale on-device mobile visual location recognition by fusing the inertial sensors and computer vision techniques. The main contributions are as follows: Firstly, we design an efficient vector quantization strategy by combining the Transform Coding (TC) and Residual Vector Quantization (RVQ). Our method can compress a visual descriptor into only several bytes while providing reasonable searching accuracy, which makes the managing of city scale image database directly on mobile devices come true. Secondly, we integrate the information from inertial sensors into the Vector of Locally Aggregated Descriptors (VLAD) generation and image similarity evaluation processes. Our method is not only fast enough for on-device implementation, but it also can improve the location recognition accuracy obviously. Thirdly, we also release a set of 1.295 million geo-tagged street view images with the information from inertial sensors, as well as a difficult set of query images. These resources can be used as a new benchmark to facilitate further research in the area. Experimental results prove the validity of the proposed methods for on-device mobile visual location recognition applications.