Inertial and magnetic posture tracking for inserting humans into networked virtual environments

Bachmann, E.R.; McGhee, Robert B.; Yun, Xiaoping; Zyda, Michael

doi:10.1145/505008.505011

Cited by 121 publications

(58 citation statements)

References 5 publications

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“…Bachmann et al used accelerometers and magnetometers in a quaternion-based complementary filter to compensate the drift in orientation obtained from angular velocity [8,5]. The system combined a triaxial accelerometer, a triaxial gyroscope and a triaxial magnetometer assembled to produce a sensor module referred to as Magnetic, Angular Rate and Gravity sensor (MARG).…”

Section: Inertial Sensors Algorithmsmentioning

confidence: 99%

Joint Angle Tracking with Inertial Sensors

El‐Gohary¹

2000

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The need to characterize normal and pathological human movement has consistently driven researchers to develop new tracking devices and to improve movement analysis systems. Movement has traditionally been captured by either optical, magnetic, mechanical, structured light, or acoustic systems. All of these systems have inherent limitations. Optical systems are costly, require fixed cameras in a controlled environment, and suffer from problems of occlusion. Similarly, acoustic and structured light systems suffer from the occlusion problem. Magnetic and radio frequency systems suffer from electromagnetic disturbances, noise and multipath problems. Mechanical systems have physical constraints that limit the natural body movement.Recently, the availability of low-cost wearable inertial sensors containing accelerometers, gyroscopes, and magnetometers has provided an alternative means to overcome the limitations of other motion capture systems. Inertial sensors can be used to track human movement in and outside of a laboratory, cannot be occluded, and are low cost. To calculate changes in orientation, researchers often integrate the angular velocity. However, a relatively small error or drift in the measured angular velocity leads to large integration errors. This restricts the time of accurate measurement and tracking to a few seconds. To compensate that drift, complementary data from accelerometers and magnetometers are normally integrated in tracking systems that utilize the Kalman filter (KF) or the extended Kalman filter (EKF) to fuse the nonlinear inertial data. Orientation estimates are only accurate for brief moments when the body is not moving and acceleration is only due to gravity. Moreover, sucii cess of using magnetometers to compensate drift about the vertical axis is limited by magnetic field disturbance.We combine kinematic models designed for control of robotic arms with state space methods to estimate angles of the human shoulder and elbow using two wireless wearable inertial measurement units. The same method can be used to track movement of other joints using a minimal sensor configuration with one sensor on each segment. Each limb is modeled as one kinematic chain. Velocity and acceleration are recursively tracked and propagated from one limb segment to another using Newton-Euler equations implemented in state space form. To mitigate the effect of sensor drift on the tracking accuracy, our system incorporates natural physical constraints on the range of motion for each joint, models gyroscope and accelerometer random drift, and uses zero-velocity updates. The combined effect of imposing physical constraints on state estimates and modeling the sensor random drift results in superior joint angles estimates. The tracker utilizes the unscented Kalman filter (UKF) which is an improvement to the EKF. This removes the need for linearization of the system equations which introduces tracking errors.We validate the performance of the inertial tracking system over long durations of slow, normal, and ...

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Section: Inertial Sensors Algorithmsmentioning

confidence: 99%

Joint Angle Tracking with Inertial Sensors

El‐Gohary¹

2000

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“…Body tracking suits were proposed in [42] and [43], with multiple sensors positioned at various points across the body. The former work tested a single instance of their iNEMO sensors, comprised of an accelerometer, gyroscope and magnetometer, against commercial inertial sensors like those by Xsens [44].…”

Section: Related Workmentioning

confidence: 99%

Wearable Sensor Networks for Motion Capture

Arsenault¹,

Whitehead²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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Abstract-This work presents the development of a full body sensor-based motion tracking system that functions through wearable inertial sensors. The system is comprised of a total of ten wearable sensors and maps the player's motions to an onscreen character in real-time. A hierarchical skeletal model was implemented that allows players to navigate and interact with the virtual world without the need of a hand-held controller. To demonstrate the capabilities of the system, a simple virtual reality game was created. As a wearable system, the ability for the users to engage in activities while not being tied to a camera system, or being forced indoors presents a significant opportunity for mobile entertainment, augmented reality and interactive systems that use the body as a significant form of input. This paper outlines the key developments necessary to implement such a system.

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“…Complementary filter design: Complementary filter exploit the complementary spectral characteristics of noisy signals to compute orientation [10]. While complementary filter can be operated at low computational costs, it has been shown that they are capable to track orientation of human body limbs with a sufficient accuracy [4].…”

Section: A Algorithms For Computation Of Orientationmentioning

confidence: 99%

ETHOS: Miniature orientation sensor for wearable human motion analysis

et al. 2010

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-Inertial and magnetic sensors offers a sourceless and mobile option to obtain body posture and motion for personal sports or healthcare assistants, if sensors could be unobtrusively integrated in casual garments and accessories. We present in this paper design, implementation, and evaluation results for a novel miniature attitude and heading reference system (AHRS) named ETHOS using current off-the-shelf technologies.ETHOS has a unit size of 2.5cm 3 , which is substantially below most currently marketed attitude heading reference systems, while the unit contains processing resources to estimate its orientation online. Results on power consumption in relation to sampling frequency and sensor use are presented. Moreover two sensor fusion algorithms to estimate orientation: a quaternionbased Kalman-, and a complementary filter. Evaluations of orientation estimation accuracy in static and dynamic conditions revealed that complementary filtering reached sufficient accuracy while consuming 46% of a Kalman's power. The system runtime of ETHOS was found to be 10 hours at a complementary filter update rate of 128Hz. Furthermore, we found that a ETHOS prototype functioned with a sufficient accuracy in estimating human movement in real-life conditions using an arm rehabilitation robot.

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Inertial and magnetic posture tracking for inserting humans into networked virtual environments

Abstract: ABSTRACT

Cited by 121 publications

References 5 publications

Joint Angle Tracking with Inertial Sensors

Joint Angle Tracking with Inertial Sensors

Wearable Sensor Networks for Motion Capture

ETHOS: Miniature orientation sensor for wearable human motion analysis

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