Localization and velocity tracking of human via 3 IMU sensors

Yuan, Qilong; Chen, I‐Ming

doi:10.1016/j.sna.2014.03.004

Cited by 67 publications

(47 citation statements)

References 12 publications

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“…In other hand, Inertial Measurement Unit (IMU) [25][26][27][28], which consisting of accelerometers, gyroscopes and magnetometers has been widely used for localization purposes, mainly for Indoor localization and motion capture, by calculating the velocity and location of the tacked person, through acceleration 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.…”

Section: Body Sensorsmentioning

confidence: 99%

Activity and Emotion Recognition for Elderly Health Monitoring

Benaissa

Köppen

Yoshida

2018

IJAE

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The problems facing elderly who are living independently, are considered to be one of the most important motivations of the activity recognition research. The advances in sensing technologies allow collecting several types of data and communicate it wirelessly. However, most existing activity recognition systems requires pre-calculated pattern recognition models. This paper lays the theoretical foundations of a real-time methodology for activity and emotional recognition based on body and environment sensors simultaneously, then tackles one aspect of the method which is the path estimation using chest-mounted IMU sensor, for which a zero velocity update criteria is proposed. Finally path estimation results for sitting down, laying down, falling down and standing up are discussed.

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Section: Body Sensorsmentioning

confidence: 99%

Activity and Emotion Recognition for Elderly Health Monitoring

Benaissa

Köppen

Yoshida

2018

IJAE

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“…Its corresponding rotation matrix is calculated by the SO (3) matrix e φ i Vî , details can be found in (Murray et al 1994;Yuan and Chen 2014).…”

Section: Modeling Of the Taping Processmentioning

confidence: 99%

Automatic Robot Taping: Strategy and Enhancement

Yuan

Lembono

Chen

2016

ROMANSY 21 - Robot Design, Dynamics and Control

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Taping, covering the surfaces of objects with masking tapes, is a common process before conducting surface treatments like plasma spraying and painting. Manual taping is tedious and takes a lot of effort of the workers. The taping process is a special process which requires correct tape orientation and proper allocation of the masking tape for the coverage. We have introduced an automatic robot taping system consisting of a robot manipulator, a rotating platform, a 3D scanner and a specially designed novel taping end-effectors. With proper taping path planning methods, the taping of cylindrical and freeform surfaces have been achieved. With improvements on new sets of taping tools and path planning method, taping of general surfaces can be conducted using such system in future. This paper talks about the overall system design and discusses on the improvements needed to make the system available for parts with more general geometries. With further improvements, such taping solution with the taping tool and the taping methodology can be combined as a very useful and practical taping package to replace the work of human in such tedious and time consuming works.

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“…Orientation estimation is essential for various kinds of applications, such as unmanned micro-aerial vehicles, robotics, human motion analysis and mobile devices [1][2][3][4]. For human motion capture and analysis, inertial sensors have incomparable superiority than other sensing technologies.…”

Section: Introductionmentioning

confidence: 99%

Human motion tracking based on complementary Kalman filter

Wang

Yang

Huang

et al. 2017

2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Abstract-Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors.

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Localization and velocity tracking of human via 3 IMU sensors

Cited by 67 publications

References 12 publications

Activity and Emotion Recognition for Elderly Health Monitoring

Activity and Emotion Recognition for Elderly Health Monitoring

Automatic Robot Taping: Strategy and Enhancement

Human motion tracking based on complementary Kalman filter

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