Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement

Favre, Julien; Jolles, Brigitte M.; Siegrist, O.; Aminian, Kamiar

doi:10.1049/el:20060124

Cited by 137 publications

(101 citation statements)

References 3 publications

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“…Therefore, research is continuing into improved methods for deriving orientation without the use of magnetometers. A recent paper [7] showed that it was possible to obtain high accuracy 3-axis orientation without the use of a magnetometer by using a two stage approach -integration of the angular velocity signals, followed by a correction to the angle estimation based on inclination data from accelerometers gathered during periods of rest, or near constant velocity motion. However, the interest of the biomechanics community generally lies in differential orientation measurements, derived from absolute angle measurements on two adjoining limb segments.…”

Section: Introductionmentioning

confidence: 99%

Inertial sensor-based knee flexion/extension angle estimation

Cooper

Sheret

McMillian

et al. 2009

Journal of Biomechanics

243

196

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A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data.Limitations of the method, its clinical application and its possible extension are discussed.

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Section: Introductionmentioning

confidence: 99%

Inertial sensor-based knee flexion/extension angle estimation

Cooper

Sheret

McMillian

et al. 2009

Journal of Biomechanics

243

196

View full text Add to dashboard Cite

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“…Gyroscope offset was removed based on a static measurement before each run [30]. After functional calibration, initial sensor orientation was estimated based on the strapdown and drift-correction algorithm of Reference [31]. In a second step drift was further reduced by applying the method of the authors of [32] extended to 3D.…”

Section: Inertial Sensorsmentioning

confidence: 99%

Three-Dimensional Body and Centre of Mass Kinematics in Alpine Ski Racing Using Differential GNSS and Inertial Sensors

et al. 2016

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A key point in human movement analysis is measuring the trajectory of a person's center of mass (CoM). For outdoor applications, differential Global Navigation Satellite Systems (GNSS) can be used for tracking persons since they allow measuring the trajectory and speed of the GNSS antenna with centimeter accuracy. However, the antenna cannot be placed exactly at the person's CoM, but rather on the head or upper back. Thus, a model is needed to relate the measured antenna trajectory to the CoM trajectory. In this paper we propose to estimate the person's posture based on measurements obtained from inertial sensors. From this estimated posture the CoM is computed relative to the antenna position and finally fused with the GNSS trajectory information to obtain the absolute CoM trajectory. In a biomechanical field experiment, the method has been applied to alpine ski racing and validated against a camera-based stereo photogrammetric system. CoM position accuracy and precision was found to be 0.08 m and 0.04 m, respectively. CoM speed accuracy and precision was 0.04 m/s and 0.14 m/s, respectively. The observed accuracy and precision might be sufficient for measuring performance-or equipment-related trajectory differences in alpine ski racing. Moreover, the CoM estimation was not based on a movement-specific model and could be used for other skiing disciplines or sports as well.

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“…To accomplish this, over short periods of time, relative orientation may be obtained through direct integration of the gyroscope measurements, and relative velocity/position may be obtained by integrating the accelerometer measurements. The accuracy of integration may be further improved with sensor fusion algorithms that use quaternion-based representation of orientation [13], [14].…”

Section: Using Multiple Sensors For Different Functionsmentioning

confidence: 99%

Networked bio-inspired modules for sensorimotor control of wearable cyber-physical devices

Park¹,

Young²,

Chen

et al. 2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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Abstract-We present a functioning prototype of a soft, modular, active cyber-physical assistive device comprised of a sealed network of conductive liquid sensors and collectives of miniature pneumatically-driven actuators that serve as artificial muscles. The system is multi-functional, supports large deformation, and operates with its own on-board pneumatics and controllers. When multiple artificial muscles are collectively actuated (contracted), the overall displacement and force produced is scaled to the size, form, and capabilities of the wearer. Each muscle is equipped with a soft strain sensor that detects the muscle contraction. Four muscles with strain sensors are controlled by one micro-controller as one module. The current prototype has four modules with 16 muscles in total. With different combinations of contracted muscles, various shapes may be demonstrated.Index Terms-cyber-physical system (CPS); assistive device; sensor network; pneumatic artificial muscle (PAM); soft strain sensor; inertial measurement unit (IMU).

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Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement

Cited by 137 publications

References 3 publications

Inertial sensor-based knee flexion/extension angle estimation

Inertial sensor-based knee flexion/extension angle estimation

Three-Dimensional Body and Centre of Mass Kinematics in Alpine Ski Racing Using Differential GNSS and Inertial Sensors

Networked bio-inspired modules for sensorimotor control of wearable cyber-physical devices

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