Topic
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.
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TL;DR: A mobile human airbag system designed for fall protection for the elderly and an embedded digital signal processing (DSP) system is developed for real-time fall detection.
Abstract: This paper introduces a mobile human airbag system designed for fall protection for the elderly. A Micro Inertial Measurement Unit ( muIMU) of 56 mm times 23 mm times 15 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU). It records human motion information, and, through the analysis of falls using a high-speed camera, a lateral fall can be determined by gyro threshold. A human motion database that includes falls and other normal motions (walking, running, etc.) is set up. Using a support vector machine (SVM) training process, we can classify falls and other normal motions successfully with a SVM filter. Based on the SVM filter, an embedded digital signal processing (DSP) system is developed for real-time fall detection. In addition, a smart mechanical airbag deployment system is finalized. The response time for the mechanical trigger is 0.133 s, which allows enough time for compressed air to be released before a person falls to the ground. The integrated system is tested and the feasibility of the airbag system for real-time fall protection is demonstrated.
144 citations
TL;DR: In this paper, an efficient initial calibration and alignment algorithm for a 6-degrees of freedom inertial measurement unit (IMU) to be used in land vehicle applications is presented.
Abstract: This work presents an efficient initial calibration and alignment algorithm for a 6-degrees of freedom inertial measurement unit (IMU) to be used in land vehicle applications. Error models for the gyros and accelerometers are presented with a study of their perturbation in trajectory prediction. A full inertial error model is also presented to determine the sensors needed for full observability of the different perturbation parameters. Finally, dead-reckoning experimental results are presented based on the initial alignment and calibration parameters obtained with the algorithms presented. The results show that the proposed algorithms provide accurate position and velocity information for an extended period of time using nonaided IMU. ©1999 John Wiley & Sons, Inc.
143 citations
Book•
31 Aug 2010
TL;DR: In this paper, the authors focus on the application of MEMS inertial sensors to navigation systems and show how to minimize cost by adding and removing inertial sensor nodes, and provide integration strategies with examples from real field tests.
Abstract: Due to their micro-scale size and low power consumption, Microelectromechanical systems (MEMS) are now being utilized in a variety of fields This leading-edge resource focuses on the application of MEMS inertial sensors to navigation systems The book shows you how to minimize cost by adding and removing inertial sensors Moreover, this practical reference provides you with various integration strategies with examples from real field tests From an introduction to MEMS navigation related applications to special topics on Alignment for MEMS-Based Navigation to discussions on the Extended Kalman Filter, this comprehensive book covers a wide range of critical topics in this fast-growing area
143 citations
TL;DR: A method to measure stride-to-stride foot placement in unconstrained environments is developed, and whether it can accurately quantify gait parameters over long walking distances is tested.
143 citations
TL;DR: Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving, indicating the algorithm is applicable for both terrestrial applications and space robotics.
Abstract: This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, inertial measurement unit, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.
143 citations